Skip to content

P
Paddle

wzpei9 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle

体验新版 GitCode，发现更多精彩内容 >>

提交 07cde439 编写于 作者: H hedaoyuan
浏览文件
操作

Reconstruction of GemmConv Based on new im2col.

上级 eb0c7e5e
隐藏空白更改
内联 并排
Showing with 48 addition and 137 deletion
paddle/function/GemmConvOp.cpp
浏览文件 @ 07cde439
...@@ -12,101 +12,13 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -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 See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "GemmConvOp.h" #include "ConvOp.h"
#include "GemmFunctor.h" #include "GemmFunctor.h"
#include "Im2Col.h"
#include "paddle/math/MemoryHandle.h" #include "paddle/math/MemoryHandle.h"
namespace paddle { 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. * \brief Forward calculation of convolution.
*/ */
...@@ -155,15 +67,20 @@ public: ...@@ -155,15 +67,20 @@ public:
real* inputData = inputs[0].data<real>(); real* inputData = inputs[0].data<real>();
real* filterData = inputs[1].data<real>(); real* filterData = inputs[1].data<real>();
real* outputData = outputs[0].data<real>(); real* outputData = outputs[0].data<real>();
TensorShape imShape =
size_t size = inputChannels / groups_ * filterHeight * filterWidth * TensorShape({inputChannels / groups_, inputHeight, inputWidth});
outputHeight * outputWidth; TensorShape colShape = TensorShape({inputChannels / groups_,
resizeBuffer<Device>(size); filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(colShape.getElements());
real* colData = reinterpret_cast<real*>(memory_->getBuf()); real* colData = reinterpret_cast<real*>(memory_->getBuf());
Im2ColFunctor<Device, real> im2col; Im2ColFunctor<kCFO, Device, real> im2col;
GemmFunctor<Device, real> gemm; GemmFunctor<Device, real> gemm;
size_t inputOffset = (inputChannels / groups_) * inputHeight * inputWidth; size_t inputOffset = imShape.getElements();
size_t outputOffset = size_t outputOffset =
(outputChannels / groups_) * outputHeight * outputWidth; (outputChannels / groups_) * outputHeight * outputWidth;
size_t filterOffset = filter.getElements() / groups_; size_t filterOffset = filter.getElements() / groups_;
...@@ -171,18 +88,13 @@ public: ...@@ -171,18 +88,13 @@ public:
for (size_t i = 0; i < batchSize; i++) { for (size_t i = 0; i < batchSize; i++) {
for (size_t g = 0; g < groups_; g++) { for (size_t g = 0; g < groups_; g++) {
im2col(inputData + g * inputOffset, im2col(inputData + g * inputOffset,
inputChannels / groups_, imShape,
inputHeight, colData,
inputWidth, colShape,
filterHeight,
filterWidth,
strideH(), strideH(),
strideW(), strideW(),
paddingH(), paddingH(),
paddingW(), paddingW());
outputHeight,
outputWidth,
colData);
int M = outputChannels / groups_; int M = outputChannels / groups_;
int N = outputHeight * outputWidth; int N = outputHeight * outputWidth;
...@@ -249,15 +161,20 @@ public: ...@@ -249,15 +161,20 @@ public:
real* outputGrad = inputs[0].data<real>(); real* outputGrad = inputs[0].data<real>();
real* filterData = inputs[1].data<real>(); real* filterData = inputs[1].data<real>();
real* inputGrad = outputs[0].data<real>(); real* inputGrad = outputs[0].data<real>();
TensorShape imShape =
size_t size = inputChannels / groups_ * filterHeight * filterWidth * TensorShape({inputChannels / groups_, inputHeight, inputWidth});
outputHeight * outputWidth; TensorShape colShape = TensorShape({inputChannels / groups_,
resizeBuffer<Device>(size); filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(colShape.getElements());
real* colData = reinterpret_cast<real*>(memory_->getBuf()); real* colData = reinterpret_cast<real*>(memory_->getBuf());
Col2ImFunctor<Device, real> col2im; Col2ImFunctor<kCFO, Device, real> col2im;
GemmFunctor<Device, real> gemm; GemmFunctor<Device, real> gemm;
size_t inputOffset = (inputChannels / groups_) * inputHeight * inputWidth; size_t inputOffset = imShape.getElements();
size_t outputOffset = size_t outputOffset =
(outputChannels / groups_) * outputHeight * outputWidth; (outputChannels / groups_) * outputHeight * outputWidth;
size_t filterOffset = filter.getElements() / groups_; size_t filterOffset = filter.getElements() / groups_;
...@@ -280,20 +197,14 @@ public: ...@@ -280,20 +197,14 @@ public:
0.0f, 0.0f,
colData, colData,
N); N);
col2im(inputGrad + g * inputOffset,
col2im(colData, imShape,
inputChannels / groups_, colData,
inputHeight, colShape,
inputWidth,
filterHeight,
filterWidth,
strideH(), strideH(),
strideW(), strideW(),
paddingH(), paddingH(),
paddingW(), paddingW());
outputHeight,
outputWidth,
inputGrad + g * inputOffset);
} }
inputGrad += inputChannels * inputHeight * inputWidth; inputGrad += inputChannels * inputHeight * inputWidth;
outputGrad += outputChannels * outputHeight * outputWidth; outputGrad += outputChannels * outputHeight * outputWidth;
...@@ -347,33 +258,33 @@ public: ...@@ -347,33 +258,33 @@ public:
real* outputGrad = inputs[0].data<real>(); real* outputGrad = inputs[0].data<real>();
real* inputData = inputs[1].data<real>(); real* inputData = inputs[1].data<real>();
real* filterGrad = outputs[0].data<real>(); real* filterGrad = outputs[0].data<real>();
TensorShape imShape =
size_t size = inputChannels / groups_ * filterHeight * filterWidth * TensorShape({inputChannels / groups_, inputHeight, inputWidth});
outputHeight * outputWidth; TensorShape colShape = TensorShape({inputChannels / groups_,
resizeBuffer<Device>(size); filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(colShape.getElements());
real* colData = reinterpret_cast<real*>(memory_->getBuf()); real* colData = reinterpret_cast<real*>(memory_->getBuf());
Im2ColFunctor<Device, real> im2col; Im2ColFunctor<kCFO, Device, real> im2col;
GemmFunctor<Device, real> gemm; GemmFunctor<Device, real> gemm;
size_t inputOffset = (inputChannels / groups_) * inputHeight * inputWidth; size_t inputOffset = imShape.getElements();
size_t outputOffset = size_t outputOffset =
(outputChannels / groups_) * outputHeight * outputWidth; (outputChannels / groups_) * outputHeight * outputWidth;
size_t filterOffset = filter.getElements() / groups_; size_t filterOffset = filter.getElements() / groups_;
for (size_t i = 0; i < batchSize; i++) { for (size_t i = 0; i < batchSize; i++) {
for (size_t g = 0; g < groups_; g++) { for (size_t g = 0; g < groups_; g++) {
im2col(inputData + g * inputOffset, im2col(inputData + g * inputOffset,
inputChannels / groups_, imShape,
inputHeight, colData,
inputWidth, colShape,
filterHeight,
filterWidth,
strideH(), strideH(),
strideW(), strideW(),
paddingH(), paddingH(),
paddingW(), paddingW());
outputHeight,
outputWidth,
colData);
int M = outputChannels / groups_; int M = outputChannels / groups_;
int K = outputHeight * outputWidth; int K = outputHeight * outputWidth;
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或