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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.{AbstractCriterion, AbstractModule, Activity}
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
/**
* The criterion that takes two modules to transform input and target, and take
* one criterion to compute the loss with the transformed input and target.
*
* This criterion can be used to construct complex criterion. For example, the
* `inputTransformer` and `targetTransformer` can be pre-trained CNN networks,
* and we can use the networks' output to calculate the high-level feature
* reconstruction loss, which is commonly used in areas like neural style transfer
(https://arxiv.org/abs/1508.06576), texture synthesis (https://arxiv.org/abs/1505.07376),
.etc.
*
* @param inputTransformer
* @param targetTransformer
* @param criterion
* @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]]
*/
class TransformerCriterion[T: ClassTag](
criterion: AbstractCriterion[Activity, Activity, T],
inputTransformer: Option[AbstractModule[Activity, Activity, T]] = None,
targetTransformer: Option[AbstractModule[Activity, Activity, T]] = None
)(implicit ev: TensorNumeric[T]) extends AbstractCriterion[Activity, Activity, T]{
private var transformedInput: Activity = _
private var transformedTarget: Activity = _
override def updateOutput(input: Activity, target: Activity): T = {
transformedTarget = targetTransformer.map(t => t.forward(target))
.getOrElse(target) match {
case t: Tensor[T] => t.clone()
case t: Table => t.clone()
}
// if inputTransformer and target transformer are the same instance
// we must do inputTransformer last to preserve the forward state
transformedInput = inputTransformer.map(t => t.forward(input))
.getOrElse(input) match {
case t: Tensor[T] => t.clone()
case t: Table => t.clone()
}
output = criterion.forward(transformedInput, transformedTarget)
output
}
override def updateGradInput(input: Activity, target: Activity): Activity = {
require(transformedTarget != null && transformedInput != null, "please run forward first")
val gradInputCriterion = criterion.backward(transformedInput, transformedTarget)
gradInput = inputTransformer
.map(t => t.updateGradInput(input, gradInputCriterion))
.getOrElse(gradInputCriterion)
gradInput
}
}
object TransformerCriterion {
def apply[T: ClassTag](
criterion: AbstractCriterion[Activity, Activity, T],
inputTransformer: Option[AbstractModule[Activity, Activity, T]] = None,
targetTransformer: Option[AbstractModule[Activity, Activity, T]] = None
)(implicit ev: TensorNumeric[T]): TransformerCriterion[T] =
new TransformerCriterion(criterion, inputTransformer, targetTransformer)
}
