qijun
9 years ago
commit
9dd588b414
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/* 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 "BilinearInterpLayer.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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REGISTER_LAYER(bilinear_interp, BilinearInterpLayer);
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size_t BilinearInterpLayer::getSize() {
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inImgH_ = inputLayers_[0]->getOutput().getFrameHeight();
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inImgW_ = inputLayers_[0]->getOutput().getFrameWidth();
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const BilinearInterpConfig& conf = config_.inputs(0).bilinear_interp_conf();
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if (inImgH_ == 0) {
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inImgH_ = conf.img_size_y();
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}
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if (inImgW_ == 0) {
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inImgW_ = conf.img_size_x();
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}
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outImgH_ = conf.out_size_y();
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outImgW_ = conf.out_size_x();
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numChannels_ = conf.num_channels();
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CHECK(outImgH_ > 0 && outImgW_ > 0);
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CHECK(inImgH_ > 0 && inImgW_ > 0);
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CHECK(numChannels_);
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ratioH_ = (outImgH_ > 1) ?
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static_cast<real>(inImgH_ - 1) / (outImgH_ - 1) : 0.f;
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ratioW_ = (outImgW_ > 1) ?
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static_cast<real>(inImgW_ - 1) / (outImgW_ - 1) : 0.f;
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getOutput().setFrameHeight(outImgH_);
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getOutput().setFrameWidth(outImgW_);
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return outImgH_ * outImgW_ * numChannels_;
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}
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bool BilinearInterpLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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/* Initialize the basic parent class */
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Layer::init(layerMap, parameterMap);
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CHECK_EQ(1, config_.inputs_size());
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return true;
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}
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void BilinearInterpLayer::forward(PassType passType) {
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Layer::forward(passType);
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size_t batchSize = getInput(0).getBatchSize();
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size_t size = getSize();
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{
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REGISTER_TIMER_INFO("FwResetTimer", getName().c_str());
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resetOutput(batchSize, size);
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}
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MatrixPtr inV = getInputValue(0);
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MatrixPtr outV = getOutputValue();
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{
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REGISTER_TIMER_INFO("FwBilinearInterpTimer", getName().c_str());
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outV->bilinearForward(*inV, inImgH_, inImgW_, outImgH_, outImgW_,
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numChannels_, ratioH_, ratioW_);
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}
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}
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void BilinearInterpLayer::backward(const UpdateCallback& callback) {
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(void) callback;
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MatrixPtr inputG = getInputGrad(0);
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MatrixPtr outG = getOutputGrad();
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{
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REGISTER_TIMER_INFO("BwBilinearInterpTimer", getName().c_str());
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if (inputG) {
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inputG->bilinearBackward(*outG, outImgH_, outImgW_, inImgH_, inImgW_,
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numChannels_, ratioH_, ratioW_);
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}
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}
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}
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} // namespace paddle
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@ -0,0 +1,46 @@
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/* 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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#pragma once
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#include "Layer.h"
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#include "paddle/math/Matrix.h"
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namespace paddle {
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/**
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* @brief A layer for bilinear interpolation which is
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* used on conv layer output.
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*
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* @note The config file api is bilinear_interp_layer.
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*/
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class BilinearInterpLayer : public Layer {
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protected:
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size_t outImgH_, outImgW_;
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size_t inImgH_, inImgW_;
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real ratioH_, ratioW_;
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size_t numChannels_;
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public:
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explicit BilinearInterpLayer(const LayerConfig& config) : Layer(config) {}
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virtual ~BilinearInterpLayer() {}
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size_t getSize();
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bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
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void forward(PassType passType);
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void backward(const UpdateCallback& callback = nullptr);
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};
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} // namespace paddle
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/* 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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#pragma once
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#include "ConvBaseLayer.h"
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#include "paddle/math/Matrix.h"
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#include <vector>
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namespace paddle {
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/**
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* @brief A subclass of ConvBaseLayer that is a superclass of both
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* ExpandConvLayer and ExpandConvTransLayer
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*/
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class ExpandConvBaseLayer : public ConvBaseLayer {
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protected:
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/// For expand convolution.
