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Predictor.scala
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Predictor.scala
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/*
* Copyright 2016 The BigDL 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.
*/
package com.intel.analytics.bigdl.optim
import com.intel.analytics.bigdl._
import com.intel.analytics.bigdl.dataset.{MiniBatch, PaddingParam, Sample, SampleToMiniBatch, Transformer, DataSet => _}
import com.intel.analytics.bigdl.models.utils.ModelBroadcast
import com.intel.analytics.bigdl.nn.{Graph, StaticGraph}
import com.intel.analytics.bigdl.nn.abstractnn.Activity
import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase}
import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnContainer}
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame}
import com.intel.analytics.bigdl.utils._
import com.intel.analytics.bigdl.utils.intermediate.{ConversionUtils, IRGraph}
import org.apache.spark.rdd.RDD
import scala.reflect.ClassTag
object Predictor {
def apply[T: ClassTag](model: Module[T],
featurePaddingParam: Option[PaddingParam[T]] = None,
batchPerPartition: Int = 4)
(implicit ev: TensorNumeric[T]): Predictor[T] = {
new Predictor[T](model, featurePaddingParam, batchPerPartition)
}
private[optim] def predictImageBatch[T: ClassTag](
localModel: Module[T], imageFeatures: Seq[ImageFeature],
outputLayer: String, predictKey: String,
localToBatch: Transformer[Sample[T], MiniBatch[T]],
shareBuffer: Boolean)(implicit ev: TensorNumeric[T]): Seq[ImageFeature] = {
val validImageFeatures = imageFeatures.filter(_.isValid)
val samples = validImageFeatures.map(x => x[Sample[T]](ImageFeature.sample))
val batchOut = predictSamples(localModel, samples, localToBatch, shareBuffer, outputLayer)
validImageFeatures.toIterator.zip(batchOut).foreach(tuple => {
tuple._1(predictKey) = tuple._2
})
imageFeatures
}
private[optim] def predictSamples[T: ClassTag]
(localModel: Module[T], samples: Seq[Sample[T]],
localToBatch: Transformer[Sample[T], MiniBatch[T]],
shareBuffer: Boolean,
outputLayer: String = null)(implicit ev: TensorNumeric[T]): Iterator[Activity] = {
val layer = if (outputLayer == null) {
localModel
} else {
val ol = localModel(outputLayer)
require(ol.isDefined, s"cannot find layer that map name $outputLayer")
ol.get
}
localToBatch(samples.toIterator).flatMap(batch => {
localModel.forward(batch.getInput())
splitBatch[T](layer.output, shareBuffer, batch.size())
})
}
private[optim] def splitTensor[T: ClassTag](output: Tensor[T],
shareBuffer: Boolean, batchSize: Int)
(implicit ev: TensorNumeric[T]): Array[Activity] = {
val result = if (shareBuffer) output else output.clone
val out = if (batchSize == 1) {
Array(result.squeeze)
} else {
val size = result.size(1)
require(batchSize == size,
s"The batchSize is required to be $size, while actual is $batchSize")
result.split(1)
}
out.asInstanceOf[Array[Activity]]
}
private[optim] def splitBatch[T: ClassTag](output: Activity, shareBuffer: Boolean, batchSize: Int)
(implicit ev: TensorNumeric[T]): Array[Activity] = {
val out = if (output.isTensor) {
splitTensor(output.toTensor, shareBuffer, batchSize)
} else {
val result = output.toTable
val tables = new Array[Table](batchSize)
(1 to result.length()).foreach(key => {
val split = splitBatch(result(key), shareBuffer, batchSize)
val size = split.length
require(batchSize == size,
s"The batchSize is required to be $size, while actual is $batchSize")
var i = 0
while (i < batchSize) {
if (tables(i) == null) tables(i) = T()
tables(i).insert(split(i))
i += 1
}
})
tables
}
out.asInstanceOf[Array[Activity]]
}
def predictImage[T: ClassTag](imageFrame: DistributedImageFrame,
outputLayer: String = null,
shareBuffer: Boolean = false,
predictKey: String = ImageFeature.predict,
batchPerPartition: Int,
model: Module[T],
featurePaddingParam: Option[PaddingParam[T]])(
implicit ev: TensorNumeric[T]): DistributedImageFrame = {
val dummyInput = getDummyData(imageFrame.rdd, batchPerPartition)
val totalBatch = imageFrame.rdd.partitions.length * batchPerPartition
val rdd = ConversionUtils.coalesce(imageFrame.asInstanceOf[DistributedImageFrame].rdd)
val modelBroad = ModelBroadcast[T]().broadcast(rdd.sparkContext,
ConversionUtils.convert(model.evaluate()), dummyInput)
val realPartitionLength = rdd.partitions.length
val toBatchBroad = rdd.sparkContext.broadcast(SampleToMiniBatch(
batchSize = totalBatch,
partitionNum = Some(realPartitionLength),
featurePaddingParam = featurePaddingParam), shareBuffer)
val localBatchPerPartition = totalBatch / realPartitionLength
val result = rdd.mapPartitions(partition => {
val localModel = modelBroad.value(false, true, dummyInput)
val localToBatch = toBatchBroad.value._1.cloneTransformer()
partition.grouped(localBatchPerPartition).flatMap(imageFeatures => {
