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Add ImageExpandFunction. #2449

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merged 23 commits into from
Jul 25, 2017
Merged

Add ImageExpandFunction. #2449

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48e0f43
Add ImageExpandFunction.
hedaoyuan Jun 12, 2017
61aa109
BlockExpandLayer based on the ImageExpand Function.
hedaoyuan Jun 13, 2017
2acb84f
Add ImageExpandGrad Function.
hedaoyuan Jun 13, 2017
0672d33
Use the TensorShape to reconstruct the arguments of the Im2ColFunctor…
hedaoyuan Jun 13, 2017
9c009b4
Remove GemmConvOp.h file.
hedaoyuan Jun 13, 2017
34362d9
Fix some of the wrong comments in im2col.h file.
hedaoyuan Jun 13, 2017
152bd2f
Add the GPU version implementation of ImageExpand function.
hedaoyuan Jun 13, 2017
f8ef8c1
Add the GPU version implementation of ImageExpandGrad function.
hedaoyuan Jun 13, 2017
bf6dfc1
Remove some of the code that has been commented out.
hedaoyuan Jun 13, 2017
69271c9
Merge branch 'develop' of https://github.com/baidu/Paddle into ImageE…
hedaoyuan Jun 19, 2017
d558b8b
Move the code in the GemmConvOpGpu.cu file into Im2ColOpGpu.cu.
hedaoyuan Jun 21, 2017
1a53cba
Merge branch 'develop' of https://github.com/baidu/Paddle into ImageE…
hedaoyuan Jun 21, 2017
eb0c7e5
Move the Im2Col code of the CPU version into the Im2ColOp.cpp file.
hedaoyuan Jun 21, 2017
07cde43
Reconstruction of GemmConv Based on new im2col.
hedaoyuan Jun 21, 2017
9e6ed83
Fix ImageExpandFunction.
hedaoyuan Jun 21, 2017
5bfcb7f
Remove useless code.
hedaoyuan Jun 21, 2017
09d712d
Remove useless code(Matrix::convExpand and Matrix::convShrink).
hedaoyuan Jun 21, 2017
86a679b
Add unit test of ImageExpandOp.
hedaoyuan Jun 21, 2017
c761010
Add unit test for im2col.
hedaoyuan Jun 27, 2017
a83d521
Add unit test for Col2ImFunctor.
hedaoyuan Jun 27, 2017
a7ff114
Change the ImageFunction name to BlockFunction(Consistent with the na…
hedaoyuan Jun 27, 2017
7a550f9
Fix the function file name.
hedaoyuan Jun 27, 2017
ff8262e
Merge branch 'develop' into ImageExpandFunction
hedaoyuan Jul 25, 2017
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67 changes: 0 additions & 67 deletions paddle/cuda/include/hl_cnn.h
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Original file line number Diff line number Diff line change
Expand Up @@ -17,73 +17,6 @@ limitations under the License. */

#include "hl_base.h"

/**
* @brief Shrink column to feature.
*
* @param[in] dataCol expand data.
* @param[in] channels number of channel.
* @param[in] height image height.
* @param[in] width image width.
* @param[in] blockH filter height.
* @param[in] blockW filter width.
* @param[in] strideH stride height.
* @param[in] strideW stride width.
* @param[in] paddingH padding height.
* @param[in] paddingW padding width.
* @param[in] outputH output height.
* @param[in] outputW output width.
* @param[out] dataIm output image data.
* @param[in] alpha
* @param[in] beta
*/
extern void hl_shrink_col2feature(const real* dataCol,
size_t channels,
size_t height,
size_t width,
size_t blockH,
size_t blockW,
size_t strideH,
size_t strideW,
size_t paddingH,
size_t paddingW,
size_t outputH,
size_t outputW,
real* dataIm,
real alpha = 1.0f,
real beta = 0.0f);

/**
* @brief Expand feature to column.
*
* @param[in] dataIm input image data.
* @param[in] channels number of channel.
* @param[in] height image height.
* @param[in] width image width.
* @param[in] blockH filter height.
* @param[in] blockW filter width.
* @param[in] strideH stride height.
* @param[in] strideW stride width.
* @param[in] paddingH padding height.
* @param[in] paddingW padding width.
* @param[in] outputH output height.
* @param[in] outputW output width.
* @param[out] dataCol expand data.
*
*/
extern void hl_expand_feature2col(const real* dataIm,
size_t channels,
size_t height,
size_t width,
size_t blockH,
size_t blockW,
size_t strideH,
size_t strideW,
size_t paddingH,
size_t paddingW,
size_t outputH,
size_t outputW,
real* dataCol);

/**
* @brief Maximum pool forward.
*
Expand Down
30 changes: 0 additions & 30 deletions paddle/cuda/include/stub/hl_cnn_stub.h
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Original file line number Diff line number Diff line change
Expand Up @@ -17,36 +17,6 @@ limitations under the License. */

#include "hl_cnn.h"

inline void hl_shrink_col2feature(const real* dataCol,
size_t channels,
size_t height,
size_t width,
size_t blockH,
size_t blockW,
size_t strideH,
size_t strideW,
size_t paddingH,
size_t paddingW,
size_t outputH,
size_t outputW,
real* dataIm,
real alpha,
real beta) {}

inline void hl_expand_feature2col(const real* dataIm,
size_t channels,
size_t height,
size_t width,
size_t blockH,
size_t blockW,
size_t strideH,
size_t strideW,
size_t paddingH,
size_t paddingW,
size_t outputH,
size_t outputW,
real* dataCol) {}

inline void hl_maxpool_forward(const int frameCnt,
const real* inputData,
const int channels,
Expand Down
128 changes: 0 additions & 128 deletions paddle/cuda/src/hl_cuda_cnn.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -18,134 +18,6 @@ limitations under the License. */
#include "hl_cnn.h"
#include "hl_device_functions.cuh"

