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Add Conv3DLayer

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revert-3824-remove_grad_op_type
chengduoZH 8 years ago
parent 5d7f6dde52
commit 11975b4f91
2 changed files with 282 additions and 0 deletions
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225
paddle/gserver/layers/Conv3DLayer.cpp
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/* 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 "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
#include "Conv3DLayer.h"
namespace paddle {
REGISTER_LAYER(conv3d, Conv3DLayer);
bool Conv3DLayer::init(const LayerMap &layerMap,
const ParameterMap &parameterMap) {
if (!ConvBaseLayer::init(layerMap, parameterMap))
return false;
int index = 0;
for (auto &inputConfig : config_.inputs()) {
const ConvConfig &conf = inputConfig.conv_conf();
M_.push_back(numFilters_ / conf.groups());
K_.push_back(
conf.filter_channels() * conf.filter_size_z() * \
conf.filter_size_y() * conf.filter_size());
weights_[index]->getW()->reshape(
weights_[index]->getW()->getWidth(),
weights_[index]->getW()->getHeight());
weights_[index]->getWGrad()->reshape(
weights_[index]->getWGrad()->getWidth(),
weights_[index]->getWGrad()->getHeight());
++index;
}
biases_->getWGrad()->reshape(
biases_->getWGrad()->width_, biases_->getWGrad()->height_);
biases_->getW()->reshape(
biases_->getW()->width_, biases_->getW()->height_);
CHECK(inputLayers_.size() == parameters_.size());
return true;
}
size_t Conv3DLayer::getSize() {
CHECK_NE(inputLayers_.size(), 0UL);
// imgSizeH_.clear();
// imgSizeW_.clear();
// imgSizeD_.clear();
outputH_.clear();
outputW_.clear();
outputD_.clear();
N_.clear();
size_t layerSize = 0;
for (size_t i = 0; i < inputLayers_.size(); ++i) {
// imgSizeH_.push_back(inputLayers_[i]->getOutput().getFrameHeight());
// imgSizeW_.push_back(inputLayers_[i]->getOutput().getFrameWidth());
// imgSizeD_.push_back(inputLayers_[i]->getOutput().getFrameDepth());
outputW_.push_back(outputSize(
imgSizeW_[i], filterSize_[i],
padding_[i], stride_[i], true));
outputH_.push_back(outputSize(
imgSizeH_[i], filterSizeY_[i],
paddingY_[i], strideY_[i], true));
outputD_.push_back(outputSize(
imgSizeD_[i], filterSizeZ_[i],
paddingZ_[i], strideZ_[i], true));
N_.push_back(outputD_[i] * outputH_[i] * outputW_[i]);
CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize);
layerSize += N_[i] * numFilters_;
}
getOutput().setFrameHeight(outputH_[0]);
getOutput().setFrameWidth(outputW_[0]);
getOutput().setFrameDepth(outputD_[0]);
return layerSize;
}
void Conv3DLayer::forward(PassType passType) {
Layer::forward(passType);
int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
int outWidth = getSize();
resetOutput(batchSize, outWidth);
const MatrixPtr outMat = getOutputValue();
for (size_t i = 0; i != inputLayers_.size(); ++i) {
REGISTER_TIMER_INFO("FwdConv3D", getName().c_str());
const MatrixPtr& inMat = getInputValue(i);
int width = inMat->getWidth();
int M = M_[i];
int N = N_[i];
int K = K_[i];
Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
MatrixPtr wMat = weights_[i]->getW();
for (int n = 0; n < batchSize; ++n) {
colBuf_->vol2Col(inMat->getData() + n * width, channels_[i],
imgSizeD_[i], imgSizeH_[i], imgSizeW_[i],
filterSizeZ_[i], filterSizeY_[i], filterSize_[i],
strideZ_[i], strideY_[i], stride_[i],
paddingZ_[i], paddingY_[i], padding_[i]);
real *outData = outMat->getData() + n * outWidth;
MatrixPtr outMatSub =
Matrix::create(outData, groups_[i] * M, N, false, useGpu_);
for (int g = 0; g < groups_[i]; g++) {
MatrixPtr wMatSub = wMat->subMatrix(g * M, M);
MatrixPtr in = colBuf_->subMatrix(g * K, K);
MatrixPtr out = outMatSub->subMatrix(g * M, M);
out->mul(*wMatSub, *in, 1.0, 0.0);
}
}
}
if (nullptr != this->biasParameter_) {
REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str());
this->addBias();
}
forwardActivation();
}
void Conv3DLayer::backward(const UpdateCallback &callback) {
backwardActivation();
if (biases_ && biases_->getWGrad()) {
bpropBiases();
