-
Notifications
You must be signed in to change notification settings - Fork 5.6k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #269 from wangyang59/deconv
- Loading branch information
Showing
19 changed files
with
1,203 additions
and
338 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -5,4 +5,6 @@ build/ | |
| .vscode | ||
| .idea | ||
| .project | ||
| .cproject | ||
| .pydevproject | ||
| Makefile | ||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,263 @@ | ||
| /* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. */ | ||
|
|
||
|
|
||
| #include "ExpandConvBaseLayer.h" | ||
|
|
||
| #include "paddle/utils/Logging.h" | ||
| namespace paddle { | ||
|
|
||
| bool ExpandConvBaseLayer::init(const LayerMap &layerMap, | ||
| const ParameterMap ¶meterMap) { | ||
| /* Initialize the basic convolutional parent class */ | ||
| ConvBaseLayer::init(layerMap, parameterMap); | ||
|
|
||
| /* The class fields channels_ and numFilters_ are the same as in the config | ||
| * i.e., channels_ is the for the input and numFilters_ is for the output | ||
| * | ||
| * But in order for the variables in convTrans having the same semantic | ||
| * meaning as in conv, we need to swap channels_ and numFilters here for | ||
| * convTrans, and in other functions too. | ||
| * */ | ||
| int channel; | ||
| int numFilters; | ||
| /* Initialize the projection */ | ||
| for (auto &inputConfig : config_.inputs()) { | ||
| const ConvConfig &conf = inputConfig.conv_conf(); | ||
| numFilters = isDeconv_ ? conf.channels() : numFilters_; | ||
| subM_.push_back(numFilters / conf.groups()); | ||
| subN_.push_back(conf.output_x() * conf.output_x()); | ||
| channel = isDeconv_ ? numFilters_ : conf.channels(); | ||
| subK_.push_back(channel * conf.filter_size() * conf.filter_size() / | ||
| conf.groups()); | ||
| /* Consistent caffe mode for multiple input */ | ||
| caffeMode_ = conf.caffe_mode(); | ||
| } | ||
|
|
||
| getOutputSize(); | ||
|
|
||
| return true; | ||
| } | ||
|
|
||
| size_t ExpandConvBaseLayer::getOutputSize() { | ||
| CHECK_NE(inputLayers_.size(), 0UL); | ||
| size_t layerSize = ConvBaseLayer::calOutputSize(); | ||
| subN_.clear(); | ||
| for (size_t i = 0; i < inputLayers_.size(); i++) { | ||
| subN_.push_back(outputH_[i] * outputW_[i]); | ||
| } | ||
| return layerSize; | ||
| } | ||
|
|
||
| void ExpandConvBaseLayer::resetExpandInput(size_t height, size_t width) { | ||
| Matrix::resizeOrCreate(expandInput_, height, width, false, useGpu_); | ||
| } | ||
|
|
||
| void ExpandConvBaseLayer::addSharedBias() { | ||
| size_t mapW = getOutputSize() / numFilters_; | ||
| size_t mapH = getOutputValue()->getElementCnt() / mapW; | ||
| MatrixPtr out = | ||
| Matrix::create(getOutputValue()->getData(), mapH, mapW, false, useGpu_); | ||
|
|
||
| Matrix::resizeOrCreate(transOutValue_, mapW, mapH, false, useGpu_); | ||
|
|
||
| out->transpose(transOutValue_, false); // false means no memory allocation | ||
| transOutValue_->reshape(transOutValue_->getElementCnt() / numFilters_, | ||
| numFilters_); | ||
|
|
||
| MatrixPtr bias = | ||
| Matrix::create(biases_->getW()->getData(), 1, | ||
| biases_->getW()->getElementCnt(), false, useGpu_); | ||
| transOutValue_->addBias(*bias, 1.0f); | ||
|
|
||
