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modify the format and delete useless comment

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cblas_new
xzl 8 years ago
parent fc8aedb1de
commit c43f6936c0
3 changed files with 52 additions and 48 deletions
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12
paddle/function/DepthwiseConvOp.cpp
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@ -15,7 +15,6 @@ limitations under the License. */
#include "DepthwiseConvOp.h"
#include "ConvOp.h"
#include "GemmFunctor.h"
//#include "paddle/math/MemoryHandle.h"
namespace paddle {
@ -28,6 +27,7 @@ public:
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterHeight,
@ -114,7 +114,7 @@ public:
const TensorShape& output = outputs[0].shape();
size_t batchSize = input[0];
// size_t inputChannels = input[1];
size_t inputChannels = input[1];
size_t inputHeight = input[2];
size_t inputWidth = input[3];
size_t filterHeight = getFilterHeight(filter);
@ -134,6 +134,7 @@ public:
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterHeight,
@ -168,8 +169,6 @@ public:
CHECK_EQ(numInputs_, inputs.size());
CHECK_EQ(numOutputs_, outputs.size());
check(inputs, outputs);
// Since the implementation of Col2ImFunctor is ADD_TO,
// this function only supports ADD_TO mode.
CHECK_EQ(outputs[0].getArgType(), ADD_TO);
const TensorShape& output = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
@ -228,12 +227,11 @@ public:
}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
// CHECK_EQ(numInputs_, inputs.size());
// CHECK_EQ(numOutputs_, outputs.size());
CHECK_EQ(numInputs_, inputs.size());
CHECK_EQ(numOutputs_, outputs.size());
check(inputs, outputs);
const TensorShape& output = inputs[0].shape();
const TensorShape& input = inputs[1].shape();
// const TensorShape& multiplier = inputs[2].shape();
const TensorShape& filter = outputs[0].shape();
size_t batchSize = input[0];

4
paddle/function/DepthwiseConvOp.h
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@ -29,6 +29,7 @@ namespace paddle {
* \param[in] outputChannels channels of outputData.
* \param[in] outputHeight height of outputData.
* \param[in] outputWidth width of outputData.
* \param[in] inputChannels channels of inputData.
* \param[in] inputHeight height of inputData.
* \param[in] inputWidth width of inputData..
* \param[in] filterHeight height of filter.
@ -49,8 +50,9 @@ public:
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int intputWidth,
int inputWidth,
int filterHeight,
int filterWidth,
int strideH,

