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fixed the bugs in cuda SpatialConvolutionMap, works for equal strides

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This commit is contained in:
Soumith Chintala
2012-08-24 11:13:11 -04:00
parent 43fead371b
commit f179fe6d8a
1 changed files with 15 additions and 14 deletions
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29
THCTensorConv.cu
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@ -907,6 +907,7 @@ TH_API void THCudaTensor_conv2DRevgerm(THCudaTensor *output, float beta, float a
///////////////////////////////////
///// ConvolutionMap
#define CUDA_SHARED_MEM_SIZE 0
/*
* Description:
* base conv2D routine: 3D input, 3D output, 4D kernel
@ -977,11 +978,11 @@ TH_API void THCudaTensor_conv2DRevgerm(THCudaTensor *output, float beta, float a
for(yy = yy_start; yy < yy_end; yy+=yy_step) {
for(xx = xx_start; xx < xx_end; xx+=xx_step) {
// Dot product in two dimensions... (between input image and the mask)
float *input_p = input + ((long)table[ii])*input_h*input_w
float *input_p = input + ((long)table[ii]-1)*input_h*input_w
+ yy*stride_h*input_w + xx*stride_w;
float *output_p = output + oo*output_h*output_w + yy*output_w + xx;
float *kernel_p = shared_kernel
+ ((long)table[ii + 1]) *kernel_w*kernel_h + koffset;
+ ((long)table[ii + 1]-1) *kernel_w*kernel_h + koffset;
float sum = 0;
if (swapkernel) {
#pragma unroll
@ -1014,12 +1015,12 @@ TH_API void THCudaTensor_conv2DRevgerm(THCudaTensor *output, float beta, float a
for(yy = yy_start; yy < yy_end; yy+=yy_step) {
for(xx = xx_start; xx < xx_end; xx+=xx_step) {
// Dot product in two dims (between input image and the mask)
float *input_p = input + ((long)table[ii])*input_h*input_w
float *input_p = input + ((long)table[ii]-1)*input_h*input_w
+ yy*stride_h*input_w + xx*stride_w;
float *output_p = output + oo*output_h*output_w + yy*output_w
+ xx;
float *kernel_p = shared_kernel
+ (((long)table[ii]) % fanin) * kernel_w * kernel_h + koffset;
+ (((long)table[ii]-1) % fanin) * kernel_w * kernel_h + koffset;
float sum = 0;
if (swapkernel) {
for(ky = 0; ky < kernel_h; ky++) {
@ -1053,10 +1054,10 @@ TH_API void THCudaTensor_conv2DRevgerm(THCudaTensor *output, float beta, float a
for(yy = yy_start; yy < yy_end; yy+=yy_step) {
for(xx = xx_start; xx < xx_end; xx+=xx_step) {
// Dot product in two dimensions... (between input image and the mask)
float *input_p = input + ((long)table[ii])*input_h*input_w
float *input_p = input + ((long)table[ii]-1)*input_h*input_w
+ yy*stride_h*input_w + xx*stride_w;
float *output_p = output + oo*output_h*output_w + yy*output_w + xx;
float *kernel_p = kernel + ((long)table[ii + 1]) *kernel_w*kernel_h + koffset;
float *kernel_p = kernel + ((long)table[ii + 1]-1) *kernel_w*kernel_h + koffset;
float sum = 0;
if (swapkernel) {
for(ky = 0; ky < kernel_h; ky++) {
@ -1084,7 +1085,6 @@ TH_API void THCudaTensor_conv2DRevgerm(THCudaTensor *output, float beta, float a
}
/*
* API-compatible with THRealTensor_conv2Dmv
* 3D input, 4D kernel, 3D output
@ -1098,28 +1098,29 @@ TH_API void THCudaTensor_conv2Dmap(THCudaTensor *output, THCudaTensor *input,
long nKernelRows, nKernelCols;
long nOutputPlane, nOutputRows, nOutputCols;
THArgCheck(kernel->nDimension == 4 , 4, "kernel: 4D Tensor expected");
THArgCheck(kernel->nDimension == 3 , 4, "kernel: 3D Tensor expected");
THArgCheck(stride_x >= 1, 5, "Stride should be a positive integer");
THArgCheck(stride_y >= 1, 6, "Stride should be a positive integer");
input = THCudaTensor_newContiguous(input);
kernel = THCudaTensor_newContiguous(kernel);
table = THCudaTensor_newContiguous(table);
nInputPlane = input->size[0];
nInputRows = input->size[1];
nInputCols = input->size[2];
nKernelRows = kernel->size[2];
nKernelCols = kernel->size[3];
nOutputPlane = kernel->size[0];
THArgCheck(kernel->size[1] == nInputPlane, 2, "invalid number of input planes");
nKernelRows = kernel->size[1];
nKernelCols = kernel->size[2];
nOutputPlane = kernel->size[0] / fanin;
// THArgCheck(kernel->size[1] == nInputPlane, 2, "invalid number of input planes");
THArgCheck( (nInputRows >= nKernelRows && nInputCols >= nKernelCols), 2,
"conv2Dmap : Input image is smaller than kernel");
// output dims
nOutputRows = (nInputRows - nKernelRows) / stride_x + 1;
nOutputCols = (nInputCols - nKernelCols) / stride_y + 1;
nOutputRows = (nInputRows - nKernelRows) / stride_y + 1;
nOutputCols = (nInputCols - nKernelCols) / stride_x + 1;
// long nelem = THCudaTensor_nElement(output);
THCudaTensor_resize3d(output, nOutputPlane, nOutputRows, nOutputCols);