fix scala style check and disable header check (intel-analytics#4715)

Le-Zheng · Sep 13, 2021 · 31b12ed · 31b12ed
1 parent cdc0a22
commit 31b12ed
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diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/autograd/math.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/autograd/math.scala
@@ -26,7 +26,7 @@ import com.intel.analytics.bigdl.dllib.{nn => bnn}
 import com.intel.analytics.bigdl.dllib.keras.layers._
 import com.intel.analytics.bigdl.dllib.keras.layers.internal._
 import com.intel.analytics.bigdl.dllib.keras.models._
-//import com.intel.analytics.bigdl.dllib.keras.layers.TimeDistributed
+// import com.intel.analytics.bigdl.dllib.keras.layers.TimeDistributed
 
 import scala.reflect.ClassTag
 

diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/common/MKLBlas.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/common/MKLBlas.scala
@@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.dllib.common
 import com.intel.analytics.bigdl.mkl.{MKL => BMKL}
 import com.intel.analytics.bigdl.dllib.tensor.{DoubleType, FloatType, Tensor}
 import com.intel.analytics.bigdl.dllib.tensor.TensorNumericMath.TensorNumeric
-//import com.intel.analytics.zoo.mkl.MKL.{vdErf, vsErf}
+// import com.intel.analytics.zoo.mkl.MKL.{vdErf, vsErf}
 import org.apache.log4j.Logger
 
 import scala.reflect.ClassTag

diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/common/PythonZoo.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/common/PythonZoo.scala
@@ -32,7 +32,7 @@ import com.intel.analytics.bigdl.dllib.nn.abstractnn.{AbstractModule, Activity}
 import com.intel.analytics.bigdl.dllib.optim.{LocalPredictor, ValidationMethod, _}
 import com.intel.analytics.bigdl.dllib.feature.image.ImageSet
 import com.intel.analytics.bigdl.dllib.feature.text.TextSet
-//import com.intel.analytics.zoo.pipeline.api.net.TFNet
+// import com.intel.analytics.zoo.pipeline.api.net.TFNet
 
 import scala.collection.JavaConverters._
 import scala.reflect.ClassTag

diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/FeatureSet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/FeatureSet.scala
@@ -24,15 +24,15 @@ import com.intel.analytics.bigdl.DataSet
 import com.intel.analytics.bigdl.dllib.feature.dataset.{AbstractDataSet, DistributedDataSet, MiniBatch, Transformer}
 import com.intel.analytics.bigdl.dllib.tensor.Tensor
 import com.intel.analytics.bigdl.dllib.utils.RandomGenerator
-//import com.intel.analytics.bigdl.dllib.utils.PythonInterpreter
+// import com.intel.analytics.bigdl.dllib.utils.PythonInterpreter
 import com.intel.analytics.bigdl.dllib.feature.common.{ArrayLike, ArrayLikeWrapper}
-//import com.intel.analytics.bigdl.dllib.feature.pmem._
+// import com.intel.analytics.bigdl.dllib.feature.pmem._
 import com.intel.analytics.bigdl.dllib.utils.Engine
 import org.apache.spark.{SparkContext, TaskContext}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 import org.slf4j.{Logger, LoggerFactory}
-//import jep._
+// import jep._
 
 import scala.reflect.ClassTag
 import scala.collection.JavaConverters._
@@ -331,7 +331,7 @@ class CachedDistributedFeatureSet[T: ClassTag]
   }
 }
 //
-//object PythonFeatureSet{
+// object PythonFeatureSet{
 //  // One partition one loader
 //  protected def getLocalLoader(loaderName: String): String = {
 //    s"${loaderName}_${TaskContext.getPartitionId()}"
@@ -419,9 +419,9 @@ class CachedDistributedFeatureSet[T: ClassTag]
 //      Tensor[Float]()
 //    }
 //  }
-//}
+// }
 //
-//class PythonFeatureSet[T: ClassTag](
+// class PythonFeatureSet[T: ClassTag](
 //    dataset: Array[Byte],
 //    getLoader: (Int, Int, String) => String,
 //    getIterator: (String, String, Boolean) => String,
@@ -543,7 +543,7 @@ class CachedDistributedFeatureSet[T: ClassTag]
 //  override def toDistributed(): DistributedDataSet[T] = {
 //    new DistributedDataSetWrapper[T](this)
 //  }
-//}
+// }
 
