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102 lines
3.0 KiB
102 lines
3.0 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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 "NormProjectionLayer.h"
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#include "paddle/utils/Logging.h"
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#include "paddle/utils/Stat.h"
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namespace paddle {
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size_t CMRProjectionNormLayer::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_ = imgSizeY_;
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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_ = imgSizeH_;
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outputW_ = imgSizeW_;
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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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bool CMRProjectionNormLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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/* Initialize the basic parent class */
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ResponseNormLayer::init(layerMap, parameterMap);
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/* the size of inputs for norm-layer is 1 */
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CHECK_EQ(config_.inputs_size(), 1);
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createFunction(
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forward_,
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"CrossMapNormal",
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FuncConfig().set("size", size_).set("scale", scale_).set("pow", pow_));
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createFunction(
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backward_,
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"CrossMapNormalGrad",
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FuncConfig().set("size", size_).set("scale", scale_).set("pow", pow_));
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return true;
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}
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void CMRProjectionNormLayer::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 = inputLayers_[0]->getOutputValue();
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size_t batchSize = input->getHeight();
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int size = getSize();
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resetOutput(batchSize, size);
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Matrix::resizeOrCreate(denoms_, batchSize, size, /* trans */ false, useGpu_);
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shape_ = TensorShape({batchSize, channels_, imgSizeH_, imgSizeW_});
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// prepare forward arguments
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BufferArgs inputs;
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BufferArgs outputs;
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inputs.addArg(*getInputValue(0), shape_);
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outputs.addArg(*getOutputValue(), shape_, ASSIGN_TO);
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outputs.addArg(*denoms_, shape_, ASSIGN_TO);
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forward_[0]->calc(inputs, outputs);
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}
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void CMRProjectionNormLayer::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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// prepare backward arguments
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BufferArgs inputs;
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BufferArgs outputs;
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inputs.addArg(*getInputValue(0), shape_);
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inputs.addArg(*getOutputValue(), shape_);
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inputs.addArg(*getOutputGrad(), shape_);
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inputs.addArg(*denoms_, shape_);
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outputs.addArg(*getInputGrad(0), shape_, ADD_TO);
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backward_[0]->calc(inputs, outputs);
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}
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} // namespace paddle
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