com.intel.analytics.bigdl.nn.Select.scala Maven / Gradle / Ivy

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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.TensorModule
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric

import scala.reflect.ClassTag

/**
 * A Simple layer selecting an index of the input tensor in the given dimension
 *
 * @param dimension the dimension to select
 * @param index the index of the dimension to be selected
 */

@SerialVersionUID(1581502108010704056L)
class Select[T: ClassTag](
  dimension: Int,
  index: Int
)(implicit ev: TensorNumeric[T])
  extends TensorModule[T] {
  def getPositiveDimAndIndex(input: Tensor[T]): (Int, Int) = {
    val dim = if (dimension < 0) {
      input.dim() + dimension + 1
    } else {
      dimension
    }

    val index = if (this.index < 0) {
      input.size(dim) + this.index + 1
    } else {
      this.index
    }
    (dim, index)
  }

  override def updateOutput(input: Tensor[T]): Tensor[T] = {
    val (dim, index) = getPositiveDimAndIndex(input)
    if ((dim == 2) && (input.dim() > 2)) {
      Recurrent.selectCopy(input, index, this.output)
    } else {
      val output = input.select(dim, index)
      this.output.resizeAs(output)

      this.output.copy(output)
    }
  }

  override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
    val (dim, index) = getPositiveDimAndIndex(input)
    gradInput.resizeAs(input)
    gradInput.zero()
    if ((dim == 2) && (gradInput.dim() > 2)) {
      Recurrent.copyToIndex(gradOutput, gradInput, index)
    } else {
      gradInput.select(dim, index).copy(gradOutput)
    }
    gradInput
  }

  override def toString: String = s"nn.Select"
}

object Select {
  def apply[@specialized(Float, Double) T: ClassTag](
      dimension: Int,
      index: Int)(implicit ev: TensorNumeric[T]) : Select[T] = {
    new Select[T](dimension, index)
  }
}