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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/SpatialUpSampling.c"
#else
static int nn_(SpatialUpSampling_updateOutput)(lua_State *L)
{
// get all params
THTensor *input = luaT_checkudata(L, 2, torch_Tensor);
int dW = luaT_getfieldcheckint(L, 1, "dW");
int dH = luaT_getfieldcheckint(L, 1, "dH");
THTensor *output = luaT_getfieldcheckudata(L, 1, "output", torch_Tensor);
// dims
int iwidth = input->size[2];
int iheight = input->size[1];
int owidth = iwidth * dW;
int oheight = iheight * dH;
int channels1 = input->size[0];
int channels2 = input->size[3];
// get strides
long *is = input->stride;
long *os = output->stride;
// get raw pointers
real *input_data = THTensor_(data)(input);
real *output_data = THTensor_(data)(output);
// resample each plane
int k1, k2, x, y;
for (k1 = 0; k1 < channels1; k1++) {
for (k2 = 0; k2 < channels2; k2++) {
// get planes
real *input_p = input_data + k1*is[0] + k2*is[3];
real *output_p = output_data + k1*os[0] + k2*os[3];
// for each plane, resample
for (y=0; y<oheight; y++) {
for (x=0; x<owidth; x++) {
// input positions (floored)
int ix = x/dW;
int iy = y/dH;
// set output
output_p[y*os[1] + x*os[2]] = input_p[iy*is[1] + ix*is[2]];
}
}
}
}
return 1;
}
static int nn_(SpatialUpSampling_updateGradInput)(lua_State *L)
{
// get all params
//THTensor *input = luaT_checkudata(L, 2, torch_Tensor);
THTensor *gradOutput = luaT_checkudata(L, 3, torch_Tensor);
THTensor *gradInput = luaT_getfieldcheckudata(L, 1, "gradInput", torch_Tensor);
int dW = luaT_getfieldcheckint(L, 1, "dW");
int dH = luaT_getfieldcheckint(L, 1, "dH");
// dims
int owidth = gradOutput->size[2];
int oheight = gradOutput->size[1];
int channels1 = gradOutput->size[0];
int channels2 = gradOutput->size[3];
// resize gradInput
THTensor_(zero)(gradInput);
// get strides
long *gis = gradInput->stride;
long *gos = gradOutput->stride;
// get raw pointers
real *gradInput_data = THTensor_(data)(gradInput);
real *gradOutput_data = THTensor_(data)(gradOutput);
// compute gradients for each plane
int k1, k2, x, y;
for (k1 = 0; k1 < channels1; k1++) {
for (k2 = 0; k2 < channels2; k2++) {
// get planes
real *gradInput_p = gradInput_data + k1*gis[0] + k2*gis[3];
real *gradOutput_p = gradOutput_data + k1*gos[0] + k2*gos[3];
// for each plane, resample
for (y=0; y<oheight; y++) {
for (x=0; x<owidth; x++) {
// input positions (floored)
int ix = x/dW;
int iy = y/dH;
// accumulate gradient
gradInput_p[iy*gis[1] + ix*gis[2]] += gradOutput_p[y*gos[1] + x*gos[2]];
}
}
}
}
return 1;
}
static const struct luaL_Reg nn_(SpatialUpSampling__) [] = {
{"SpatialUpSampling_updateOutput", nn_(SpatialUpSampling_updateOutput)},
{"SpatialUpSampling_updateGradInput", nn_(SpatialUpSampling_updateGradInput)},
{NULL, NULL}
};
static void nn_(SpatialUpSampling_init)(lua_State *L)
{
luaT_pushmetatable(L, torch_Tensor);
luaT_registeratname(L, nn_(SpatialUpSampling__), "nn");
lua_pop(L,1);
}
#endif
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