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126 lines
4.6 KiB
126 lines
4.6 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 "ConvTransOperator.h"
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#include "paddle/math/MathUtils.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 ConvTransOperator takes two inputs to perform the convolution.
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* The first input is the image, and the second input is the convolution kernel.
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* The height of data for two inputs are the same. Each data of the first input
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* is convolved with each data of the second input indepedently.
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*
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* The config file api is conv_operator.
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*/
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REGISTER_OPERATOR(convt, ConvTransOperator);
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void ConvTransOperator::reshape(int batchSize) {
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outputH_ = ins_[0]->getFrameHeight();
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outputW_ = ins_[0]->getFrameWidth();
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if (outputH_ == 0) outputH_ = outputY_;
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if (outputW_ == 0) outputW_ = outputX_;
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imageH_ = imageSize(outputH_, filterSizeY_, paddingY_, strideY_, caffeMode_);
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imageW_ = imageSize(outputW_, filterSize_, padding_, stride_, caffeMode_);
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/// Check that the imageSizes are consistent with config
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CHECK_EQ(imageH_, imgSizeY_);
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CHECK_EQ(imageW_, imgSize_);
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out_->setFrameHeight(imageH_);
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out_->setFrameWidth(imageW_);
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reshapeImageDescriptors();
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inputOffset_ = numFilters_ * outputH_ * outputW_;
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outputOffset_ = channels_ * imageH_ * imageW_;
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weightOffset_ = numFilters_ * channels_ * filterSize_ * filterSizeY_;
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if (!isSelectAlgo_) {
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allocConvWorkSpace();
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}
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isSelectAlgo_ = true;
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}
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void ConvTransOperator::forward() {
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size_t batchSize = ins_[0]->value->getHeight();
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reshape(batchSize);
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CHECK_EQ(ins_[1]->value->getHeight(), batchSize);
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checkFilterSize(ins_[1]->value);
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Matrix::resizeOrCreate(
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out_->value, batchSize, imageH_ * imageW_ * channels_, false, useGpu_);
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{
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AsyncGpuBlock block;
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for (size_t batchId = 0; batchId < batchSize; ++batchId) {
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real *inputData = ins_[0]->value->getData() + inputOffset_ * batchId;
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real *wgtData = ins_[1]->value->getData() + weightOffset_ * batchId;
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real *outData = out_->value->getData() + outputOffset_ * batchId;
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hl_convolution_backward_data(imageDesc_,
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outData,
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outputDesc_,
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inputData,
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filterDesc_,
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wgtData,
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convDesc_,
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workSpace_,
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workSpaceInBytes_,
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bwdDataAlgo_);
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}
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}
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}
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void ConvTransOperator::backward() {
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size_t batchSize = ins_[0]->value->getHeight();
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{
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AsyncGpuBlock block;
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for (size_t batchId = 0; batchId < batchSize; ++batchId) {
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real *outGrad = out_->grad->getData() + outputOffset_ * batchId;
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if (ins_[1]->grad) {
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real *inputData = ins_[0]->value->getData() + inputOffset_ * batchId;
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real *weightGrad = ins_[1]->grad->getData() + weightOffset_ * batchId;
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hl_convolution_backward_filter(imageDesc_,
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outGrad,
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outputDesc_,
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inputData,
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filterDesc_,
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weightGrad,
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convDesc_,
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workSpace_,
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workSpaceInBytes_,
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bwdFilterAlgo_);
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}
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MatrixPtr preGrad = ins_[0]->grad;
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if (NULL != preGrad) {
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real *inputGrad = preGrad->getData() + inputOffset_ * batchId;
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real *wgtData = ins_[1]->value->getData() + weightOffset_ * batchId;
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hl_convolution_forward(imageDesc_,
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outGrad,
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outputDesc_,
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inputGrad,
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filterDesc_,
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wgtData,
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convDesc_,
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workSpace_,
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workSpaceInBytes_,
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fwdAlgo_);
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}
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}
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}
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}
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} // namespace paddle
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