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/// subM_ = numFilters_ / groups_.
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IntV subM_;
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/// subN_ = outputH_ * outputW_.
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IntV subN_;
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/// subK_ = channels_ * filterPixels_ * groups_.
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IntV subK_;
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/*The expandInput_ and transOutValue_ are used for CPU expand conv calc
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* Expand one sample at a time. shape:
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* (numChannels * filterPixels_, outputSizeH * outputSizeW)
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* */
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MatrixPtr expandInput_;
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/// The transpose of output, which is an auxiliary matrix.
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MatrixPtr transOutValue_;
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public:
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explicit ExpandConvBaseLayer(const LayerConfig& config)
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: ConvBaseLayer(config) {}
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~ExpandConvBaseLayer() {}
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bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
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size_t getOutputSize();
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/**
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* Create or resize expandInput_.
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*/
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void resetExpandInput(size_t height, size_t width);
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/**
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* Add shared bias.
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*/
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void addSharedBias();
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/**
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* Add unshared bias.
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*/
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void addUnsharedBias();
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/**
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* Expand one input sample.
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*/
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void expandOneFrame(MatrixPtr image, size_t startIdx, int inIdx);
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/**
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* Expand one input sample and perform matrix multiplication.
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*/
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void expandFwdOnce(MatrixPtr image, MatrixPtr out, int inIdx, int startIdx);
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void bpropSharedBias(MatrixPtr biases, MatrixPtr v);
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void bpropBiases(MatrixPtr v);
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void bpropWeights(MatrixPtr image, MatrixPtr out, int inpIdx);
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void bpropActs(MatrixPtr image, MatrixPtr out, int inpIdx);
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};
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} // namespace paddle
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/* 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 "ExpandConvTransLayer.h"
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/* The implementation of the convTransLayer is basically a swap of forward and
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* backward of the original convLayer.
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* The variable naming follows the convention of the convLayer.
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* */
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namespace paddle {
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REGISTER_LAYER(exconvt, ExpandConvTransLayer);
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bool ExpandConvTransLayer::init(const LayerMap &layerMap,
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const ParameterMap ¶meterMap) {
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/* Initialize the basic convolutional parent class */
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ExpandConvBaseLayer::init(layerMap, parameterMap);
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return true;
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}
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void ExpandConvTransLayer::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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int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
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resetOutput(batchSize, getOutputSize());
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MatrixPtr output = nullptr;
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for (size_t i = 0; i < inputLayers_.size(); ++i) {
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LayerPtr prevLayer = getPrev(i);
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output = prevLayer->getOutputValue();
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REGISTER_TIMER_INFO("shrinkFwd", getName().c_str());
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bpropActs(output, getOutputValue(), i);
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}
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/* add the bias-vector */
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if (biases_.get()) {
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if (sharedBiases_) {
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addSharedBias();
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} else {
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addUnsharedBias();
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}
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}
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/* activation */
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forwardActivation();
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}
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void ExpandConvTransLayer::backward(const UpdateCallback &callback) {
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backwardActivation();
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MatrixPtr imageGrad = getOutputGrad();
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if (biases_ && biases_->getWGrad()) {
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bpropBiases(imageGrad);
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/* Increasing the number of gradient */
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biases_->getParameterPtr()->incUpdate(callback);
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}
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for (size_t i = 0; i < inputLayers_.size(); ++i) {
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/* First, calculate the input layers error */
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for (size_t off = 0; off < imageGrad->getHeight(); off++) {
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if (getPrev(i)->getOutputGrad()) {
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expandFwdOnce(imageGrad, getPrev(i)->getOutputGrad(), i, off);
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}
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}
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if (weights_[i]->getWGrad()) {
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/* Then, calculate the W-gradient for the current layer */
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bpropWeights(imageGrad, getPrev(i)->getOutputValue(), i);
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/* Increasing the number of gradient */
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weights_[i]->getParameterPtr()->incUpdate(callback);
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}
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}
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}
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} // namespace paddle
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@ -0,0 +1,44 @@
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/* 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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|
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http://www.apache.org/licenses/LICENSE-2.0
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|
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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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#pragma once
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#include "paddle/math/Matrix.h"
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#include <vector>
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#include "ExpandConvBaseLayer.h"
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namespace paddle {
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/**
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* @brief A subclass of convolution layer.