84
paddle/function/DepthwiseConvOpGpu.cu
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@ -24,7 +24,7 @@ __global__
void ConvolutionDepthwiseForward(const int nthreads,
const T* const inputData, const T* const filterData,
const int batchSize, const int outputChannels, const int outputHeight,
const int outputWidth, const int inputHeight, const int inputWidth,
const int outputWidth,const int inputChannels, const int inputHeight, const int inputWidth,
const int filterHeight, const int filterWidth, const int strideH,
const int strideW, const int paddingH, const int paddingW,
T* const outputData) {
@ -39,36 +39,36 @@ void ConvolutionDepthwiseForward(const int nthreads,
const int w = index % outputWidth;
const T* weight = filterData + c * filterHeight * filterWidth;
T value = 0;
const int h_in_start = -paddingH + h * strideH;
const int w_in_start = -paddingW + w * strideW;
const int h_in_end = -paddingH + h * strideH + filterHeight - 1;
const int w_in_end = -paddingW + w * strideW + filterWidth - 1;
const int h_in_start = -paddingH + h * strideH;
const int w_in_start = -paddingW + w * strideW;
const int h_in_end = -paddingH + h * strideH + filterHeight - 1;
const int w_in_end = -paddingW + w * strideW + filterWidth - 1;
if ((h_in_start >= 0) && (h_in_end < inputHeight)
&&(w_in_start >= 0) && (w_in_end < inputWidth)) {
for (int kh = 0; kh < filterHeight; ++kh) {
for (int kw = 0; kw < filterWidth; ++kw) {
const int h_in = -paddingH + h * strideH + kh;
const int w_in = -paddingW + w * strideW + kw;
const int offset = ((n * outputChannels + c) * inputHeight + h_in)
for (int kh = 0; kh < filterHeight; ++kh) {
for (int kw = 0; kw < filterWidth; ++kw) {
const int h_in = -paddingH + h * strideH + kh;
const int w_in = -paddingW + w * strideW + kw;
const int offset = ((n * inputChannels + c) * inputHeight + h_in)
* inputWidth + w_in;
value += (*weight) * inputData[offset];
++weight;
}
}
}else{
for (int kh = 0; kh < filterHeight; ++kh) {
for (int kw = 0; kw < filterWidth; ++kw) {
const int h_in = -paddingH + h * strideH + kh;
const int w_in = -paddingW + w * strideW + kw;
if ((h_in >= 0) && (h_in < inputHeight)
&& (w_in >= 0) && (w_in < inputWidth)) {
const int offset = ((n * outputChannels + c) * inputHeight + h_in)
* inputWidth + w_in;
value += (*weight) * inputData[offset];
}
++weight;
value += (*weight) * inputData[offset];
++weight;
}
}
}else{
for (int kh = 0; kh < filterHeight; ++kh) {
for (int kw = 0; kw < filterWidth; ++kw) {
const int h_in = -paddingH + h * strideH + kh;
const int w_in = -paddingW + w * strideW + kw;
if ((h_in >= 0) && (h_in < inputHeight)
&& (w_in >= 0) && (w_in < inputWidth)) {
const int offset = ((n * outputChannels + c) * inputHeight + h_in)
* inputWidth + w_in;
value += (*weight) * inputData[offset];
}
++weight;
}
}
}
outputData[index] = value;
}
@ -80,15 +80,15 @@ __global__
void ConvolutionDepthwiseInputBackward(const int nthreads,
const T* const top_diff, const T* const weight_data,
const int num, const int outputChannels, const int outputHeight,
const int outputWidth, const int inputHeight, const int inputWidth,
const int outputWidth,const int inputChannels, const int inputHeight, const int inputWidth,
const int filterHeight, const int filterWidth, const int strideH,
const int strideW, const int paddingH, const int paddingW,
T* const bottom_diff) {
int index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if(index < nthreads) {
const int n = index / outputChannels / inputHeight / inputWidth;
const int c = (index / inputHeight / inputWidth) % outputChannels;
const int n = index / inputChannels / inputHeight / inputWidth;
const int c = (index / inputHeight / inputWidth) % inputChannels;
const int h = (index / inputWidth) % inputHeight;
const int w = index % inputWidth;
const T* weight = weight_data + c * filterHeight * filterWidth;
@ -100,7 +100,7 @@ void ConvolutionDepthwiseInputBackward(const int nthreads,
if (((h_out_s % strideH) == 0) && ((w_out_s % strideW) == 0)) {
const int h_out = h_out_s / strideH;
const int w_out = w_out_s / strideW;
// TODO(zhaolong) : the 'if' affect the effectiveness, it needs to optimize
// TODO(zhaolong) : the 'if' affect the effectiveness, it needs to optimize
if ((h_out >= 0) && (h_out < outputHeight)
&& (w_out >= 0) && (w_out < outputWidth)) {
const int offset = ((n * outputChannels + c) * outputHeight + h_out)
@ -121,7 +121,7 @@ __global__
void ConvolutionDepthwiseFilterBackward(const int num_i, const int nthreads,
const T* const top_diff, const T* const inputData,
const int num, const int outputChannels, const int outputHeight,
const int outputWidth, const int inputHeight, const int inputWidth,
const int outputWidth, const int inputChannels, const int inputHeight, const int inputWidth,
const int filterHeight, const int filterWidth, const int strideH,
const int strideW, const int paddingH, const int paddingW,
T* const buffer_data) {
@ -141,7 +141,7 @@ void ConvolutionDepthwiseFilterBackward(const int num_i, const int nthreads,
const int n = num_i;
const int top_offset = ((n * outputChannels + c) * outputHeight + h)
* outputWidth + w;
const int bottom_offset = ((n * outputChannels + c) * inputHeight + h_in)
const int bottom_offset = ((n * inputChannels + c) * inputHeight + h_in)
* inputWidth + w_in;
buffer_data[index] = top_diff[top_offset] * inputData[bottom_offset];
} else {
@ -159,6 +159,7 @@ public:
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterHeight,
@ -186,6 +187,7 @@ public:
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterHeight,
@ -218,7 +220,7 @@ public:
int paddingW,
T* inputGrad){
int inputSize = batchSize * inputChannels * inputHeight * inputWidth;
int inputSize = batchSize * inputChannels * inputHeight * inputWidth;
size_t blocks = (inputSize + 1024 -1) / 1024;
size_t blockX = 512;
@ -237,6 +239,7 @@ public:
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterHeight,
@ -277,11 +280,11 @@ public:
size_t blockY = (blocks+512-1)/512;
dim3 threads(1024, 1);
dim3 grid(blockX, blockY);
BaseMatrix filterGradMatrix(inputChannels * filterHeight * filterWidth, 1, filterGrad, false, true);
BaseMatrix filterGradMatrix(inputChannels * filterHeight * filterWidth, 1, filterGrad, false, true);
for(int i = 0; i < batchSize; i++) {
ConvolutionDepthwiseFilterBackward<T>
<<< grid, threads, 0, STREAM_DEFAULT >>>(
ConvolutionDepthwiseFilterBackward<T>
<<< grid, threads, 0, STREAM_DEFAULT >>>(
i,
colDataSize,
outputGrad,
@ -290,6 +293,7 @@ public:
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterHeight,
@ -299,12 +303,12 @@ public:
paddingH,
paddingW,
colData
);
int M = colDataSize / outputHeight / outputWidth;
int K = outputHeight * outputWidth;
);
int M = colDataSize / outputHeight / outputWidth;
int K = outputHeight * outputWidth;
BaseMatrix colMatrix(M, K, colData, false, true);
filterGradMatrix.sumRows(colMatrix, (T)1.0, (T)1.0);
filterGradMatrix.sumRows(colMatrix, (T)1.0, (T)1.0);
}
}
};

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