Zhaolong Xing
7 years ago
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GitHub
commit
d02a68c447
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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 "UpsampleLayer.h"
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#include "iostream"
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namespace paddle {
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REGISTER_LAYER(upsample, UpsampleLayer);
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size_t UpsampleLayer::getOutputSize() {
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if (upsampleSize_ == 0) {
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upsampleSize_ = imgSize_ * scale_ - static_cast<int>(padOutX_);
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upsampleSizeY_ = imgSizeY_ * scaleY_ - static_cast<int>(padOutY_);
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}
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return upsampleSize_ * upsampleSizeY_ * channels_;
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}
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bool UpsampleLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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Layer::init(layerMap, parameterMap);
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CHECK_EQ(inputLayers_.size(), 2U);
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CHECK_EQ(config_.inputs_size(), 2);
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const auto& conf = config_.inputs(0).upsample_conf();
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const auto& img_conf = conf.image_conf();
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imgSizeY_ =
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img_conf.has_img_size_y() ? img_conf.img_size_y() : img_conf.img_size();
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imgSize_ = img_conf.img_size();
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channels_ = img_conf.channels();
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CHECK((conf.has_upsample_size()) || (conf.has_scale()))
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<< "scale or upsample_size is required.";
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if (conf.has_upsample_size()) {
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upsampleSize_ = conf.upsample_size();
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upsampleSizeY_ = upsampleSize_;
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if (conf.has_upsample_size_y()) {
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upsampleSizeY_ = conf.upsample_size_y();
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}
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} else {
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if (!conf.has_scale_y()) {
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scale_ = scaleY_ = conf.scale_y();
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CHECK_GT(static_cast<int>(scale_), 1);
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} else {
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scale_ = conf.scale();
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scaleY_ = conf.scale_y();
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}
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padOutX_ = conf.pad_out_x();
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padOutY_ = conf.pad_out_y();
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CHECK(!padOutX_ || scale_ == 2)
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<< "Output height padding compensation requires scale_ == 2";
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CHECK(!padOutY_ || scaleY_ == 2)
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<< "Output width padding compensation requires scaleY_ == 2";
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upsampleSize_ = upsampleSizeY_ = 0;
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}
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return true;
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}
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void UpsampleLayer::forward(PassType passType) {
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Layer::forward(passType);
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MatrixPtr input = getInputValue(0);
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MatrixPtr mask = inputLayers_[1]->getOutput("mask").value;
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size_t batchSize = input->getHeight();
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size_t outSize = getOutputSize();
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CHECK_EQ(input->getWidth(), mask->getWidth());
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CHECK_EQ(mask->getHeight(), batchSize);
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resetOutput(batchSize, outSize);
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MatrixPtr output = getOutputValue();
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output->upsampleForward(*input,
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*mask,
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imgSize_,
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imgSizeY_,
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channels_,
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upsampleSize_,
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upsampleSizeY_);
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}
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void UpsampleLayer::backward(const UpdateCallback& callback) {
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MatrixPtr mask = inputLayers_[1]->getOutput("mask").value;
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MatrixPtr inputGrad = getInputGrad(0);
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MatrixPtr outputGrad = getOutputGrad();
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inputGrad->upsampleBackward(*outputGrad,
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*mask,
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imgSize_,
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imgSizeY_,
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channels_,
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upsampleSize_,
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upsampleSizeY_);
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}
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} // namespace paddle
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@ -0,0 +1,53 @@
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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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#pragma once
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#include <vector>
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#include "Layer.h"
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#include "paddle/math/Matrix.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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/**
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* This layer transpose the pooling process.
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* It takes two input, the first input is the input data, and
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* the second is the mask data from the max-pool-with-mask layer.
