2. Make SwitchOrderLayer support for softmax activation 3. Fix bugsAdaptive_data_structure_for_SwitchOrderLayer
wanghaoshuang
8 years ago
parent
475dd708b9
commit
1cdf149b6f
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/* Copyright (c) 2016 PaddlePaddle Authors. 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 "PixelSoftmaxLayer.h"
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#include "paddle/utils/Stat.h"
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namespace paddle {
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REGISTER_LAYER(pixel_softmax, PixelSoftmaxLayer);
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bool PixelSoftmaxLayer::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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auto& img_conf = config_.inputs(0).image_conf();
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inH_ =
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img_conf.has_img_size_y() ? img_conf.img_size_y() : img_conf.img_size();
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inW_ = img_conf.img_size();
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inC_ = img_conf.channels();
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createFunction(forward_, "NCHW2NHWC", FuncConfig());
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createFunction(backward_, "NHWC2NCHW", FuncConfig());
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inDims_ = TensorShape({0, inH_, inW_, inC_});
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outDims_ = TensorShape({0, inC_, inH_, inW_});
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return true;
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}
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void PixelSoftmaxLayer::forward(PassType passType) {
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Layer::forward(passType);
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MatrixPtr input = inputLayers_[0]->getOutputValue();
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size_t batchSize = input->getHeight();
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// cout<<"useGpu:"<<useGpu(deviceId_)<<endl;
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Matrix::resizeOrCreate(
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tmpInput_, batchSize * inH_ * inW_, inC_, false, useGpu_);
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Matrix::resizeOrCreate(
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tmpOutput_, batchSize * inH_ * inW_, inC_, false, useGpu_);
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tmpOutput_->zeroMem();
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resetOutput(batchSize, inH_ * inW_ * inC_);
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inDims_.setDim(0, batchSize);
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outDims_.setDim(0, batchSize);
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// switch NCHW to NHWC
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BufferArgs inputs;
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BufferArgs outputs;
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inputs.addArg(*getInputValue(0), inDims_);
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outputs.addArg(*tmpInput_, outDims_);
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forward_[0]->calc(inputs, outputs);
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// softmax forward and save softmax result into tmpMatrix_
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tmpInput_->softmax(*tmpOutput_);
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// switch NHWC to NCHW
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BufferArgs inputs_1;
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BufferArgs outputs_1;
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inputs_1.addArg(*tmpOutput_, outDims_);
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outputs_1.addArg(*getOutputValue(), inDims_);
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backward_[0]->calc(inputs_1, outputs_1);
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}
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void PixelSoftmaxLayer::backward(const UpdateCallback& callback) {
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(void)callback;
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REGISTER_TIMER_INFO("PixelSoftmaxBackward", getName().c_str());
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// switch NCHW to NHWC
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BufferArgs inputs;
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BufferArgs outputs;
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inputs.addArg(*getOutputGrad(), inDims_);
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outputs.addArg(*tmpInput_, outDims_);
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forward_[0]->calc(inputs, outputs);
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// softmax backward and save grad result into tmpOutput_
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tmpInput_->softmaxBackward(*tmpOutput_);
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// switch NHWC to NCHW
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BufferArgs inputs_1;
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BufferArgs outputs_1;
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inputs_1.addArg(*tmpInput_, outDims_);
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outputs_1.addArg(*getInputGrad(0), inDims_);
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backward_[0]->calc(inputs_1, outputs_1);
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}
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} // namespace paddle
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@ -0,0 +1,112 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. 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 "SwitchOrderLayer.h"
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#include "paddle/utils/Stat.h"
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namespace paddle {
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REGISTER_LAYER(switch_order, SwitchOrderLayer);
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bool SwitchOrderLayer::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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auto& img_conf = config_.inputs(0).image_conf();
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size_t inH =
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img_conf.has_img_size_y() ? img_conf.img_size_y() : img_conf.img_size();
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size_t inW = img_conf.img_size();
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size_t inC = img_conf.channels();
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inDims_ = TensorShape({0, inC, inH, inW});
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outDims_ = TensorShape(4);
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auto& reshape_conf = config_.reshape_conf();
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for (size_t i = 0; i < reshape_conf.heightaxis_size(); i++) {
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LOG(INFO) << "reshape height axis: " << reshape_conf.heightaxis(i);
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heightAxis_.push_back(reshape_conf.heightaxis(i));
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}
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for (size_t i = 0; i < reshape_conf.widthaxis_size(); i++) {
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LOG(INFO) << "reshape width axis: " << reshape_conf.widthaxis(i);
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widthAxis_.push_back(reshape_conf.widthaxis(i));
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}
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createFunction(nchw2nhwc_, "NCHW2NHWC", FuncConfig());
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createFunction(nhwc2nchw_, "NHWC2NCHW", FuncConfig());
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return true;
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}
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void SwitchOrderLayer::setOutDims() {
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outDims_.setDim(0, inDims_[0]);
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outDims_.setDim(1, inDims_[2]);
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outDims_.setDim(2, inDims_[3]);
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outDims_.setDim(3, inDims_[1]);
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reshapeHeight_ = 1;
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for (size_t i = 0; i < heightAxis_.size(); i++) {
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reshapeHeight_ *= outDims_[heightAxis_[i]];
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}
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output_.setFrameHeight(reshapeHeight_);
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reshapeWidth_ = 1;
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for (size_t i = 0; i < widthAxis_.size(); i++) {
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reshapeWidth_ *= outDims_[widthAxis_[i]];
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}
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output_.setFrameWidth(reshapeWidth_);
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LOG(INFO) << "outDims: " << outDims_[0] << "; " << outDims_[1] << ";"
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<< outDims_[2] << ";" << outDims_[3];
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}
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void SwitchOrderLayer::setInDims() {
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MatrixPtr input = inputLayers_[0]->getOutputValue();
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size_t batchSize = input->getHeight();
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inDims_.setDim(0, batchSize);
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int h = inputLayers_[0]->getOutput().getFrameHeight();
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if (h != 0) inDims_.setDim(2, h);
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int w = inputLayers_[0]->getOutput().getFrameWidth();
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if (w != 0) inDims_.setDim(3, w);
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int totalCount = input->getElementCnt();
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int channels = totalCount / (inDims_[0] * inDims_[2] * inDims_[3]);
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if (channels != 0) inDims_.setDim(1, channels);
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LOG(INFO) << "inDims: " << inDims_[0] << "; " << inDims_[1] << ";"
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<< inDims_[2] << ";" << inDims_[3];
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}
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void SwitchOrderLayer::forward(PassType passType) {
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Layer::forward(passType);
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setInDims();
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setOutDims();
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resetOutput(outDims_[0], outDims_[1] * outDims_[2] * outDims_[3]);
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if (heightAxis_.size() > 0) {
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getOutputValue()->reshape(reshapeHeight_, reshapeWidth_);
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}
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// switch NCHW to NHWC
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BufferArgs inputs;
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BufferArgs outputs;
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inputs.addArg(*getInputValue(0), inDims_);
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outputs.addArg(*getOutputValue(), outDims_);
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nchw2nhwc_[0]->calc(inputs, outputs);
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// forwardActivation();
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}
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void SwitchOrderLayer::backward(const UpdateCallback& callback) {
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(void)callback;
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// backwardActivation();
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// switch NHWC to NCHW
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BufferArgs inputs;
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BufferArgs outputs;
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inputs.addArg(*getOutputGrad(), outDims_);
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outputs.addArg(*getInputGrad(0), inDims_, ADD_TO);
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nhwc2nchw_[0]->calc(inputs, outputs);
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}
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} // namespace paddle
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