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Reconstruction of GemmConv Based on new im2col.

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cblas_new
hedaoyuan 8 years ago
parent eb0c7e5ebc
commit 07cde439aa
1 changed files with 48 additions and 137 deletions

179
paddle/function/GemmConvOp.cpp
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@ -12,101 +12,13 @@ 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 "GemmConvOp.h"
#include "ConvOp.h"
#include "GemmFunctor.h"
#include "Im2Col.h"
#include "paddle/math/MemoryHandle.h"
namespace paddle {
/*
* imData = [input_channels, input_height, input_width]
* colData = [input_channels, filter_height, filter_width,
* output_height, output_width]
*/
template <class T>
class Im2ColFunctor<DEVICE_TYPE_CPU, T> {
public:
void operator()(const T* imData,
int inputChannels,
int inputHeight,
int inputWidth,
int filterHeight,
int filterWidth,
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth,
int outputHeight,
int outputWidth,
T* colData) {
int channelsCol = inputChannels * filterHeight * filterWidth;
for (int c = 0; c < channelsCol; ++c) {
int wOffset = c % filterWidth;
int hOffset = (c / filterWidth) % filterHeight;
int c_im = c / filterWidth / filterHeight;
for (int h = 0; h < outputHeight; ++h) {
for (int w = 0; w < outputWidth; ++w) {
int imRowIdx = h * strideHeight + hOffset;
int imColIdx = w * strideWidth + wOffset;
if ((imRowIdx - paddingHeight) < 0 ||
(imRowIdx - paddingHeight) >= inputHeight ||
(imColIdx - paddingWidth) < 0 ||
(imColIdx - paddingWidth) >= inputWidth) {
colData[(c * outputHeight + h) * outputWidth + w] = T(0);
} else {
imRowIdx += c_im * inputHeight - paddingHeight;
imColIdx -= paddingWidth;
colData[(c * outputHeight + h) * outputWidth + w] =
imData[imRowIdx * inputWidth + imColIdx];
}
}
}
}
}
};
template <class T>
class Col2ImFunctor<DEVICE_TYPE_CPU, T> {
public:
void operator()(const T* colData,
int inputChannels,
int inputHeight,
int inputWidth,
int filterHeight,
int filterWidth,
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth,
int outputHeight,
int outputWidth,
T* imData) {
int channelsCol = inputChannels * filterHeight * filterWidth;
for (int c = 0; c < channelsCol; ++c) {
int wOffset = c % filterWidth;
int hOffset = (c / filterWidth) % filterHeight;
int c_im = c / filterWidth / filterHeight;
for (int h = 0; h < outputHeight; ++h) {
for (int w = 0; w < outputWidth; ++w) {
int imRowIdx = h * strideHeight + hOffset;
int imColIdx = w * strideWidth + wOffset;
if ((imRowIdx - paddingHeight) >= 0 &&
(imRowIdx - paddingHeight) < inputHeight &&
(imColIdx - paddingWidth) >= 0 &&
(imColIdx - paddingWidth) < inputWidth) {
imRowIdx += c_im * inputHeight - paddingHeight;
imColIdx -= paddingWidth;
imData[imRowIdx * inputWidth + imColIdx] +=
colData[(c * outputHeight + h) * outputWidth + w];
}
}
}
}
}
};
/*
* \brief Forward calculation of convolution.
*/
@ -155,15 +67,20 @@ public:
real* inputData = inputs[0].data<real>();
real* filterData = inputs[1].data<real>();
real* outputData = outputs[0].data<real>();
TensorShape imShape =
TensorShape({inputChannels / groups_, inputHeight, inputWidth});
TensorShape colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
size_t size = inputChannels / groups_ * filterHeight * filterWidth *
outputHeight * outputWidth;
resizeBuffer<Device>(size);
resizeBuffer<Device>(colShape.getElements());
real* colData = reinterpret_cast<real*>(memory_->getBuf());
