org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor Maven / Gradle / Ivy
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* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
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* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.nn.conf.preprocessor;
import lombok.Data;
import lombok.EqualsAndHashCode;
import org.deeplearning4j.nn.api.MaskState;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.shade.jackson.annotation.JsonCreator;
import org.nd4j.shade.jackson.annotation.JsonProperty;
/**
* Composable input pre processor
* @author Adam Gibson
*/
@Data
@EqualsAndHashCode(callSuper = false)
public class ComposableInputPreProcessor extends BaseInputPreProcessor {
private InputPreProcessor[] inputPreProcessors;
@JsonCreator
public ComposableInputPreProcessor(@JsonProperty("inputPreProcessors") InputPreProcessor... inputPreProcessors) {
this.inputPreProcessors = inputPreProcessors;
}
@Override
public INDArray preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr) {
for (InputPreProcessor preProcessor : inputPreProcessors)
input = preProcessor.preProcess(input, miniBatchSize, workspaceMgr);
return workspaceMgr.leverageTo(ArrayType.ACTIVATIONS, input);
}
@Override
public INDArray backprop(INDArray output, int miniBatchSize, LayerWorkspaceMgr workspaceMgr) {
//Apply input preprocessors in opposite order for backprop (compared to forward pass)
//For example, CNNtoFF + FFtoRNN, need to do backprop in order of FFtoRNN + CNNtoFF
for (int i = inputPreProcessors.length - 1; i >= 0; i--) {
output = inputPreProcessors[i].backprop(output, miniBatchSize, workspaceMgr);
}
return workspaceMgr.leverageTo(ArrayType.ACTIVATION_GRAD, output);
}
@Override
public ComposableInputPreProcessor clone() {
ComposableInputPreProcessor clone = (ComposableInputPreProcessor) super.clone();
if (clone.inputPreProcessors != null) {
InputPreProcessor[] processors = new InputPreProcessor[clone.inputPreProcessors.length];
for (int i = 0; i < clone.inputPreProcessors.length; i++) {
processors[i] = clone.inputPreProcessors[i].clone();
}
clone.inputPreProcessors = processors;
}
return clone;
}
@Override
public InputType getOutputType(InputType inputType) {
for (InputPreProcessor p : inputPreProcessors) {
inputType = p.getOutputType(inputType);
}
return inputType;
}
@Override
public Pair feedForwardMaskArray(INDArray maskArray, MaskState currentMaskState,
int minibatchSize) {
for (InputPreProcessor preproc : inputPreProcessors) {
Pair p = preproc.feedForwardMaskArray(maskArray, currentMaskState, minibatchSize);
maskArray = p.getFirst();
currentMaskState = p.getSecond();
}
return new Pair<>(maskArray, currentMaskState);
}
}