m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Add DeConv3DLayer

Browse Source
revert-3824-remove_grad_op_type
chengduoZH 8 years ago
parent 11975b4f91
commit 23cf0c61e0
2 changed files with 269 additions and 0 deletions
Show Stats Download Patch File Download Diff File

211
paddle/gserver/layers/DeConv3DLayer.cpp
Unescape View File

@ -0,0 +1,211 @@
/* 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 "DeConv3DLayer.h"
namespace paddle {
REGISTER_LAYER(deconv3d, DeConv3DLayer);
#define DECONV_OUTPUT_SIZE(IN_SIZE, STRID, PAD, KSIZE) \
(((IN_SIZE) - 1) * (STRID) - 2 * (PAD) + (KSIZE))
bool DeConv3DLayer::init(const LayerMap &layerMap,
const ParameterMap &parameterMap) {
if (!ConvBaseLayer::init(layerMap, parameterMap)) return false;
// for Deconv, the dimension of Kernel is
// channel * output * depth * height * weigth
// Matrix storage format: (output * depth * height * weigth) x channel
for (int index = 0; index < config_.inputs().size(); ++index) {
M_.push_back(filterChannels_[index]);
K_.push_back(
filterPixels_[index] * (numFilters_/groups_[index]));
weights_[index]->getW()->reshape(
filterPixels_[index] * numFilters_,
filterChannels_[index]);
weights_[index]->getWGrad()->reshape(
filterPixels_[index] * numFilters_,
filterChannels_[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 DeConv3DLayer::getSize() {
CHECK_NE(inputLayers_.size(), 0UL);
// imgSizeH_.clear();
// imgSizeW_.clear();
// imgSizeD_.clear();
outputH_.clear();
outputW_.clear();
outputD_.clear();
N_.clear();
No_.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(
DECONV_OUTPUT_SIZE(
imgSizeW_[i], stride_[i],
padding_[i], filterSize_[i]));
outputH_.push_back(
DECONV_OUTPUT_SIZE(
imgSizeH_[i], strideY_[i],
paddingY_[i], filterSizeY_[i]));
outputD_.push_back(
DECONV_OUTPUT_SIZE(
imgSizeD_[i], strideZ_[i],
paddingZ_[i], filterSizeZ_[i]));
No_.push_back(outputD_[i] * outputH_[i] * outputW_[i]);
N_.push_back(imgSizeD_[i] * imgSizeH_[i] * imgSizeW_[i]);
CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize);
layerSize += No_[i] * numFilters_;
}
getOutput().setFrameHeight(outputH_[0]);
getOutput().setFrameWidth(outputW_[0]);
getOutput().setFrameDepth(outputD_[0]);
return layerSize;
}
void DeConv3DLayer::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("FwdDeConv3D", getName().c_str());
const MatrixPtr& inMat = getInputValue(i);
int width = inMat->getWidth();
int M = M_[i];
int N = N_[i];
int K = K_[i];
MatrixPtr wMat = weights_[i]->getW();
Matrix::resizeOrCreate(colBuf_, K * groups_[i] , N, false, useGpu_);
for (int n = 0; n < batchSize; ++n) {
real *inData = inMat->getData() + n * width;
real *colBufData = colBuf_->getData();
for (int g = 0; g < groups_[i]; g++) {
MatrixPtr wMatSub = wMat->subMatrix(g * K, K);
MatrixPtr inMatSub =
Matrix::create(inData, M, N, false, useGpu_);
MatrixPtr colBufDataSub =
Matrix::create(colBufData, K, N, false, useGpu_);
colBufDataSub->mul(*wMatSub, *inMatSub, 1.0, 0.0);
colBufData += K * N;
inData += M * N;
}
colBuf_->col2Vol(outMat->getData()+ n * outMat->getWidth(),
numFilters_, outputD_[i], outputH_[i], outputW_[i],
filterSizeZ_[i], filterSizeY_[i], filterSize_[i],
