com.intel.analytics.bigdl.nn.CMulTable.scala Maven / Gradle / Ivy
/*
* 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
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.Table
import scala.reflect.ClassTag
/**
* Takes a table of Tensors and outputs the multiplication of all of them.
*/
@SerialVersionUID(8888147326550637025L)
class CMulTable[T: ClassTag]()(
implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T]{
private var scalarIndexes : Array[Int] = _
override def updateOutput(input: Table): Tensor[T] = {
var scalar = ev.one
var hasTensor = false
var hasScalar = false
var initTensor = false
var i = 1
while (i <= input.length()) {
val curTensor = input[Tensor[T]](i)
if (curTensor.isScalar) {
scalar = ev.times(scalar, curTensor.value())
hasScalar = true
} else if (curTensor.isTensor) {
if (initTensor) {
output.cmul(curTensor)
} else {
output.resizeAs(curTensor).copy(curTensor)
initTensor = true
}
hasTensor = true
}
i += 1
}
if (hasTensor && hasScalar) {
output.mul(scalar)
} else if (hasScalar) {
output.resizeAs(input[Tensor[T]](1)).setValue(scalar)
}
output
}
override def updateGradInput(input: Table, gradOutput: Tensor[T]) : Table = {
var i = 1
while (i <= input.length()) {
if (!gradInput.contains(i)) gradInput.insert(i, Tensor[T]())
gradInput[Tensor[T]](i).resizeAs(gradOutput).copy(gradOutput)
var j = 1
while (j <= input.length()) {
if (i != j) {
if (input[Tensor[T]](j).isScalar) {
gradInput[Tensor[T]](i).mul(input[Tensor[T]](j).value())
} else {
gradInput[Tensor[T]](i).cmul(input(j))
}
}
j += 1
}
if (input[Tensor[T]](i).isScalar) {
val sum = gradInput[Tensor[T]](i).sum()
gradInput(i) = gradInput[Tensor[T]](i).resizeAs(input(i)).setValue(sum)
}
i += 1
}
gradInput
}
override def canEqual(other: Any): Boolean = other.isInstanceOf[CMulTable[T]]
override def equals(other: Any): Boolean = other match {
case that: CMulTable[T] =>
super.equals(that) &&
(that canEqual this)
case _ => false
}
override def hashCode(): Int = {
def getHashCode(a: Any): Int = if (a == null) 0 else a.hashCode()
val state = Seq(super.hashCode())
state.map(getHashCode).foldLeft(0)((a, b) => 31 * a + b)
}
}
object CMulTable {
def apply[@specialized(Float, Double) T: ClassTag]()
(implicit ev: TensorNumeric[T]) : CMulTable[T] = {
new CMulTable[T]()
}
}