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104 lines
3.1 KiB
104 lines
3.1 KiB
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/utils/Logging.h"
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#include "paddle/utils/Stat.h"
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#include "PoolProjectionLayer.h"
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namespace paddle {
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size_t PoolProjectionLayer::getSize() {
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CHECK_EQ(inputLayers_.size(), 1UL);
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size_t layerSize = 0;
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imgSizeH_ = inputLayers_[0]->getOutput().getFrameHeight();
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imgSizeW_ = inputLayers_[0]->getOutput().getFrameWidth();
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if (imgSizeH_ == 0) {
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imgSizeH_ = imgSize_;
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}
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if (imgSizeW_ == 0) {
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imgSizeW_ = imgSize_;
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}
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outputH_ = 1 + (imgSizeH_ - start_ - sizeX_ + stride_ - 1) / stride_;
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outputW_ = 1 + (imgSizeW_ - start_ - sizeX_ + stride_ - 1) / stride_;
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layerSize = outputH_ * outputW_ * channels_;
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getOutput().setFrameHeight(outputH_);
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getOutput().setFrameWidth(outputW_);
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return layerSize;
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}
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void MaxPoolProjectionLayer::forward(PassType passType) {
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Layer::forward(passType);
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/* malloc memory for the output_ if necessary */
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/* note: one sample correspond to one ROW */
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MatrixPtr input = getInputValue(0);
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int batchSize = input->getHeight();
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int size = getSize();
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resetOutput(batchSize, size);
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MatrixPtr outV = getOutputValue();
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outV->maxPoolForward(*input, imgSizeH_, imgSizeW_, channels_, sizeX_, start_,
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stride_, outputH_, outputW_);
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}
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void MaxPoolProjectionLayer::backward(const UpdateCallback& callback) {
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(void)callback;
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if (NULL == getInputGrad(0)) {
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return;
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}
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/* Do derivation */
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MatrixPtr outGrad = getOutputGrad();
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MatrixPtr inputV = getInputValue(0);
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MatrixPtr outV = getOutputValue();
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MatrixPtr inputGrad = getInputGrad(0);
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inputGrad->maxPoolBackward(*inputV, imgSizeH_, imgSizeW_, *outGrad, *outV,
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sizeX_, start_, stride_, outputH_, outputW_, 1, 1);
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}
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void AvgPoolProjectionLayer::forward(PassType passType) {
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Layer::forward(passType);
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/* malloc memory for the output_ if necessary */
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/* note: one sample correspond to one ROW */
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MatrixPtr input = getInputValue(0);
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int batchSize = input->getHeight();
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int size = getSize();
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resetOutput(batchSize, size);
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MatrixPtr outV = getOutputValue();
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outV->avgPoolForward(*input, imgSizeH_, imgSizeW_, channels_, sizeX_, start_,
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stride_, outputH_, outputW_);
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}
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void AvgPoolProjectionLayer::backward(const UpdateCallback& callback) {
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(void)callback;
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if (NULL == getInputGrad(0)) {
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return;
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}
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/* Do derivation */
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MatrixPtr outputGrad = getOutputGrad();
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MatrixPtr inputGrad = getInputGrad(0);
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inputGrad->avgPoolBackward(*outputGrad, imgSizeH_, imgSizeW_, sizeX_, start_,
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stride_, outputH_, outputW_, 1, 1);
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}
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} // namespace paddle
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