__global__ void KeFeature2col(size_t n, size_t height, const real* data_im,
size_t blockH, size_t blockW, size_t width,
size_t strideH, size_t strideW,
size_t paddingH, size_t paddingW,
size_t height_col, size_t width_col,
real* data_col) {
size_t index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if (index < n) {
size_t w_out = index % width_col;
index /= width_col;
size_t h_out = index % height_col;
size_t channel_in = index / height_col;
size_t channel_out = channel_in * blockH * blockW;
size_t h_in = h_out * strideH;
size_t w_in = w_out * strideW;

data_col += (channel_out * height_col + h_out) * width_col + w_out;
for (size_t i = 0; i < blockH; ++i) {
for (size_t j = 0; j < blockW; ++j) {
int rIdx = int(h_in+i);
int cIdx = int(w_in+j);
if ((rIdx-(int)paddingH) >= (int)height ||
(rIdx-(int)paddingH) < 0 ||
(cIdx-(int)paddingW) >= (int)width ||
(cIdx-(int)paddingW) < 0) {
*data_col = 0;
} else {
rIdx = rIdx + channel_in*height - paddingH;
cIdx = cIdx - paddingW;
*data_col = data_im[rIdx* width + cIdx];
}
data_col += height_col * width_col;
}
}
}
}

void hl_expand_feature2col(const real* dataIm, size_t channels,
size_t height, size_t width,
size_t blockH, size_t blockW,
size_t strideH, size_t strideW,
size_t paddingH, size_t paddingW,
size_t outputH, size_t outputW,
real* dataCol) {
size_t numKernels = channels * outputH * outputW;

size_t blocks = (numKernels + 1024 -1) / 1024;
size_t blockX = 512;
size_t blockY = (blocks+512-1)/512;
dim3 threads(1024, 1);
dim3 grid(blockX, blockY);
KeFeature2col<<< grid, threads, 0, STREAM_DEFAULT >>>
(numKernels, height, dataIm, blockH, blockW, width,
strideH, strideW, paddingH, paddingW,
outputH, outputW, dataCol);
CHECK_SYNC("hl_expand_feature2col failed");
}

__global__ void KeCol2Feature(size_t n, const real* data_col, size_t height,
size_t width, size_t channels,
size_t blockH, size_t blockW,
size_t strideH, size_t strideW,
size_t paddingH, size_t paddingW,
size_t height_col, size_t width_col,
real* data_im, real alpha, real beta) {
size_t index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if (index < n) {
real val = 0;
int w = int(index % width);
int h = int((index / width) % height);
int c = int(index / (width * height));
if ((w - (int)paddingW) >= 0 &&
(w - (int)paddingW) < (width-2 * paddingW) &&
(h - (int)paddingH) >= 0 &&
(h - paddingH) < (height - 2 * paddingH)) {
// compute the start and end of the output
int w_col_start =
(w < (int)blockW) ? 0 : (w - int(blockW)) / (int)strideW + 1;
int w_col_end =
min((int)(w / (int)strideW + 1), (int)(width_col));
int h_col_start =
(h < (int)blockH) ? 0 : (h - (int)blockH) / (int)strideH + 1;
int h_col_end = min(int(h / strideH + 1), int(height_col));
for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
// the col location: [c * width * height + h_out, w_out]
int c_col = int(c * blockH* blockW) + \
(h - h_col * (int)strideH) * (int)blockW +
(w - w_col * (int)strideW);
val += data_col[(c_col * height_col + h_col) * width_col + w_col];
}
}
h -= paddingH;
w -= paddingW;
real tD = data_im[c*((width-2*paddingW) * (height-2*paddingH)) +
h*(width-2*paddingW) + w];
data_im[c*((width-2*paddingW) * (height-2*paddingH)) +
h*(width-2*paddingW) + w] = alpha * val + beta*tD;
}
}
}

void hl_shrink_col2feature(const real * dataCol, size_t channels,
size_t height, size_t width,
size_t blockH, size_t blockW,
size_t strideH, size_t strideW,
size_t paddingH, size_t paddingW,
size_t outputH, size_t outputW,
real* dataIm, real alpha, real beta) {
size_t numKernels = channels * (height + 2*paddingH) * (width + 2*paddingW);

size_t blocks = (numKernels + 1024 -1) / 1024;
size_t blockX = 512;
size_t blockY = (blocks+512-1)/512;
dim3 threads(1024, 1);
dim3 grid(blockX, blockY);

// To avoid involving atomic operations, we will launch one kernel per
// bottom dimension, and then in the kernel add up the top dimensions.
KeCol2Feature<<< grid, threads, 0, STREAM_DEFAULT >>>
(numKernels, dataCol, height + 2*paddingH, width + 2*paddingW,
channels, blockH, blockW, strideH, strideW, paddingH, paddingW,
outputH, outputW, dataIm, alpha, beta);
CHECK_SYNC("hl_shrink_col2feature failed");
}

__global__ void KeMaxPoolForward(const int nthreads, const real* inputData,
const int channels, const int height,
const int width,
Expand Down
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