 /**
  * Wrap a RDD as a FeatureSet. RDD will be persist on local disk, and will load

diff --git a/...b/src/main/scala/com/intel/analytics/bigdl/dllib/feature/common/MTSampleToMiniBatch.scala b/...b/src/main/scala/com/intel/analytics/bigdl/dllib/feature/common/MTSampleToMiniBatch.scala
@@ -101,9 +101,11 @@ object MTSampleToMiniBatch {
    * @param batchSize           total batch size
    * @param transformer         transformer who rawData to Sample
    * @param featurePaddingParam feature padding strategy, see
-   *                            [[com.intel.analytics.bigdl.dllib.feature.dataset.PaddingParam]] for details.
+   *                            [[com.intel.analytics.bigdl.dllib.feature.dataset.PaddingParam]]
+   *                            for details.
    * @param labelPaddingParam   label padding strategy, see
-   *                            [[com.intel.analytics.bigdl.dllib.feature.dataset.PaddingParam]] for details.
+   *                            [[com.intel.analytics.bigdl.dllib.feature.dataset.PaddingParam]]
+   *                            for details.
    * @return
    */
   def apply[A: ClassTag, T: ClassTag](

diff --git a/...a/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/common/Preprocessing.scala b/...a/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/common/Preprocessing.scala
@@ -53,9 +53,11 @@ trait Preprocessing[A, B] extends Transformer[A, B] {
 
 def apply(imageSet: ImageSet): ImageSet = {
     this match {
-case xs: com.intel.analytics.bigdl.dllib.feature.common.Preprocessing[com.intel.analytics.bigdl.dllib.feature.transform.vision.image.ImageFeature,com.intel.analytics.bigdl.dllib.feature.transform.vision.image.ImageFeature] => 
-imageSet.transform(this.asInstanceOf[Preprocessing[ImageFeature, ImageFeature]])
-case _ => throw new IllegalArgumentException("We expect " +
+      case xs: com.intel.analytics.bigdl.dllib.feature.common.Preprocessing[com.intel.analytics.
+      bigdl.dllib.feature.transform.vision.image.ImageFeature, com.intel.analytics.bigdl.dllib.
+      feature.transform.vision.image.ImageFeature] =>
+        imageSet.transform(this.asInstanceOf[Preprocessing[ImageFeature, ImageFeature]])
+      case _ => throw new IllegalArgumentException("We expect " +
         "Preprocessing[ImageFeature, ImageFeature] here")
 }
 //    if (this.isInstanceOf[Preprocessing[ImageFeature, ImageFeature]]) {

diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/pmem/FeatureSet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/pmem/FeatureSet.scala
@@ -1,4 +1,4 @@
-///*
+// /*
 // * Copyright 2018 Analytics Zoo Authors.
 // *
 // * Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,26 +14,27 @@
 // * limitations under the License.
 // */
 //
-//package com.intel.analytics.bigdl.dllib.feature.pmem
+// package com.intel.analytics.bigdl.dllib.feature.pmem
 //
-//import com.intel.analytics.bigdl.dllib.feature.dataset.{ByteRecord, Sample}
-//import com.intel.analytics.bigdl.dllib.feature.transform.vision.image.ImageFeature
-//import com.intel.analytics.bigdl.dllib.feature.{CachedDistributedFeatureSet, DistributedFeatureSet}
-//import com.intel.analytics.bigdl.dllib.feature.common.ArrayLike
-//import org.apache.spark.rdd.RDD
+// import com.intel.analytics.bigdl.dllib.feature.dataset.{ByteRecord, Sample}
+// import com.intel.analytics.bigdl.dllib.feature.transform.vision.image.ImageFeature
+// import com.intel.analytics.bigdl.dllib.feature.{CachedDistributedFeatureSet,
+// DistributedFeatureSet}
+// import com.intel.analytics.bigdl.dllib.feature.common.ArrayLike
+// import org.apache.spark.rdd.RDD
 //
-//import scala.reflect.ClassTag
+// import scala.reflect.ClassTag
 //
-//private[bigdl] abstract class NativeArrayConverter[T: ClassTag]
+// private[bigdl] abstract class NativeArrayConverter[T: ClassTag]
 //  extends Serializable {
 //
 //  def getBytesPerRecord(record: T): Long
 //
 //  def toArray(recordIterator: Iterator[T],
 //      countPerPartition: Iterator[(Int, Long)]): Iterator[ArrayLike[T]]
-//}
+// }
 //
-//private[bigdl] class ByteRecordConverter(
+// private[bigdl] class ByteRecordConverter(
 //    memoryType: MemoryType = PMEM) extends NativeArrayConverter[ByteRecord] {
 //
 //  override def getBytesPerRecord(byteRecord: ByteRecord): Long = {
@@ -55,9 +56,9 @@
 //      }
 //      Iterator.single(ByteRecordArray(nativeArray, labels))
 //    }
-//}
+// }
 //
-//private[bigdl] case class ByteRecordArray(records: VarLenBytesArray,
+// private[bigdl] case class ByteRecordArray(records: VarLenBytesArray,
 //    label: Array[Float]) extends ArrayLike[ByteRecord] {
 //  override def length: Int = {
 //    records.recordNum
@@ -68,9 +69,9 @@
 //  override def free(): Unit = {
 //    records.free()
 //  }
-//}
+// }
 //
-//private[bigdl] class SampleConverter(
+// private[bigdl] class SampleConverter(
 //    memoryType: MemoryType = PMEM) extends NativeArrayConverter[Sample[Float]] {
 //
 //  override def getBytesPerRecord(sample: Sample[Float]): Long = {
@@ -96,9 +97,9 @@
 //    }
 //    Iterator.single(SampleArray(nativeArray, featureSizes, labelSizes))
 //  }
-//}
+// }
 //
-//private[bigdl] case class SampleArray(
+// private[bigdl] case class SampleArray(
 //    samples: VarLenFloatsArray,
 //    featureSizes: Array[Array[Array[Int]]],
 //    labelSizes: Array[Array[Array[Int]]]) extends ArrayLike[Sample[Float]] {
@@ -113,9 +114,9 @@
 //  override def free(): Unit = {
 //    samples.free()
 //  }
-//}
+// }
 //
-//private[bigdl] class ImageFeatureConverter(
+// private[bigdl] class ImageFeatureConverter(
 //      memoryType: MemoryType = PMEM) extends NativeArrayConverter[ImageFeature] {
 //
 //  override def getBytesPerRecord(imageFeature: ImageFeature): Long = {
@@ -145,14 +146,14 @@
 //    }
 //    Iterator.single(ImageFeatureArray(nativeArray, metrics))
 //  }
-//}
+// }
 //
-///**
+// /**
 // * Cached ImageFeatures in PMEM.
 // * @param bytesData bytes in PMEM.
 // * @param metrics ImageFeature without bytes, just some metrics.
 // */
-//private[bigdl] case class ImageFeatureArray(
+// private[bigdl] case class ImageFeatureArray(
 //      bytesData: VarLenBytesArray,
 //      metrics: Array[ImageFeature]) extends ArrayLike[ImageFeature] {
 //  override def length: Int = {
@@ -166,9 +167,9 @@
 //  override def free(): Unit = {
 //    bytesData.free()
 //  }
-//}
+// }
 //
-//object PmemFeatureSet {
+// object PmemFeatureSet {
 //
 //  private def rdd[T: ClassTag](data: RDD[T],
 //      nativeArrayConverter: NativeArrayConverter[T],
@@ -221,4 +222,5 @@
 //          s"${implicitly[ClassTag[T]].runtimeClass} is not supported for now")