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* This layer expands input and use matrix multiplication to
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* calculate convolution transpose (deconv) operation.
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*
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* The config file api is img_conv_layer with flag trans=True.
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*/
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class ExpandConvTransLayer : public ExpandConvBaseLayer {
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public:
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explicit ExpandConvTransLayer(const LayerConfig& config) :
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ExpandConvBaseLayer(config) {}
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~ExpandConvTransLayer() {}
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bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
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void forward(PassType passType);
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void backward(const UpdateCallback& callback);
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};
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} // namespace paddle
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@ -0,0 +1,246 @@
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/* 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
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
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|
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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,
|
||||
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 <gtest/gtest.h>
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#include <vector>
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#include <string>
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#include "paddle/gserver/layers/DataLayer.h"
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#include "ModelConfig.pb.h"
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#include "paddle/trainer/Trainer.h"
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#include "paddle/utils/GlobalConstants.h"
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#include "paddle/gserver/layers/ExpandConvTransLayer.h"
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#include "paddle/math/MathUtils.h"
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#include "TestUtil.h"
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#include "LayerGradUtil.h"
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using namespace paddle; // NOLINT
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using namespace std; // NOLINT
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P_DECLARE_bool(use_gpu);
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P_DECLARE_int32(gpu_id);
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P_DECLARE_double(checkgrad_eps);
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P_DECLARE_bool(thread_local_rand_use_global_seed);
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P_DECLARE_bool(prev_batch_state);
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// Test that the convTrans forward is the same as conv backward
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TEST(Layer, convTransLayerFwd) {
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// Setting up conv-trans layer
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TestConfig configt;
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configt.biasSize = 3;
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configt.layerConfig.set_type("exconvt");
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configt.layerConfig.set_num_filters(3);
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configt.layerConfig.set_partial_sum(1);
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configt.layerConfig.set_shared_biases(true);
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configt.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 384});
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LayerInputConfig* input = configt.layerConfig.add_inputs();
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ConvConfig* conv = input->mutable_conv_conf();
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conv->set_filter_size(2);
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conv->set_filter_size_y(4);
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conv->set_channels(16);
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conv->set_padding(0);
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conv->set_padding_y(1);
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conv->set_stride(2);
