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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.nn
import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule}
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.serializer._
import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter
import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule}
import com.intel.analytics.bigdl.utils.Shape
import scala.reflect.ClassTag
import scala.reflect.runtime.universe
/**
* The `forward(input)` reshape the input tensor into a
* `size(0) * size(1) * ...` tensor, taking the elements row-wise.
*
* @param size the reshape size
* @param batchMode It is a optional argument. If it is set to `Some(true)`,
* the first dimension of input is considered as batch dimension,
* and thus keep this dimension size fixed. This is necessary
* when dealing with batch sizes of one. When set to `Some(false)`,
* it forces the entire input (including the first dimension) to be reshaped
* to the input size. Default is `None`, which means the module considers
* inputs with more elements than the product of provided sizes (size(0) *
* size(1) * ..) to be batches, otherwise in no batch mode.
*
*/
@SerialVersionUID(- 830146931795053244L)
class Reshape[T: ClassTag](
val size: Array[Int], var batchMode: Option[Boolean] = None)(
implicit ev: TensorNumeric[T]) extends TensorModule[T] {
val batchSize = new Array[Int](size.length + 1)
var nElement: Int = 1
for (i <- 1 to size.length) {
batchSize(i) = size(i - 1)
nElement *= size(i - 1)
}
// whether share the storage between input and output
// in this layer, if input is contiguous, inplace is true. otherwise, inplace is false
private var inplace: Boolean = true
override def updateOutput(input: Tensor[T]): Tensor[T] = {
if ((batchMode.nonEmpty && !batchMode.get) ||
(input.nElement() == nElement && batchMode.isEmpty && input.size(1) != 1)) {
require(input.nElement() == nElement, s"element number must match Reshape size. " +
s"But In ${this.getName()} : element number is: ${ input.nElement() } , " +
s"reshape size is: ${nElement}")
if (input.isContiguous()) output =
input.view(size)
else {
output = input.contiguous().view(size)
inplace = false
}
}
else {
require(input.nElement() == nElement * input.size(1),
s"element number must match Reshape size. " +
s"But In ${this.getName()} : element number is: ${ input.nElement() } , " +
s"reshape size is: ${ nElement * input.size(1) }")
batchSize(0) = input.size(1)
if (input.isContiguous()) {
output = input.view(batchSize)
} else {
output = input.contiguous().view(batchSize)
inplace = false
}
}
output
}
override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
if (gradOutput.isContiguous()) {
gradInput = gradOutput.view(input.size())
} else {
gradInput = gradOutput.contiguous().view(input.size())
}
gradInput
}
override def equals(obj: Any): Boolean = {
if (!super.equals(obj)) {
return false
}
if (!obj.isInstanceOf[Reshape[T]]) {
return false
}
val other = obj.asInstanceOf[Reshape[T]]
if (this.eq(other)) {
return true
}
var i = 0
while (i < batchSize.length) {
if (batchSize(i) != other.batchSize(i)) {
return false
}
i += 1
}
nElement == other.nElement &&
batchMode == other.batchMode
}
override def hashCode() : Int = {
val seed = 37
var hash = super.hashCode()
var i = 0
while (i < batchSize.length) {
hash = hash * seed + batchSize(i).hashCode()
i += 1
}
hash = hash * seed + nElement.hashCode()
hash = hash * seed + batchMode.hashCode()
hash
}
override def toString(): String = {
s"${getPrintName}(${size.mkString("x")})"
}
override def clearState(): this.type = {
if (!inplace) {
super.clearState()
}
this
}
override def computeOutputShape(inputShape: Shape): Shape = {
val input = inputShape.toSingle().toArray
val output = if ((batchMode.nonEmpty && !batchMode.get) ||
(input.product == nElement && batchMode.isEmpty && input(0) != 1)) {
size
} else {
Array(input(0)) ++ batchSize.slice(1, batchSize.length)
}
Shape(output)
}
}
object Reshape extends ModuleSerializable {
def apply[T: ClassTag](
size: Array[Int],
batchMode: Option[Boolean] = None)(implicit ev: TensorNumeric[T]) : Reshape[T] = {
new Reshape[T](size, batchMode)
}
override def doLoadModule[T: ClassTag](context: DeserializeContext)
(implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = {
val attrMap = context.bigdlModule.getAttrMap
val size = DataConverter.getAttributeValue(context, attrMap.get("size")).
asInstanceOf[Array[Int]]
val batchModeV = DataConverter.getAttributeValue(context, attrMap.get("batchMode")).
asInstanceOf[Int]
var batchMode : Option[Boolean] = None
if (batchModeV == 1) {
batchMode = Some(false)
} else if (batchModeV == 2) {
batchMode = Some(true)
}
Reshape(size, batchMode).asInstanceOf[AbstractModule[Activity, Activity, T]]
}
override def doSerializeModule[T: ClassTag](context: SerializeContext[T],
reshapeBuilder : BigDLModule.Builder)
(implicit ev: TensorNumeric[T]) : Unit = {
val reshape = context.moduleData.module.asInstanceOf[Reshape[T]]
val sizeBuilder = AttrValue.newBuilder
DataConverter.setAttributeValue(context, sizeBuilder, reshape.size,
universe.typeOf[Array[Int]])
reshapeBuilder.putAttr("size", sizeBuilder.build)
var batchMode = 0
if (reshape.batchMode != None) {
batchMode = if (reshape.batchMode.get == false) 1 else 2
}
val batchModeBuilder = AttrValue.newBuilder
DataConverter.setAttributeValue(context, batchModeBuilder, batchMode,
universe.typeOf[Int])
reshapeBuilder.putAttr("batchMode", batchModeBuilder.build)
}
}