biases_->getParameterPtr()->incUpdate(callback);
}
for (size_t i = 0; i != inputLayers_.size(); ++i) {
REGISTER_TIMER_INFO("BwdConv3D", getName().c_str());
if (weights_[i]->getWGrad()) {
bpropWeights(i);
}
if (this->needGradient_) {
bpropData(i);
}
REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
weights_[i]->getParameterPtr()->incUpdate(callback);
}
}
void Conv3DLayer::bpropWeights(int i) {
int M = M_[i];
int N = N_[i];
int K = K_[i];
const MatrixPtr& inMat = getInputValue(i);
int width = inMat->getWidth();
Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
MatrixPtr wGradMat = weights_[i]->getWGrad();
real* outGradData = getOutputGrad()->getData();
int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
for (int n = 0; n < batchSize; ++n) {
colBuf_->vol2Col(inMat->getData() + n * width, channels_[i],
imgSizeD_[i], imgSizeH_[i], imgSizeW_[i],
filterSizeZ_[i], filterSizeY_[i], filterSize_[i],
strideZ_[i], strideY_[i], stride_[i],
paddingZ_[i], paddingY_[i], padding_[i]);
outGradData += n * getOutputGrad()->getWidth();
MatrixPtr outGradSub =
Matrix::create(outGradData, groups_[i] * M, N, false, useGpu_);
for (int g = 0; g < groups_[i]; ++g) {
MatrixPtr inMatSub = colBuf_->subMatrix(g * K, K);
MatrixPtr outG = outGradSub->subMatrix(g * M, M);
MatrixPtr wGradSub = wGradMat->subMatrix(g * M, M);
wGradSub->mul(*outG, *(inMatSub->getTranspose()), 1.0, 1.0);
}
}
}
void Conv3DLayer::bpropData(int i) {
int M = M_[i];
int N = N_[i];
int K = K_[i];
Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
MatrixPtr wMat = weights_[i]->getW();
real* outGradData = getOutputGrad()->getData();
real* preGradData = getInputGrad(i)->getData();
int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
for (int n = 0; n < batchSize; ++n) {
outGradData += n * getOutputGrad()->getWidth();
preGradData += n * getInputGrad(i)->getWidth();
MatrixPtr outGradSub =
Matrix::create(outGradData, M * groups_[i], N, false, useGpu_);
for (int g = 0; g < groups_[i]; ++g) {
MatrixPtr wMatSub = wMat->subMatrix(g * M, M);
MatrixPtr outG = outGradSub->subMatrix(g * M, M);
MatrixPtr inGradMatSub = colBuf_->subMatrix(g * K, K);
inGradMatSub->mul(*(wMatSub->getTranspose()), *outG, 1.0, 0.0);
}
colBuf_->col2Vol(preGradData, channels_[i],
imgSizeD_[i], imgSizeH_[i], imgSizeW_[i],
filterSizeZ_[i], filterSizeY_[i], filterSize_[i],
strideZ_[i], strideY_[i], stride_[i],
paddingZ_[i], paddingY_[i], padding_[i],
1.0, 1.0);
}
}
void Conv3DLayer::bpropBiases() {
MatrixPtr outGradMat = getOutputGrad();
if (this->sharedBiases_) {
biases_->getWGrad()->collectSharedBias(*outGradMat, 1.0f);
} else {
biases_->getWGrad()->collectBias(*outGradMat, 1.0f);
}
}
void Conv3DLayer::addBias() {
MatrixPtr outMat = getOutputValue();
if (this->sharedBiases_) {
outMat->addSharedBias(*(biases_->getW()), 1.0f);
} else {
outMat->addBias(*(biases_->getW()), 1.0f);
}
}
} // namespace paddle

57
paddle/gserver/layers/Conv3DLayer.h
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/* 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. */
#pragma once
#include "ConvBaseLayer.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/MathUtils.h"
#include <vector>
namespace paddle {
/**
* @brief A subclass of convolution layer.
* This layer expands input and use matrix multiplication to
* calculate convolution operation.
*/
class Conv3DLayer : public ConvBaseLayer {
public:
explicit Conv3DLayer(const LayerConfig& config) : ConvBaseLayer(config) {}
~Conv3DLayer() {}
bool init(const LayerMap &layerMap, const ParameterMap &parameterMap);
size_t getSize();
void forward(PassType passType);
void addBias();
void backward(const UpdateCallback& callback);
void bpropBiases();
void bpropData(int i);
void bpropWeights(int i);
protected:
// Figure out the dimensions for individual gemms.
IntV M_; /// numFilters_ / filter_group_;
IntV N_; /// channels_ * filterSizeZ_ * filterSize_ * filterSizeY_
IntV K_; /// outputD_ * outputH_ * outputW_
MatrixPtr colBuf_;
};
} // namespace paddle
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