Add Estimator Python API and Inception Example

pyzoo/test/zoo/pipeline/estimator/__init__.py

@@ -0,0 +1,15 @@
+#
+# Copyright 2018 Analytics Zoo Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#

pyzoo/test/zoo/pipeline/estimator/test_estimator.py

@@ -0,0 +1,84 @@
+#
+# Copyright 2018 Analytics Zoo Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import pytest
+from pyspark.ml import Pipeline
+from zoo.pipeline.estimator import *
+
+from bigdl.nn.layer import *
+from bigdl.nn.criterion import *
+from bigdl.optim.optimizer import *
+from test.zoo.pipeline.utils.test_utils import ZooTestCase
+from zoo.feature.common import *
+from zoo import init_nncontext, init_spark_conf
+
+
+class TestEstimator(ZooTestCase):
+
+    def setup_method(self, method):
+        """ setup any state tied to the execution of the given method in a
+        class.  setup_method is invoked for every test method of a class.
+        """
+        sparkConf = init_spark_conf().setMaster("local[1]").setAppName("testEstimator")
+        self.sc = init_nncontext(sparkConf)
+        self.sqlContext = SQLContext(self.sc)
+        assert(self.sc.appName == "testEstimator")
+
+    def teardown_method(self, method):
+        """ teardown any state that was previously setup with a setup_method
+        call.
+        """
+        self.sc.stop()
+
+    def test_estimator_train_imagefeature(self):
+        batch_size = 8
+        epoch_num = 5
+        images = []
+        labels = []
+        for i in range(0, 8):
+            features = np.random.uniform(0, 1, (200, 200, 3))
+            label = np.array([2])
+            images.append(features)
+            labels.append(label)
+
+        image_frame = DistributedImageFrame(self.sc.parallelize(images),
+                                            self.sc.parallelize(labels))
+
+        transformer = Pipeline([BytesToMat(), Resize(256, 256), CenterCrop(224, 224),
+                                ChannelNormalize(0.485, 0.456, 0.406, 0.229, 0.224, 0.225),
+                                MatToTensor(), ImageFrameToSample(target_keys=['label'])])
+        data_set = FeatureSet.image_frame(image_frame).transform(transformer)
+
+        model = Sequential()
+        model.add(SpatialConvolution(3, 1, 5, 5))
+        model.add(View([1 * 220 * 220]))
+        model.add(Linear(1 * 220 * 220, 20))
+        model.add(LogSoftMax())
+        optim_method = SGD(learningrate=0.01)
+
+        estimator = Estimator(model, optim_method, "")
+        estimator.set_constant_gradient_clipping(0.1, 1.2)
+        estimator.train_imagefeature(train_set=data_set, criterion=ClassNLLCriterion(),
+                                        end_trigger=MaxEpoch(epoch_num),
+                                        checkpoint_trigger=EveryEpoch(),
+                                        validation_set=data_set,
+                                        validation_method=[Top1Accuracy()], batch_size=batch_size)
+        predict_result = model.predict_image(image_frame.transform(transformer))
+        assert(predict_result.get_predict().count(), 8)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])

pyzoo/zoo/__init__.py

@@ -29,6 +29,7 @@
 JavaCreator.add_creator_class("com.intel.analytics.zoo.feature.python.PythonFeatureSet")
 JavaCreator.add_creator_class("com.intel.analytics.zoo.pipeline.api.net.python.PythonZooNet")
 JavaCreator.add_creator_class("com.intel.analytics.zoo.pipeline.inference.PythonInferenceModel")
+JavaCreator.add_creator_class("com.intel.analytics.zoo.pipeline.estimator.python.PythonEstimator")
 for clz in creator_classes:
     JavaCreator.add_creator_class(clz)
 