Im2ColFunctor<Device, real> im2col;
Im2ColFunctor<kCFO, Device, real> im2col;
GemmFunctor<Device, real> gemm;
size_t inputOffset = (inputChannels / groups_) * inputHeight * inputWidth;
size_t inputOffset = imShape.getElements();
size_t outputOffset =
(outputChannels / groups_) * outputHeight * outputWidth;
size_t filterOffset = filter.getElements() / groups_;
@ -171,18 +88,13 @@ public:
for (size_t i = 0; i < batchSize; i++) {
for (size_t g = 0; g < groups_; g++) {
im2col(inputData + g * inputOffset,
inputChannels / groups_,
inputHeight,
inputWidth,
filterHeight,
filterWidth,
imShape,
colData,
colShape,
strideH(),
strideW(),
paddingH(),
paddingW(),
outputHeight,
outputWidth,
colData);
paddingW());
int M = outputChannels / groups_;
int N = outputHeight * outputWidth;
@ -249,15 +161,20 @@ public:
real* outputGrad = inputs[0].data<real>();
real* filterData = inputs[1].data<real>();
real* inputGrad = outputs[0].data<real>();
TensorShape imShape =
TensorShape({inputChannels / groups_, inputHeight, inputWidth});
TensorShape colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
size_t size = inputChannels / groups_ * filterHeight * filterWidth *
outputHeight * outputWidth;
resizeBuffer<Device>(size);
resizeBuffer<Device>(colShape.getElements());
real* colData = reinterpret_cast<real*>(memory_->getBuf());
Col2ImFunctor<Device, real> col2im;
Col2ImFunctor<kCFO, Device, real> col2im;
GemmFunctor<Device, real> gemm;
size_t inputOffset = (inputChannels / groups_) * inputHeight * inputWidth;
size_t inputOffset = imShape.getElements();
size_t outputOffset =
(outputChannels / groups_) * outputHeight * outputWidth;
size_t filterOffset = filter.getElements() / groups_;
@ -280,20 +197,14 @@ public:
0.0f,
colData,
N);
col2im(colData,
inputChannels / groups_,
inputHeight,
inputWidth,
filterHeight,
filterWidth,
col2im(inputGrad + g * inputOffset,
imShape,
colData,
colShape,
strideH(),
strideW(),
paddingH(),
paddingW(),
outputHeight,
outputWidth,
inputGrad + g * inputOffset);
paddingW());
}
inputGrad += inputChannels * inputHeight * inputWidth;
outputGrad += outputChannels * outputHeight * outputWidth;
@ -347,33 +258,33 @@ public:
real* outputGrad = inputs[0].data<real>();
real* inputData = inputs[1].data<real>();
real* filterGrad = outputs[0].data<real>();
TensorShape imShape =
TensorShape({inputChannels / groups_, inputHeight, inputWidth});
TensorShape colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
size_t size = inputChannels / groups_ * filterHeight * filterWidth *
outputHeight * outputWidth;
resizeBuffer<Device>(size);
resizeBuffer<Device>(colShape.getElements());
real* colData = reinterpret_cast<real*>(memory_->getBuf());
Im2ColFunctor<Device, real> im2col;
Im2ColFunctor<kCFO, Device, real> im2col;
GemmFunctor<Device, real> gemm;
size_t inputOffset = (inputChannels / groups_) * inputHeight * inputWidth;
size_t inputOffset = imShape.getElements();
size_t outputOffset =
(outputChannels / groups_) * outputHeight * outputWidth;
size_t filterOffset = filter.getElements() / groups_;
for (size_t i = 0; i < batchSize; i++) {
for (size_t g = 0; g < groups_; g++) {
im2col(inputData + g * inputOffset,
inputChannels / groups_,
inputHeight,
inputWidth,
filterHeight,
filterWidth,
imShape,
colData,
colShape,
strideH(),
strideW(),
paddingH(),
paddingW(),
outputHeight,
outputWidth,
colData);
paddingW());
int M = outputChannels / groups_;
int K = outputHeight * outputWidth;

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