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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.utils.tf
import java.io.{FileOutputStream, OutputStream}
import java.nio.ByteOrder
import com.google.protobuf.CodedOutputStream
import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity}
import com.intel.analytics.bigdl.nn._
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.{File, FileWriter, T}
import org.apache.log4j.Logger
import org.tensorflow.framework._
import scala.collection.mutable
import scala.collection.mutable.ArrayBuffer
import com.intel.analytics.bigdl.utils.tf.Tensorflow._
import scala.reflect.ClassTag
object TensorflowSaver {
/**
* Save a graph model to protobuf files so that it can be used in tensorflow inference.
*
* When save the model, placeholders will be added to the tf model as input nodes. So you need to
* pass in the names and shape for the placeholders. BigDL model doesn't have such information.
* The order of the placeholder information should be same as the inputs of the graph model
*
* @param model graph model instance
* @param inputs input node defs
* @param path where to save
* @param byteOrder model byte order
* @tparam T
*/
def saveGraphWithNodeDef[T](
model : Graph[T],
inputs : Seq[NodeDef],
path: String,
byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN,
extraNodes: Set[NodeDef] = Set()): Unit = {
val inputNodeCache =
new mutable.HashMap[String, ArrayBuffer[NodeDef]]()
model.inputs.zip(inputs).foreach(n => {
inputNodeCache(n._1.element.getName()) = ArrayBuffer(n._2)
})
val graphBuilder = GraphDef.newBuilder()
inputs.foreach(graphBuilder.addNode(_))
model.getSortedForwardExecutions.foreach(n => {
val nodeDefs = maps(n.element.getClass.getName).toTFDef(n.element,
inputNodeCache(n.element.getName()),
byteOrder)
nodeDefs.foreach(nDef => {
graphBuilder.addNode(nDef)
})
n.nextNodes.foreach(n => {
val list = inputNodeCache.getOrElse(n.element.getName(), ArrayBuffer())
list.append(nodeDefs(0))
inputNodeCache(n.element.getName()) = list
})
})
extraNodes.foreach(graphBuilder.addNode(_))
// Save to file
var fw: FileWriter = null
var out: OutputStream = null
try {
fw = FileWriter(path)
out = fw.create(true)
val output = CodedOutputStream.newInstance(out)
val graph = graphBuilder.build()
logger.debug("Graph definition is:")
logger.debug(graph.toString)
graph.writeTo(output)
output.flush()
out.flush()
logger.info(s"Save as tensorflow model file to $path")
} finally {
if (out != null) out.close()
if (fw != null) fw.close()
}
}
/**
* Save a graph model to protobuf files so that it can be used in tensorflow inference.
*
* When save the model, placeholders will be added to the tf model as input nodes. So you need to
* pass in the names and shape for the placeholders. BigDL model doesn't have such information.
* The order of the placeholder information should be same as the inputs of the graph model
*
* @param model graph model instance
* @param inputs placeholder information
* @param path where to save
* @param byteOrder model byte order
* @param dataFormat model data format
* @tparam T
*/
def saveGraph[T: ClassTag](
model : Graph[T],
inputs : Seq[(String, Seq[Int])],
path: String,
byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN,
dataFormat: TensorflowDataFormat = TensorflowDataFormat.NHWC)(
implicit ev: TensorNumeric[T]): Unit = {
// Check if there's pooling layer in which ceilMode is enable and pad is zero, we need double
// check if the ceilMode is real needed
val ceiledPoolingModules = model.modules.filter(m =>
if (m.isInstanceOf[SpatialMaxPooling[_]]) {
val a = m.asInstanceOf[SpatialMaxPooling[_]]
a.ceilMode == true && a.padH == 0 && a.padW == 0
} else if (m.isInstanceOf[SpatialAveragePooling[_]]) {
val a = m.asInstanceOf[SpatialAveragePooling[_]]
a.ceilMode == true && a.padH == 0 && a.padW == 0
} else {
false
})
if (ceiledPoolingModules.size != 0) {
val inputTensors = inputs.map(shape => Tensor[T]().resize(shape._2.toArray))
val inputActivity = if (inputTensors.size == 1) {
inputTensors.head
} else {
val t = T()
var i = 1
inputTensors.foreach(tensor => {
t(i) = tensor
i += 1
})
t
}
model.forward(inputActivity)
}
val inputNodeDefs = inputs.map(input =>
placeholder(model.getNumericType(), input._2, input._1)
)
saveGraphWithNodeDef(model, inputNodeDefs, path, byteOrder)
}
/**
* Register a customized BigDL module saver.
* @param className class name of the BigDL module
* @param saver customized saver
*/
def register(className : String, saver: BigDLToTensorflow): Unit = {
maps(className) = saver
}
private val logger = Logger.getLogger(getClass)
private val maps = mutable.Map[String, BigDLToTensorflow](
getNameFromObj(TemporalConvolution.getClass.getName) -> TemporalConvolutionToTF,
getNameFromObj(ReLU.getClass.getName) -> ReLUToTF,
getNameFromObj(Linear.getClass.getName) -> LinearToTF,
getNameFromObj(SpatialConvolution.getClass.getName) -> SpatialConvolutionToTF,
getNameFromObj(Squeeze.getClass.getName) -> SqueezeToTF,
getNameFromObj(Tanh.getClass.getName) -> TanhToTF,
getNameFromObj(Reshape.getClass.getName) -> ReshapeToTF,
getNameFromObj(View.getClass.getName) -> ViewToTF,
getNameFromObj(SpatialMaxPooling.getClass.getName) -> MaxpoolToTF,
getNameFromObj(Padding.getClass.getName) -> PaddingToTF,
getNameFromObj(SpatialAveragePooling.getClass.getName) -> AvgpoolToTF,
getNameFromObj(Sigmoid.getClass.getName) -> SigmoidToTF,
getNameFromObj(Dropout.getClass.getName) -> DropoutToTF,
getNameFromObj(CAddTable.getClass.getName) -> CAddTableToTF,
getNameFromObj(CMulTable.getClass.getName) -> CMultTableToTF,
getNameFromObj(JoinTable.getClass.getName) -> JoinTableToTF,
getNameFromObj(Mean.getClass.getName) -> MeanToTF,
getNameFromObj(SoftMax.getClass.getName) -> SoftMaxToTF,
getNameFromObj(LogSoftMax.getClass.getName) -> LogSoftMaxToTF,
getNameFromObj(SpatialBatchNormalization.getClass.getName) -> BatchNorm2DToTF,
getNameFromObj(Input.getClass.getName) -> InputToTF,
getNameFromObj(Sigmoid.getClass.getName) -> SigmoidToTF,
getNameFromObj(Scale.getClass.getName) -> ScaleToTF,
getNameFromObj(SpatialCrossMapLRN.getClass.getName) -> LRNToTF
)
private def getNameFromObj(name: String) : String = name.substring(0, name.length - 1)
}