pyzoo/zoo/examples/inception/README.md

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+# Inception Model on Imagenet
+This example demonstrates how to use Analytics-zoo to train [Inception v1](https://arxiv
+.org/abs/1409.4842) architecture on the [ImageNet](http://image-net.org/index) data.
+## Get the JAR
+You can build one by refer to the
+[Build Page](https://analytics-zoo.github.io/master/#ScalaUserGuide/install/#download-analytics-zoo-source) from the source code. We
+will release a pre-build package soon.
+
+## Prepare the data
+You can download imagenet-2012 data from <http://image-net.org/download-images>.
+
+After you download the files(**ILSVRC2012_img_train.tar** and **ILSVRC2012_img_val.tar**), 
+run the following commands to prepare the data.
+
+```bash
+mkdir train
+mv ILSVRC2012_img_train.tar train/
+cd train
+tar -xvf ILSVRC2012_img_train.tar
+rm -f ILSVRC2012_img_train.tar
+find . -name "*.tar" | while read CLASS_NAME ; do mkdir -p "${CLASS_NAME%.tar}"; tar -xvf "${CLASS_NAME}" -C "${CLASS_NAME%.tar}"; done
+rm *.tar
+cd ../
+mkdir val
+mv ILSVRC2012_img_val.tar val/
+cd val
+tar -xvf ILSVRC2012_img_val.tar
+cat classes.lst | while read CLASS_NAME; do mkdir -p ${CLASS_NAME}; done
+cat img_class.lst | while read PARAM; do mv ${PARAM/ n[0-9]*/} ${PARAM/ILSVRC*JPEG /}; done
+rm ILSVRC2012_img_val.tar
+```
+
+Now all the images belonging to the same category are moved to the same folder.
+
+This command will transform the images into hadoop sequence files, which are 
+more suitable for a distributed training.
+
+```bash
+spark-submit --class com.intel.analytics.bigdl.models.utils.ImageNetSeqFileGenerator bigdl-VERSION-jar-with-dependencies.jar -f imagenet_folder -o output_folder -p cores_number
+```
+
+It will generate the hadoop sequence files in the output folder.
+
+
+
+
+## Train the Model
+* Spark standalone, example command
+```bash
+export SPARK_HOME=the root directory of Spark
+export ANALYTICS_ZOO_HOME=the dist directory under the Analytics Zoo project
+
+${ANALYTICS_ZOO_HOME}/bin/spark-submit-with-zoo.sh \
+--master spark://xxx.xxx.xxx.xxx:xxxx \  
+--executor-cores 32 \  
+--num-executors 16 \  
+--executor-memory 150G \ 
+--driver-memory 20G \ 
+--conf spark.network.timeout=10000000  pyzoo/zoo/examples/inception/inception.py \ 
+--batchSize 1024 \
+--learningRate 0.065 \
+--weightDecay 0.0002 \
+--checkpointIteration 1000 \ 
+-f hdfs://... \
+--checkpoint /models/inception \ 
+--maxIteration 90000
+```
+
+* Spark yarn client mode, example command
+```
+export SPARK_HOME=the root directory of Spark
+export ANALYTICS_ZOO_HOME=the dist directory under the Analytics Zoo project
+
+${ANALYTICS_ZOO_HOME}/bin/spark-submit-with-zoo.sh \
+--master yarn \
+--deploy-mode client \
+--executor-cores 32 \
+--num-executors 16 \ 
+--executor-memory 150G \ 
+--driver-memory 20G \ 
+--conf spark.network.timeout=10000000  pyzoo/zoo/examples/inception/inception.py \ 
+--batchSize 1024 \
+--learningRate 0.065 \
+--weightDecay 0.0002 \
+--checkpointIteration 1000 \ 
+-f hdfs://... \
+--checkpoint /models/incepton \ 
+--maxIteration 90000
+```
+
+In the above commands
+* -f: where you put your ImageNet data, it should be a hdfs folder
+* --checkpoint: Where you cache the model/train_state snapshot. You should input a folder and
+make sure the folder is created when you run this example. The model snapshot will be named as
+model.#iteration_number, and train state will be named as optimMethod.#iteration_number. Note that if
+there are some files already exist in the folder, the old file will not be overwrite for the
+safety of your model files.
+* --batchSize: The mini-batch size. It is expected that the mini-batch size is a multiple of node_number *
+core_number. In this example, node_number is 1 and the mini-batch size is suggested to be set to core_number * 4
+* --learningRate: inital learning rate. Note in this example, we use a Poly learning rate decay
+policy.
+* --weightDecay: weight decay.
+* --checkpointIteration: the checkpoint interval in iteration.
+* --maxLr: optional. Max learning rate after warm up. It has to be set together with warmupEpoch.
+* --warmupEpoch: optional. Epoch numbers need to take to increase learning rate from learningRate to maxLR.
+* --gradientL2NormThreshold: optional. Gradient L2-Norm threshold used for norm2 gradient clipping.
+* --gradientMin: optional. Max gradient clipping by value, used in constant gradient clipping.
+* --gradientMax: optional. Min gradient clipping by value, used in constant gradient clipping.
+* --maxIteration: max iteration

pyzoo/zoo/examples/inception/__init__.py

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+#
+# Copyright 2018 Analytics Zoo Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#