 //    }
 //  }
-//}
+// }
+// }
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/pmem/FloatArray.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/pmem/FloatArray.scala
@@ -1,4 +1,4 @@
-///*
+// /*
 // * Copyright 2018 Analytics Zoo Authors.
 // *
 // * Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,11 +14,11 @@
 // * limitations under the License.
 // */
 //
-//package com.intel.analytics.bigdl.dllib.feature.pmem
+// package com.intel.analytics.bigdl.dllib.feature.pmem
 //
-//import org.apache.spark.unsafe.Platform
+// import org.apache.spark.unsafe.Platform
 //
-//object FloatArray {
+// object FloatArray {
 //  def apply(iterator: Iterator[Float], numOfRecord: Int,
 //      memoryType: MemoryType = PMEM): FloatArray = {
 //    val nativeArray = new FloatArray(numOfRecord, memoryType = memoryType)
@@ -29,13 +29,13 @@
 //    }
 //    nativeArray
 //  }
-//}
+// }
 //
-///**
+// /**
 // * An float array with fixed size stored in native memory.
 // * @param recordNum number of item for this array.
 // */
-//class FloatArray(val recordNum: Int,
+// class FloatArray(val recordNum: Int,
 //    sizeOfRecordByBytes: Int = 4,
 //    memoryType: MemoryType = PMEM) extends NativeArray[Float](
 //  recordNum * sizeOfRecordByBytes, memoryType) {
@@ -55,9 +55,9 @@
 //    assert(index <= lastOffSet)
 //    index
 //  }
-//}
+// }
 //
-//object VarLenFloatsArray {
+// object VarLenFloatsArray {
 //  // Backward compatible with Spark.6
 //  val FLOAT_ARRAY_OFFSET = {
 //    var unsafe: sun.misc.Unsafe = null
@@ -78,10 +78,10 @@
 //      0
 //    }
 //  }
-//}
+// }
 //
 //
-//class VarLenFloatsArray(recordNum: Int, totalSizeByBytes: Long,
+// class VarLenFloatsArray(recordNum: Int, totalSizeByBytes: Long,
 //    memoryType: MemoryType = PMEM) extends NativeVarLenArray[Float](recordNum,
 //  totalSizeByBytes, memoryType, 2) {
 //
@@ -90,4 +90,4 @@
 //  }
 //
 //  override def getTypeOffSet(): Int = VarLenFloatsArray.FLOAT_ARRAY_OFFSET
-//}
+// }
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/pmem/NativeArray.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/pmem/NativeArray.scala
@@ -1,4 +1,4 @@
-///*
+// /*
 // * Copyright 2018 Analytics Zoo Authors.
 // *
 // * Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,27 +14,27 @@
 // * limitations under the License.
 // */
 //
-//package com.intel.analytics.bigdl.dllib.feature.pmem
+// package com.intel.analytics.bigdl.dllib.feature.pmem
 //
-//import scala.collection.mutable.ArrayBuffer
+// import scala.collection.mutable.ArrayBuffer
 //
-//sealed trait MemoryType extends Serializable
+// sealed trait MemoryType extends Serializable
 //
-//case object PMEM extends MemoryType
+// case object PMEM extends MemoryType
 //
-//case object DRAM extends MemoryType
+// case object DRAM extends MemoryType
 //
-//case object DIRECT extends MemoryType
+// case object DIRECT extends MemoryType
 //
-//case class DISK_AND_DRAM(numSlice: Int) extends MemoryType
+// case class DISK_AND_DRAM(numSlice: Int) extends MemoryType
 //
-//sealed trait DataStrategy
+// sealed trait DataStrategy
 //
-//case object PARTITIONED extends DataStrategy
+// case object PARTITIONED extends DataStrategy
 //
-//case object REPLICATED extends DataStrategy
+// case object REPLICATED extends DataStrategy
 //
-//object MemoryType {
+// object MemoryType {
 //  def fromString(str: String): MemoryType = {
 //    val diskPattern = "DISK_(\\d+)".r
 //    str.toUpperCase() match {
@@ -47,21 +47,21 @@
 //              s"excepted PMEM, DRAM, DIRECT or DISK_n.")
 //    }
 //  }
-//}
+// }
 //
-//object NativeArray {
+// object NativeArray {
 //  private val natives = new ArrayBuffer[NativeArray[_]]()
 //
 //  def free(): Unit = {
 //    NativeArray.natives.map{_.free()}
 //  }
-//}
+// }
 //
-///**
+// /**
 // *
 // * @param totalBytes
 // */
-//abstract class NativeArray[T](totalBytes: Long, memoryType: MemoryType) {
+// abstract class NativeArray[T](totalBytes: Long, memoryType: MemoryType) {
 //
 //  assert(totalBytes > 0, s"The size of bytes should be larger than 0, but got: ${totalBytes}!")
 //
@@ -91,6 +91,6 @@
 //  }
 //
 //  protected def indexOf(i: Int): Long
-//}
+// }
 //
 //