Predictor.predictImageBatch[T](localModel, imageFeatures, outputLayer, predictKey,
localToBatch, shareBuffer)
imageFeatures
})
})
ImageFrame.rdd(result)
}
def predict[T: ClassTag](dataSet: RDD[Sample[T]], batchSize: Int = -1,
shareBuffer: Boolean = false, model: Module[T], batchPerPartition: Int,
featurePaddingParam: Option[PaddingParam[T]])(implicit ev: TensorNumeric[T]): RDD[Activity] = {
val partitionNum = dataSet.partitions.length
val totalBatch = if (batchSize > 0) {
require(batchSize % partitionNum == 0, s"Predictor.predict: total batch size $batchSize " +
s"should be divided by partitionNum ${partitionNum}")
batchSize
} else {
batchPerPartition * partitionNum
}
val dummyInput = getDummyData(dataSet, totalBatch / partitionNum)
val modelBroad = ModelBroadcast[T]().broadcast(dataSet.sparkContext,
ConversionUtils.convert(model.evaluate()), dummyInput)
val rdd = ConversionUtils.coalesce(dataSet)
val otherBroad = rdd.sparkContext.broadcast(SampleToMiniBatch(
batchSize = totalBatch,
partitionNum = Some(rdd.partitions.length),
featurePaddingParam = featurePaddingParam))
rdd.mapPartitions { partition =>
val localModel = modelBroad.value(false, true, dummyInput)
val localTransformer = otherBroad.value.cloneTransformer()
val miniBatch = localTransformer(partition)
miniBatch.flatMap(batch => {
val output = localModel.forward(batch.getInput)
splitBatch(output, shareBuffer, batch.size())
})
}
}
def predictClass[T: ClassTag](dataSet: RDD[Sample[T]], batchSize: Int = -1, model: Module[T],
batchPerPartition: Int, featurePaddingParam: Option[PaddingParam[T]])(
implicit ev: TensorNumeric[T]): RDD[Int] = {
val result = Predictor.predict(dataSet, batchSize, true, model,
batchPerPartition, featurePaddingParam)
result.mapPartitions { partition =>
partition.map(output => {
val _output = output.toTensor[T]
require(_output.dim() == 1, s"Predictor.predictClass:" +
s"Only support one sample has one label, but got ${_output.dim()} label")
ev.toType[Int](_output.max(1)._2.valueAt(1))
})
}
}
// because Evaluator will use it too, we extend the scope out of Predictor
private[optim] def getDummyData[T: ClassTag, R](dataSet: RDD[R],
batchSize: Int)(implicit ev: TensorNumeric[T]): Activity = {
if (Engine.getEngineType() == MklDnn && Engine.isMultiModels) {
// here has an assumption, batchSizePerPar is not very large.
val samples = dataSet.takeSample(withReplacement = false, num = batchSize)
.map {
case feature: ImageFeature => feature[Sample[T]](ImageFeature.sample)
case sample => sample.asInstanceOf[Sample[T]]
}
val sampleToMiniBatch = SampleToMiniBatch(batchSize, partitionNum = Some(1))
val miniBatch = sampleToMiniBatch(samples.toIterator).toSeq
miniBatch.head.getInput()
} else {
Tensor()
}
}
}
/**
* Predictor for distributed data
*
* NOTE: The `predictClass`, `predict` and `predictImage` will call the relevant methods of
* object `Predictor`. Why we do this? Because every these methods uses the ClassTag `T`. If we do
* these jobs in the methods of class`Predictor`, when we do `mapPartition`, Spark will find all
* used values and do serialization. The `T` is the argument of constructor, the serialization will
* package the whole `Predictor` class, which contains the`model`. It will send a duplicate model
* to the workers. So we should move these methods to object `Predictor`.
*
* @param model BigDL model
* @param featurePaddingParam featurePaddingParam if the inputs have variant size
* @param batchPerPartition batch size per partition, default is 4
*/
class Predictor[T: ClassTag] private[optim](
model: Module[T],
featurePaddingParam: Option[PaddingParam[T]] = None,
batchPerPartition: Int = 4)
(implicit ev: TensorNumeric[T]) extends Serializable {
def predictClass(dataSet: RDD[Sample[T]], batchSize: Int = -1): RDD[Int] = {
Predictor.predictClass(dataSet, batchSize, model, batchPerPartition, featurePaddingParam)
}
def predict(dataSet: RDD[Sample[T]], batchSize: Int = -1,
shareBuffer: Boolean = false): RDD[Activity] = {
Predictor.predict(dataSet, batchSize, shareBuffer, model, batchPerPartition,
featurePaddingParam)
}
/**
* model predict DistributedImageFrame, return imageFrame with predicted tensor
* @param imageFrame imageFrame that contains images
* @param outputLayer if outputLayer is not null, the output of layer that matches
* outputLayer will be used as predicted output
* @param shareBuffer whether to share same memory for each batch predict results
* @param predictKey key to store predicted result
*/
def predictImage(imageFrame: DistributedImageFrame,
outputLayer: String = null,
shareBuffer: Boolean = false,
predictKey: String = ImageFeature.predict): DistributedImageFrame = {
Predictor.predictImage(imageFrame, outputLayer, shareBuffer, predictKey, batchPerPartition,
model, featurePaddingParam)
}
}