| transOutValue_->reshape(mapW, mapH); | ||
| transOutValue_->transpose(out, false); // false means no memory allocation | ||
|
|
||
| out->clear(); | ||
| bias->clear(); | ||
| } | ||
|
|
||
| void ExpandConvBaseLayer::addUnsharedBias() { | ||
| MatrixPtr outValue = getOutputValue(); | ||
| MatrixPtr bias = | ||
| Matrix::create(biases_->getW()->getData(), 1, | ||
| biases_->getW()->getElementCnt(), false, useGpu_); | ||
| outValue->addBias(*bias, 1.0f); | ||
| } | ||
|
|
||
|
|
||
| void ExpandConvBaseLayer::expandOneFrame(MatrixPtr image, size_t startIdx, | ||
| int inIdx) { | ||
| int channel = isDeconv_ ? numFilters_ : channels_[inIdx]; | ||
|
|
||
| resetExpandInput(subK_[inIdx] * groups_[inIdx], subN_[inIdx]); | ||
| real *imgData = image->getData() + startIdx * image->getWidth(); | ||
| MatrixPtr imageTmp = Matrix::create( | ||
| imgData, 1, imgSizeH_[inIdx] * imgSizeW_[inIdx] * channel, false, | ||
| useGpu_); | ||
| expandInput_->convExpand(*imageTmp, imgSizeH_[inIdx], imgSizeW_[inIdx], | ||
| channel, filterSize_[inIdx], | ||
| filterSize_[inIdx], stride_[inIdx], stride_[inIdx], | ||
| padding_[inIdx], padding_[inIdx], | ||
| outputH_[inIdx], outputW_[inIdx]); | ||
| imageTmp->clear(); | ||
| } | ||
|
|
||
| void ExpandConvBaseLayer::expandFwdOnce(MatrixPtr image, MatrixPtr out, | ||
| int inIdx, int startIdx) { | ||
| int subM = subM_[inIdx]; | ||
| int subN = subN_[inIdx]; | ||
| int subK = subK_[inIdx]; | ||
|
|
||
| expandOneFrame(image, startIdx, inIdx); | ||
|
|
||
| int numFilters = isDeconv_ ? channels_[inIdx] : numFilters_; | ||
|
|
||
| real *outData = | ||
| out->getData() + startIdx * subN * numFilters; | ||
|
|
||
| real *wgtData = weights_[inIdx]->getW()->getData(); | ||
| real *expInData = expandInput_->getData(); | ||
| for (int g = 0; g < groups_[inIdx]; ++g) { | ||
| MatrixPtr A = | ||
| Matrix::create(wgtData, subK, subM, true, useGpu_); // mark transpose | ||
| MatrixPtr B = Matrix::create(expInData, subK, subN, false, useGpu_); | ||
| MatrixPtr C = Matrix::create(outData, subM, subN, false, useGpu_); | ||
| C->mul(A, B, 1, 1); | ||
|
|
||
| A->clear(); | ||
| B->clear(); | ||
| C->clear(); | ||
| wgtData += subK * subM; | ||
| expInData += subK * subN; | ||
| outData += subM * subN; | ||
| } | ||
| } | ||
|
|
||
| void ExpandConvBaseLayer::bpropActs(MatrixPtr out, MatrixPtr image, | ||
| int inpIdx) { | ||
| int channel = isDeconv_ ? numFilters_ : channels_[inpIdx]; | ||
|
|
||
| int subM = subM_[inpIdx]; | ||
| int subN = subN_[inpIdx]; | ||
| int subK = subK_[inpIdx]; | ||
| size_t batchSize = image->getHeight(); | ||
|
|
||
| /* reset the expand-grad memory */ | ||
| resetExpandInput(subK * groups_[inpIdx], subN); | ||
|
|
||
| real *localGradData = out->getData(); | ||
| real *tgtGradData = image->getData(); | ||
| for (size_t n = 0; n < batchSize; n++) { | ||
| real *wgtData = weights_[inpIdx]->getW()->getData(); | ||
| real *expandInData = expandInput_->getData(); | ||
|
|
||
| for (int g = 0; g < groups_[inpIdx]; g++) { | ||
| // create temporary matrix | ||
| MatrixPtr C = Matrix::create(expandInData, subK, subN, false, useGpu_); | ||
| MatrixPtr B = Matrix::create(localGradData, subM, subN, false, useGpu_); | ||
| MatrixPtr A = Matrix::create(wgtData, subK, subM, false, useGpu_); | ||
