com.intel.analytics.bigdl.nn.CAveTable.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._
/**
* Merge the input tensors in the input table by element wise taking the average. The input table
* is actually an array of tensor with same size.
* @param inplace reuse the input memory
* @param ev numeric operator
* @tparam T Numeric type. Only support float/double now
*/
@SerialVersionUID(- 4230815940936944708L)
class CAveTable[T: ClassTag](val inplace: Boolean = false)(
implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T] {
override def updateOutput(input: Table): Tensor[T] = {
if (inplace) {
output.set(input[Tensor[T]](1))
} else {
output.resizeAs(input[Tensor[T]](1)).copy(input[Tensor[T]](1))
}
var i = 2
while (i <= input.length()) {
output.add(input[Tensor[T]](i))
i += 1
}
output.div(ev.fromType(input.length()))
}
override def updateGradInput(input: Table, gradOutput: Tensor[T]) : Table = {
var i = 1
val size = input.length()
val gradResult = gradOutput.div(ev.fromType(size))
while (i <= size) {
if (i > gradInput.length()) gradInput.insert(i, Tensor[T]().resizeAs(input(1)))
if (inplace) {
gradInput[Tensor[T]](i).set(gradResult)
} else {
gradInput[Tensor[T]](i).resizeAs(gradOutput).copy(gradResult)
}
i += 1
}
i = input.length() + 1
while (i <= gradInput.length) {
gradInput.remove(i)
}
gradInput
}
override def clearState(): this.type = {
if (!inplace) {
super.clearState()
}
this
}
}
object CAveTable {
def apply[@specialized(Float, Double) T: ClassTag](
inplace: Boolean = false)(implicit ev: TensorNumeric[T]) : CAveTable[T] = {
new CAveTable[T](inplace)
}
}