com.intel.analytics.bigdl.nn.AbsCriterion.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.TensorCriterion
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric

import scala.reflect.ClassTag

/**
 * measures the mean absolute value of the element-wise difference between input and target
 */
@SerialVersionUID( - 7860793007567513758L)
class AbsCriterion[@specialized(Float, Double) T: ClassTag](val sizeAverage: Boolean = true)
(implicit ev: TensorNumeric[T]) extends TensorCriterion[T] {
  @transient
  private var buffer: Tensor[T] = null

  override def updateOutput(input: Tensor[T], target : Tensor[T]): T = {
    if (null == buffer) buffer = Tensor[T]()
    buffer.resizeAs(input).add(input)
    buffer.mul(input, ev.fromType[Int](-1)).add(target).abs()

    output = buffer.sum()
    if (sizeAverage) output = ev.divide(output, ev.fromType[Int](input.nElement()))
    output
  }

  override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = {
    gradInput.resizeAs(input).zero()
    var norm : Double = 0
    if (sizeAverage)  {
      norm = 1.0/input.nElement()
    } else {
      norm = 1.0
    }
    gradInput.mul(input, ev.fromType[Int](-1)).add(target)

    require(gradInput.isContiguous(), "AbsCriterion: gradInput should be contiguous")
    val bufferArray = gradInput.storage().array()
    val bufferOffset = gradInput.storageOffset() - 1
    var i = 0
    while(i < gradInput.nElement()) {
      val z = bufferArray(i)
      bufferArray(i + bufferOffset) = ev.times(ev.fromType(norm),
        if (ev.isGreater(z, ev.fromType(0))) ev.fromType(-1) else ev.fromType(1))
      i += 1
    }
    gradInput
  }

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

  override def equals(other: Any): Boolean = other match {
    case that: AbsCriterion[T] =>
      super.equals(that) &&
        (that canEqual this) &&
        sizeAverage == that.sizeAverage
    case _ => false
  }

  override def hashCode(): Int = {
    def getHashCode(a: Any): Int = if (a == null) 0 else a.hashCode()
    val state = Seq(super.hashCode(), sizeAverage)
    state.map(getHashCode).foldLeft(0)((a, b) => 31 * a + b)
  }
}

object AbsCriterion {
  def apply[@specialized(Float, Double) T: ClassTag](
    sizeAverage: Boolean = true)(implicit ev: TensorNumeric[T]) : AbsCriterion[T] = {
    new AbsCriterion[T](sizeAverage)
  }
}