com.intel.analytics.bigdl.nn.tf.SplitAndSelect.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.tf
import com.intel.analytics.bigdl.nn.abstractnn.TensorModule
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import scala.reflect.ClassTag
/**
* First split the tensor along the [[dimension]] into [[numSplit]] sub tensors,
* then select the [[index]]th one
*/
@SerialVersionUID(-9096120159559947483L)
private[bigdl] class SplitAndSelect[T: ClassTag](dimension: Int, index: Int, numSplit: Int)
(implicit ev: TensorNumeric[T]) extends TensorModule[T] {
override def updateOutput(input: Tensor[T]): Tensor[T] = {
val dim = if (dimension < 0) input.dim() + dimension + 1 else dimension
val dimSize = input.size(dimension)
require(dimSize % numSplit == 0,
s"numSplit must evenly divides input.size(dimension), " +
s"numSplit: $numSplit, dimension: $dimension, dimSize: $dimSize")
val length = dimSize / numSplit
val offset = (index - 1) * length + 1
val outputNarrow = input.narrow(dim, offset, length)
output.resizeAs(outputNarrow).copy(outputNarrow)
output
}
override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
val dim = if (dimension < 0) input.dim() + dimension + 1 else dimension
val dimSize = input.size(dimension)
val length = dimSize / numSplit
val offset = (index - 1) * length + 1
gradInput.resizeAs(input).zero()
gradInput.narrow(dim, offset, length).copy(gradOutput)
gradInput
}
}
private[bigdl] object SplitAndSelect {
def apply[T: ClassTag](
dimension: Int,
index: Int,
numSplit: Int)(implicit ev: TensorNumeric[T]) : SplitAndSelect[T] = {
new SplitAndSelect[T](dimension, index, numSplit)
}
}