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conv->set_stride_y(2);
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conv->set_groups(1);
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conv->set_filter_channels(3 / conv->groups());
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conv->set_img_size(16);
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conv->set_output_x(outputSize(conv->img_size(), conv->filter_size(),
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conv->padding(), conv->stride(),
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/* caffeMode */ true));
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configt.layerConfig.set_size(conv->img_size() * conv->img_size() *
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configt.layerConfig.num_filters());
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configt.layerConfig.set_name("convTrans");
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// data layer initialize
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std::vector<DataLayerPtr> dataLayers;
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LayerMap layerMap;
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vector<Argument> datas;
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initDataLayer(configt, &dataLayers, &datas, &layerMap, "convTrans",
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100, false, false);
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// test layer initialize
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std::vector<ParameterPtr> parameters;
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LayerPtr convtLayer;
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initTestLayer(configt, &layerMap, ¶meters, &convtLayer);
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convtLayer->getBiasParameter()->zeroMem();
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convtLayer->forward(PASS_GC);
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// Setting up conv-layer config
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TestConfig config;
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config.biasSize = 16;
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config.layerConfig.set_type("exconv");
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config.layerConfig.set_num_filters(16);
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config.layerConfig.set_partial_sum(1);
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config.layerConfig.set_shared_biases(true);
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config.inputDefs.push_back({INPUT_DATA, "layer_1", 768, 384});
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input = config.layerConfig.add_inputs();
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conv = input->mutable_conv_conf();
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conv->set_filter_size(2);
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conv->set_filter_size_y(4);
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conv->set_channels(3);
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conv->set_padding(0);
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conv->set_padding_y(1);
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conv->set_stride(2);
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conv->set_stride_y(2);
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conv->set_groups(1);
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conv->set_filter_channels(conv->channels() / conv->groups());
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conv->set_img_size(16);
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conv->set_output_x(outputSize(conv->img_size(), conv->filter_size(),
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conv->padding(), conv->stride(),
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/* caffeMode */ true));
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config.layerConfig.set_size(conv->output_x() * conv->output_x() *
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config.layerConfig.num_filters());
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config.layerConfig.set_name("conv");
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// data layer initialize
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std::vector<DataLayerPtr> dataLayers2;
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LayerMap layerMap2;
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vector<Argument> datas2;
|
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initDataLayer(config, &dataLayers2, &datas2, &layerMap2, "conv",
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100, false, false);
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// test layer initialize
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std::vector<ParameterPtr> parameters2;