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*
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*/
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class UpsampleLayer : public Layer {
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public:
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explicit UpsampleLayer(const LayerConfig& config) : Layer(config) {}
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~UpsampleLayer() {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback) override;
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size_t getOutputSize();
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protected:
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size_t scale_, scaleY_;
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size_t upsampleSize_, upsampleSizeY_;
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size_t padOutX_, padOutY_;
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size_t imgSize_, imgSizeY_;
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size_t channels_;
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};
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} // namespace paddle
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@ -0,0 +1,152 @@
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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 <gtest/gtest.h>
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#include <string>
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#include <vector>
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#include "LayerGradUtil.h"
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#include "paddle/math/MathUtils.h"
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#include "paddle/testing/TestUtil.h"
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using namespace paddle;
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void setPoolConfig(TestConfig* config,
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PoolConfig* pool,
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const string& poolType) {
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(*config).biasSize = 0;
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(*config).layerConfig.set_type("pool");
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(*config).layerConfig.set_num_filters(1);
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int kw = 2, kh = 2;
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int pw = 0, ph = 0;
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int sw = 2, sh = 2;
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pool->set_pool_type(poolType);
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pool->set_channels(2);
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pool->set_size_x(kw);
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pool->set_size_y(kh);
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pool->set_start(0);
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pool->set_padding(pw);
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pool->set_padding_y(ph);
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pool->set_stride(sw);
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pool->set_stride_y(sh);
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int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false);
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int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false);
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pool->set_output_x(ow);
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pool->set_output_y(oh);
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}
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LayerPtr doOneUpsampleTest(MatrixPtr& inputMat,
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const string& poolType,
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bool use_gpu,
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real* tempGradData) {
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/* prepare maxPoolWithMaskLayer */
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TestConfig config;
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config.inputDefs.push_back({INPUT_DATA, "layer_0", 128, 0});
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LayerInputConfig* input = config.layerConfig.add_inputs();
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PoolConfig* pool = input->mutable_pool_conf();
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pool->set_img_size(8);
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pool->set_img_size_y(8);
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setPoolConfig(&config, pool, "max-pool-with-mask");
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config.layerConfig.set_size(pool->output_x() * pool->output_y() *
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pool->channels());
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config.layerConfig.set_name("MaxPoolWithMask");
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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(config,
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&dataLayers,
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&datas,
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&layerMap,
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"MaxPoolWithMask",
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1,
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false,
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use_gpu);
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dataLayers[0]->getOutputValue()->copyFrom(*inputMat);
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FLAGS_use_gpu = use_gpu;
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std::vector<ParameterPtr> parameters;
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LayerPtr maxPoolingWithMaskOutputLayer;
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initTestLayer(config, &layerMap, ¶meters, &maxPoolingWithMaskOutputLayer);
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maxPoolingWithMaskOutputLayer->forward(PASS_GC);
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/* prepare the upsample layer */
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LayerConfig upsampleLayerConfig;
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upsampleLayerConfig.set_type("upsample");
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LayerInputConfig* input1 = upsampleLayerConfig.add_inputs();
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upsampleLayerConfig.add_inputs();
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UpsampleConfig* upsampleConfig = input1->mutable_upsample_conf();
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upsampleConfig->set_scale(2);
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ImageConfig* imageConfig = upsampleConfig->mutable_image_conf();
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imageConfig->set_channels(2);
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imageConfig->set_img_size(4);
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imageConfig->set_img_size_y(4);
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upsampleLayerConfig.set_size(2 * 8 * 8);
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upsampleLayerConfig.set_name("upsample");
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for (size_t i = 0; i < 2; i++) {
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LayerInputConfig& inputTemp = *(upsampleLayerConfig.mutable_inputs(i));
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inputTemp.set_input_layer_name("MaxPoolWithMask");
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}
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LayerPtr upsampleLayer;
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ParameterMap parameterMap;
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upsampleLayer = Layer::create(upsampleLayerConfig);
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layerMap[upsampleLayerConfig.name()] = upsampleLayer;
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upsampleLayer->init(layerMap, parameterMap);
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upsampleLayer->setNeedGradient(true);
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upsampleLayer->forward(PASS_GC);
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upsampleLayer->getOutputGrad()->copyFrom(tempGradData, 128);
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upsampleLayer->backward();
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return upsampleLayer;
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}
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TEST(Layer, maxPoolingWithMaskOutputLayerFwd) {
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bool useGpu = false;
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MatrixPtr inputMat;
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MatrixPtr inputGPUMat;
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MatrixPtr tempGradMat;
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inputMat = Matrix::create(1, 128, false, useGpu);
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inputMat->randomizeUniform();
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tempGradMat = Matrix::create(1, 128, false, useGpu);
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tempGradMat->randomizeUniform();
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real* data = inputMat->getData();
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real* tempGradData = tempGradMat->getData();
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LayerPtr upsampleLayerCPU =
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doOneUpsampleTest(inputMat, "max-pool-with-mask", useGpu, tempGradData);
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#ifdef PADDLE_WITH_CUDA
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useGpu = true;
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inputGPUMat = Matrix::create(1, 128, false, useGpu);
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inputGPUMat->copyFrom(data, 128);
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LayerPtr upsampleLayerGPU = doOneUpsampleTest(
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inputGPUMat, "max-pool-with-mask", useGpu, tempGradData);
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checkMatrixEqual(upsampleLayerCPU->getOutput("").value,
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upsampleLayerGPU->getOutput("").value);
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checkMatrixEqual(upsampleLayerCPU->getPrev(0)->getOutputGrad(),
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upsampleLayerGPU->getPrev(0)->getOutputGrad());
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#endif
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}
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