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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/SpatialAdaptiveAveragePooling.c"
#else
#define START_IND(a,b,c) (int)floor((float)(a * c) / b)
#define END_IND(a,b,c) (int)ceil((float)((a + 1) * c) / b)
// #define START_IND(a,b,c) a * c / b
// #define END_IND(a,b,c) (a + 1) * c / b + ((a + 1) * c % b > 0)?1:0
static void THNN_(SpatialAdaptiveAveragePooling_updateOutput_frame)(
real *input_p,
real *output_p,
long nslices,
long iwidth,
long iheight,
long owidth,
long oheight,
long stridew,
long strideh,
long strided)
{
long k;
#pragma omp parallel for private(k)
for (k = 0; k < nslices; k++)
{
/* loop over output */
long i, j;
for(i = 0; i < oheight; i++)
{
int y_start = START_IND(i, oheight, iheight);
int y_end = END_IND(i, oheight, iheight);
int kH = y_end-y_start;
for(j = 0; j < owidth; j++)
{
int x_start = START_IND(j, owidth, iwidth);
int x_end = END_IND(j, owidth, iwidth);
int kW = x_end-x_start;
/* local pointers */
real *ip = input_p + k*strided + y_start*strideh + x_start*stridew;
real *op = output_p + k*owidth*oheight + i*owidth + j;
/* compute local average: */
real sum = 0;
int x,y;
for(y = 0; y < kH; y++)
{
for(x = 0; x < kW; x++)
{
real val = *(ip + y*strideh + x*stridew);
sum += val;
}
}
/* set output to local average */
*op = sum / kW / kH;
}
}
}
}
void THNN_(SpatialAdaptiveAveragePooling_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output,
int owidth,
int oheight)
{
int dimw = 2;
int dimh = 1;
long nbatch = 1;
long nslices;
long iheight;
long iwidth;
long istride_d;
long istride_h;
long istride_w;
long istride_b;
real *input_data;
real *output_data;
THNN_ARGCHECK(input->nDimension == 3 || input->nDimension == 4, 2, input,
"3D or 4D (batch mode) tensor expected for input, but got: %s");
if (input->nDimension == 4)
{
istride_b = input->stride[0];
nbatch = input->size[0];
dimw++;
dimh++;
}
/* sizes */
nslices = input->size[dimh-1];
iheight = input->size[dimh];
iwidth = input->size[dimw];
/* strides */
istride_d = input->stride[dimh-1];
istride_h = input->stride[dimh];
istride_w = input->stride[dimw];
/* resize output */
if (input->nDimension == 3)
{
THTensor_(resize3d)(output, nslices, oheight, owidth);
input_data = THTensor_(data)(input);
output_data = THTensor_(data)(output);
THNN_(SpatialAdaptiveAveragePooling_updateOutput_frame)(input_data, output_data,
nslices,
iwidth, iheight,
owidth, oheight,
istride_w,istride_h,
istride_d);
}
else
{
long p;
THTensor_(resize4d)(output, nbatch, nslices, oheight, owidth);
input_data = THTensor_(data)(input);
output_data = THTensor_(data)(output);
#pragma omp parallel for private(p)
for (p = 0; p < nbatch; p++)
{
THNN_(SpatialAdaptiveAveragePooling_updateOutput_frame)(input_data+p*istride_b, output_data+p*nslices*owidth*oheight,
nslices,
iwidth, iheight,
owidth, oheight,
istride_w,istride_h,
istride_d);
}
}
}
static void THNN_(SpatialAdaptiveAveragePooling_updateGradInput_frame)(
real *gradInput_p,
real *gradOutput_p,
long nslices,
long iwidth,
long iheight,
long owidth,
long oheight)
{
long k;
#pragma omp parallel for private(k)
for (k = 0; k < nslices; k++)
{
real *gradInput_p_k = gradInput_p + k*iwidth*iheight;
real *gradOutput_p_k = gradOutput_p + k*owidth*oheight;
/* calculate average */
long i, j;
for(i = 0; i < oheight; i++)
{
int y_start = START_IND(i, oheight, iheight);
int y_end = END_IND(i, oheight, iheight);
int kH = y_end-y_start;
for(j = 0; j < owidth; j++)
{
int x_start = START_IND(j, owidth, iwidth);
int x_end = END_IND(j, owidth, iwidth);
int kW = x_end-x_start;
int x,y;
for(y = y_start; y < y_end; y++)
{
for(x = x_start; x < x_end; x++)
{
/* update gradient */
gradInput_p_k[y*iwidth + x] += gradOutput_p_k[i*owidth + j] / kW / kH;
}
}
}
}
}
}
void THNN_(SpatialAdaptiveAveragePooling_updateGradInput)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradInput)
{
int dimw = 2;
int dimh = 1;
long nbatch = 1;
int nslices;
int iheight;
int iwidth;
int oheight;
int owidth;
real *gradInput_data;
real *gradOutput_data;
/* get contiguous gradOutput */
gradOutput = THTensor_(newContiguous)(gradOutput);
/* resize */
THTensor_(resizeAs)(gradInput, input);
THTensor_(zero)(gradInput);
if (input->nDimension == 4) {
nbatch = input->size[0];
dimw++;
dimh++;
}
/* sizes */
nslices = input->size[dimh-1];
iheight = input->size[dimh];
iwidth = input->size[dimw];
oheight = gradOutput->size[dimh];
owidth = gradOutput->size[dimw];
/* get raw pointers */
gradInput_data = THTensor_(data)(gradInput);
gradOutput_data = THTensor_(data)(gradOutput);
/* backprop */
if (input->nDimension == 3)
{
THNN_(SpatialAdaptiveAveragePooling_updateGradInput_frame)(gradInput_data, gradOutput_data,
nslices,
iwidth, iheight,
owidth, oheight);
}
else
{
long p;
#pragma omp parallel for private(p)
for (p = 0; p < nbatch; p++)
{
THNN_(SpatialAdaptiveAveragePooling_updateGradInput_frame)(gradInput_data+p*nslices*iwidth*iheight, gradOutput_data+p*nslices*owidth*oheight,
nslices,
iwidth, iheight,
owidth, oheight);
}
}
/* cleanup */
THTensor_(free)(gradOutput);
}
#endif
#undef START_IND
#undef END_IND
|