strideZ_[i], strideY_[i], stride_[i],
paddingZ_[i], paddingY_[i], padding_[i], 1.0, 1.0);
}
}
if (nullptr != this->biasParameter_) {
REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str());
this->addBias();
}
forwardActivation();
}
void DeConv3DLayer::backward(const UpdateCallback &callback) {
backwardActivation();
int batchSize = getOutputGrad()->getHeight();
int outputWidth = getOutputGrad()->getWidth();
if (biases_ && biases_->getWGrad()) {
bpropBiases();
biases_->getParameterPtr()->incUpdate(callback);
}
for (size_t i =0; i < inputLayers_.size(); ++i) {
int M = M_[i];
int N = N_[i];
int K = K_[i];
Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
const MatrixPtr& inMat = getInputValue(i);
for (int n = 0; n < batchSize; ++n) {
REGISTER_TIMER_INFO("BwdDeConv3D", getName().c_str());
if (weights_[i]->getWGrad() || this->needGradient_) {
colBuf_->vol2Col(getOutputGrad()->getData() + n * outputWidth,
numFilters_, outputD_[i], outputH_[i], outputW_[i],
filterSizeZ_[i], filterSizeY_[i], filterSize_[i],
strideZ_[i], strideY_[i], stride_[i],
paddingZ_[i], paddingY_[i], padding_[i]);
}
if (weights_[i]->getWGrad()) {
real *inData = inMat->getData() + n * inMat->getWidth();;
real *wGradData = weights_[i]->getWGrad()->getData();
for (int g = 0; g < groups_[i]; g++) {
MatrixPtr colBufDataSub = colBuf_->subMatrix(g * K, K);
MatrixPtr inMatSub = Matrix::create(
inData, M, N, false, useGpu_);
MatrixPtr wGradMatSub = Matrix::create(
wGradData, K, M, false, useGpu_);
wGradMatSub->mul(*colBufDataSub,
*(inMatSub->getTranspose()), 1.0, 1.0);
wGradData += K * M;
inData += M * N;
}
weights_[i]->getParameterPtr()->incUpdate(callback);
}
if (this->needGradient_) {
real* preGrad = getInputGrad(i)->getData();
for (int g = 0; g < groups_[i]; ++g) {
MatrixPtr w = weights_[i]->getW()->subMatrix(g * K, K);
MatrixPtr outGradMat = colBuf_->subMatrix(g * K, K);
MatrixPtr inGradMatSub = Matrix::create(
preGrad, M, N, false, useGpu_);
inGradMatSub->mul(*(w->getTranspose()), *outGradMat, 1.0, 0.0);
preGrad += M * N;
}
}
REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
}
}
}
void DeConv3DLayer::bpropWeights(int i) { }
void DeConv3DLayer::bpropData(int i) { }
void DeConv3DLayer::bpropBiases() {
MatrixPtr outGradMat = getOutputGrad();
if (this->sharedBiases_) {
biases_->getWGrad()->collectSharedBias(*outGradMat, 1.0f);
} else {
biases_->getWGrad()->collectBias(*outGradMat, 1.0f);
}
}
void DeConv3DLayer::addBias() {
MatrixPtr outMat = getOutputValue();
if (this->sharedBiases_) {
outMat->addSharedBias(*(biases_->getW()), 1.0f);
} else {
outMat->addBias(*(biases_->getW()), 1.0f);
}
}
} // namespace paddle

58
paddle/gserver/layers/DeConv3DLayer.h
Unescape View File

@ -0,0 +1,58 @@
/* 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 deconvolution3D layer.
* This layer expands input and use matrix multiplication to
* calculate deconvolution3D operation.
*/
class DeConv3DLayer : public ConvBaseLayer {
public:
explicit DeConv3DLayer(const LayerConfig& config) : ConvBaseLayer(config) {}
~DeConv3DLayer() {}
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_
IntV No_;
MatrixPtr colBuf_;
};
} // namespace paddle
Write Preview
Loading…
Cancel
Save