| C->mul(A, B); // mul | ||
|
|
||
| // clear the temporary matrix | ||
| A->clear(); | ||
| B->clear(); | ||
| C->clear(); | ||
|
|
||
| expandInData += subK * subN; | ||
| localGradData += subM * subN; | ||
| wgtData += subK * subM; | ||
| } | ||
|
|
||
| // shrink one frame outGrad | ||
| MatrixPtr oneGradTmp = Matrix::create( | ||
| expandInput_->getData(), subK * groups_[inpIdx], subN, false, useGpu_); | ||
| MatrixPtr vTmp = Matrix::create( | ||
| tgtGradData, 1, | ||
| imgSizeH_[inpIdx] * imgSizeW_[inpIdx] * channel, false, | ||
| useGpu_); | ||
| vTmp->convShrink(*oneGradTmp, imgSizeH_[inpIdx], imgSizeW_[inpIdx], | ||
| channel, filterSize_[inpIdx], | ||
| filterSize_[inpIdx], stride_[inpIdx], stride_[inpIdx], | ||
| padding_[inpIdx], padding_[inpIdx], | ||
| outputH_[inpIdx], outputW_[inpIdx], 1.0f, 1.0f); | ||
| vTmp->clear(); | ||
| oneGradTmp->clear(); | ||
|
|
||
| // move the data-pointer | ||
| tgtGradData += imgSizeH_[inpIdx] * imgSizeW_[inpIdx] * channel; | ||
| } | ||
| } | ||
|
|
||
| void ExpandConvBaseLayer::bpropWeights(MatrixPtr image, MatrixPtr out, | ||
| int inpIdx) { | ||
| MatrixPtr weightGrad = weights_[inpIdx]->getWGrad(); | ||
|
|
||
| int subM = subM_[inpIdx]; | ||
| int subN = subN_[inpIdx]; | ||
| int subK = subK_[inpIdx]; | ||
| size_t batchSize = image->getHeight(); | ||
| resetExpandInput(subK * groups_[inpIdx], subN); | ||
|
|
||
| real *gradData = out->getData(); | ||
|
|
||
| for (size_t n = 0; n < batchSize; n++) { // frame by frame | ||
| // expand | ||
| expandOneFrame(image, n, inpIdx); | ||
| real *wGradData = weightGrad->getData(); | ||
| real *expandInData = expandInput_->getData(); | ||
|
|
||
| // expand-mul one-group by one | ||
| for (int g = 0; g < groups_[inpIdx]; g++) { | ||
| MatrixPtr A = Matrix::create(expandInData, subK, subN, false, useGpu_); | ||
| MatrixPtr B = Matrix::create(gradData, subM, subN, true, useGpu_); | ||
| MatrixPtr C = Matrix::create(wGradData, subK, subM, false, useGpu_); | ||
| C->mul(A, B, 1, 1); | ||
|
|
||
| A->clear(); | ||
| B->clear(); | ||
| C->clear(); | ||
| gradData += subM * subN; | ||
| wGradData += subK * subM; | ||
| expandInData += subK * subN; | ||
| } | ||
| } | ||
| } | ||
|
|
||
| void ExpandConvBaseLayer::bpropSharedBias(MatrixPtr biases, MatrixPtr v) { | ||
| size_t mapW = getOutputSize() / numFilters_; | ||
| size_t mapH = v->getElementCnt() / mapW; | ||
| MatrixPtr vTmp = Matrix::create(v->getData(), mapH, mapW, false, useGpu_); | ||
|
|
||
| Matrix::resizeOrCreate(transOutValue_, mapW, mapH, false, useGpu_); | ||
|
|
||
| vTmp->transpose(transOutValue_, false); // false means no memory allocation | ||
| transOutValue_->reshape(transOutValue_->getElementCnt() / numFilters_, | ||
| numFilters_); | ||
| biases->collectBias(*transOutValue_, 1.0f); | ||
| } | ||
|
|
||
| void ExpandConvBaseLayer::bpropBiases(MatrixPtr v) { | ||
| MatrixPtr biases = | ||
| Matrix::create(biases_->getWGrad()->getData(), 1, | ||
| biases_->getWGrad()->getElementCnt(), false, useGpu_); | ||
| if (sharedBiases_) { | ||
| bpropSharedBias(biases, v); | ||
| } else { | ||
| biases->collectBias(*v, 1.0f); | ||
| } | ||
| biases->clear(); | ||
| } | ||
|
|
||
| } // namespace paddle |
Oops, something went wrong.