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116 lines
3.7 KiB
116 lines
3.7 KiB
/* 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 "PadLayer.h"
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#include "paddle/utils/Stat.h"
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namespace paddle {
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REGISTER_LAYER(pad, PadLayer);
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bool PadLayer::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& pad_conf = config_.inputs(0).pad_conf();
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auto& img_conf = pad_conf.image_conf();
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CHECK_EQ(config_.inputs_size(), 1);
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inDims_ = TensorShape(
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{0,
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img_conf.channels(),
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img_conf.has_img_size_y() ? img_conf.img_size_y() : img_conf.img_size(),
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img_conf.img_size()});
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CHECK_EQ(2, pad_conf.pad_c_size());
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CHECK_EQ(2, pad_conf.pad_h_size());
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CHECK_EQ(2, pad_conf.pad_w_size());
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padc_.push_back(pad_conf.pad_c(0));
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padc_.push_back(pad_conf.pad_c(1));
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padh_.push_back(pad_conf.pad_h(0));
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padh_.push_back(pad_conf.pad_h(1));
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padw_.push_back(pad_conf.pad_w(0));
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padw_.push_back(pad_conf.pad_w(1));
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outDims_ = TensorShape(4);
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setOutDims(0);
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createFunction(forward_,
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"Pad",
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FuncConfig()
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.set("cstart", padc_[0])
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.set("cend", padc_[1])
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.set("hstart", padh_[0])
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.set("hend", padh_[1])
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.set("wstart", padw_[0])
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.set("wend", padw_[1]));
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createFunction(backward_,
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"PadGrad",
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FuncConfig()
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.set("cstart", padc_[0])
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.set("cend", padc_[1])
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.set("hstart", padh_[0])
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.set("hend", padh_[1])
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.set("wstart", padw_[0])
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.set("wend", padw_[1]));
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return true;
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}
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void PadLayer::setOutDims(const size_t batchSize) {
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outDims_.reshape({batchSize,
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inDims_[1] + padc_[0] + padc_[1],
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inDims_[2] + padh_[0] + padh_[1],
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inDims_[3] + padw_[0] + padw_[1]});
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}
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void PadLayer::setTensorDim(const size_t batchSize) {
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CHECK_EQ(static_cast<int>(inputLayers_.size()), 1);
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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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setOutDims(batchSize);
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}
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void PadLayer::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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setTensorDim(batchSize);
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int size = outDims_[1] * outDims_[2] * outDims_[3];
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resetOutput(batchSize, size);
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MatrixPtr outV = getOutputValue();
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REGISTER_TIMER_INFO("PadForward", getName().c_str());
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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_, ASSIGN_TO);
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forward_[0]->calc(inputs, outputs);
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}
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void PadLayer::backward(const UpdateCallback& callback) {
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(void)callback;
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REGISTER_TIMER_INFO("PadBackward", getName().c_str());
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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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backward_[0]->calc(inputs, outputs);
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}
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} // namespace paddle
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