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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
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

/**
 * Applies the Tensor index operation along the given dimension.
 *
 * @param dimension
 */

@SerialVersionUID(2608373524149209793L)
class Index[T: ClassTag](dimension: Int)(implicit ev: TensorNumeric[T])
  extends AbstractModule[Table, Tensor[T], T]{

  override def updateOutput(input: Table): Tensor[T] = {
    val t = input[Tensor[T]](1)
    val index = input[Tensor[T]](2)
    output.index(dimension, index, t)
    output
  }

  override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = {
    val t = input[Tensor[T]](1)
    val index = input[Tensor[T]](2)

    if (!gradInput.contains(1)) gradInput.insert(1, Tensor[T])
    if (!gradInput.contains(2)) gradInput.insert(2, Tensor[T])

    gradInput[Tensor[T]](2).resizeAs(index).zero()
    gradInput[Tensor[T]](1).resizeAs(t).zero()
    gradInput[Tensor[T]](1).indexAdd(dimension, index, gradOutput)
    gradInput
  }

  override def canEqual(other: Any): Boolean = other.isInstanceOf[Index[T]]

  override def equals(other: Any): Boolean = other match {
    case that: Index[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)
  }

  override def toString(): String = {
    s"${getPrintName}($dimension)"
  }
}

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




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