|
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LayerPtr convLayer;
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initTestLayer(config, &layerMap2, ¶meters2, &convLayer);
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// Sync convLayer and convtLayer parameter
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convLayer->getBiasParameter()->zeroMem();
|
||||
convLayer->getParameters()[0]->getBuf(PARAMETER_VALUE)->copyFrom(
|
||||
*(convtLayer->getParameters()[0]->getBuf(PARAMETER_VALUE)));
|
||||
|
||||
// Set convLayer outputGrad as convTransLayer input value
|
||||
convLayer->forward(PASS_GC);
|
||||
convLayer->getOutput().grad->copyFrom(*(dataLayers[0]->getOutputValue()));
|
||||
|
||||
vector<int> callbackFlags(parameters2.size(), 0);
|
||||
auto callback = [&](Parameter* para) { ++callbackFlags[para->getID()]; };
|
||||
convLayer->backward(callback);
|
||||
|
||||
// Check that the convLayer backward is the same as convTransLayer forward
|
||||
checkMatrixEqual(convtLayer->getOutputValue(),
|
||||
dataLayers2[0]->getOutputGrad());
|
||||
}
|
||||
|
||||
|
||||
// Do one forward pass of convTrans layer and check to see if its output
|
||||
// matches the given result
|
||||
void doOneConvtTest(size_t imgSize, size_t output_x, size_t stride,
|
||||
size_t padding, size_t filter_size, MatrixPtr& result) {
|
||||
TestConfig configt;
|
||||
configt.biasSize = 1;
|
||||
configt.layerConfig.set_type("exconvt");
|
||||
configt.layerConfig.set_num_filters(1);
|
||||
configt.layerConfig.set_partial_sum(1);
|
||||
configt.layerConfig.set_shared_biases(true);
|
||||
|
||||
configt.inputDefs.push_back({INPUT_DATA, "layer_0", output_x * output_x,
|
||||
filter_size * filter_size});
|
||||
LayerInputConfig* input = configt.layerConfig.add_inputs();
|
||||
ConvConfig* conv = input->mutable_conv_conf();
|
||||
conv->set_filter_size(filter_size);
|
||||
conv->set_filter_size_y(filter_size);
|
||||
conv->set_channels(1);
|
||||
conv->set_padding(padding);
|
||||
conv->set_padding_y(padding);
|
||||
conv->set_stride(stride);
|
||||
conv->set_stride_y(stride);
|
||||
conv->set_groups(1);
|
||||
conv->set_filter_channels(1);
|
||||
conv->set_img_size(imgSize);
|
||||
conv->set_output_x(output_x);
|
||||
|
||||
configt.layerConfig.set_size(conv->img_size() * conv->img_size() *
|
||||
configt.layerConfig.num_filters());
|
||||
configt.layerConfig.set_name("convTrans");
|
||||
|
||||
std::vector<DataLayerPtr> dataLayers;
|
||||
LayerMap layerMap;
|
||||
vector<Argument> datas;
|
||||
initDataLayer(configt, &dataLayers, &datas, &layerMap, "convTrans",
|
||||
1, false, false);
|
||||
dataLayers[0]->getOutputValue()->zeroMem();
|
||||
dataLayers[0]->getOutputValue()->add(1.0);
|
||||
|
||||
// test layer initialize
|
||||
std::vector<ParameterPtr> parameters;
|
||||
LayerPtr convtLayer;
|
||||
initTestLayer(configt, &layerMap, ¶meters, &convtLayer);
|
||||
convtLayer->getBiasParameter()->zeroMem();
|
||||
convtLayer->getParameters()[0]->zeroMem();
|
||||
convtLayer->getParameters()[0]->getBuf(PARAMETER_VALUE)->add(1.0);
|
||||
convtLayer->forward(PASS_GC);
|
||||
|
||||
checkMatrixEqual(convtLayer->getOutputValue(), result);
|
||||
}
|
||||
|
||||
TEST(Layer, convTransLayerFwd2) {
|
||||
MatrixPtr result;
|
||||
result = Matrix::create(1, 5 * 5, false, false);
|
||||
result->zeroMem();
|
||||
result->add(1.0);
|
||||
doOneConvtTest(/* imgSize */ 5,
|
||||
/* output_x */ 1,
|
||||
/* stride */ 1,
|
||||
/* padding */ 0,
|
||||
/* filter_size */ 5,
|
||||
result);
|
||||
|
||||
float resultData[] = {1, 2, 2, 2, 1,
|
||||
2, 4, 4, 4, 2,
|
||||
2, 4, 4, 4, 2,
|
||||
2, 4, 4, 4, 2,
|
||||
1, 2, 2, 2, 1};
|
||||
result->setData(resultData);
|
||||
doOneConvtTest(/* imgSize */ 5,
|
||||
/* output_x */ 2,
|
||||
/* stride */ 1,
|
||||
/* padding */ 0,
|
||||
/* filter_size */ 4,
|
||||
result);
|
||||
|
||||
float resultData2[] = {1, 2, 2, 2, 1,
|
||||
2, 4, 4, 4, 2,
|
||||
2, 4, 4, 4, 2,
|
||||
2, 4, 4, 4, 2,
|
||||
1, 2, 2, 2, 1};
|
||||
result->setData(resultData2);
|
||||
doOneConvtTest(/* imgSize */ 5,
|
||||
/* output_x */ 2,
|
||||
/* stride */ 2,
|
||||
/* padding */ 1,
|
||||
/* filter_size */ 5,
|
||||
result);
|
||||
|
||||
float resultData3[] = {1, 1, 2, 1, 1,
|
||||
1, 1, 2, 1, 1,
|
||||
2, 2, 4, 2, 2,
|
||||
1, 1, 2, 1, 1,
|
||||
1, 1, 2, 1, 1};
|
||||
result->setData(resultData3);
|
||||
doOneConvtTest(/* imgSize */ 5,
|
||||
/* output_x */ 2,
|
||||
/* stride */ 2,
|
||||
/* padding */ 0,
|
||||
/* filter_size */ 3,
|
||||
result);}
|
||||
|
||||
int main(int argc, char** argv) {
|
||||
testing::InitGoogleTest(&argc, argv);
|
||||
initMain(argc, argv);
|
||||
FLAGS_thread_local_rand_use_global_seed = true;
|
||||
srand(1);
|
||||
return RUN_ALL_TESTS();
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
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Reference in new issue