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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/SpatialMaxSampling.c"
#else
#ifndef MAX
#define MAX(a,b) ( ((a)>(b)) ? (a) : (b) )
#endif
#ifndef MIN
#define MIN(a,b) ( ((a)<(b)) ? (a) : (b) )
#endif
static int nn_(SpatialMaxSampling_updateOutput)(lua_State *L)
{
// get all params
THTensor *input = luaT_checkudata(L, 2, torch_Tensor);
int owidth = luaT_getfieldcheckint(L, 1, "owidth");
int oheight = luaT_getfieldcheckint(L, 1, "oheight");
THTensor *output = luaT_getfieldcheckudata(L, 1, "output", torch_Tensor);
THTensor *indices = luaT_getfieldcheckudata(L, 1, "indices", torch_Tensor);
// check dims
luaL_argcheck(L, input->nDimension == 3, 2, "3D tensor expected");
// dims
int ichannels = input->size[0];
int iheight = input->size[1];
int iwidth = input->size[2];
int ochannels = ichannels;
float dW = (float)iwidth/owidth;
float dH = (float)iheight/oheight;
// get contiguous input
input = THTensor_(newContiguous)(input);
// resize output
THTensor_(resize3d)(output, ochannels, oheight, owidth);
// indices will contain i,j locations for each output point
THTensor_(resize4d)(indices, 2, ochannels, oheight, owidth);
// get raw pointers
real *input_data = THTensor_(data)(input);
real *output_data = THTensor_(data)(output);
real *indices_data = THTensor_(data)(indices);
// compute max pooling for each input slice
long k;
for (k = 0; k < ochannels; k++) {
// pointers to slices
real *input_p = input_data + k*iwidth*iheight;
real *output_p = output_data + k*owidth*oheight;
real *indy_p = indices_data + k*owidth*oheight;
real *indx_p = indices_data + (k+ochannels)*owidth*oheight;
// loop over output
int i,j;
for(i = 0; i < oheight; i++) {
for(j = 0; j < owidth; j++) {
// compute nearest offsets
long ixs = (long)(j*dW);
long iys = (long)(i*dH);
long ixe = MAX(ixs+1, (long)((j+1)*dW));
long iye = MAX(iys+1, (long)((i+1)*dH));
// local pointers
real *op = output_p + i*owidth + j;
real *indxp = indx_p + i*owidth + j;
real *indyp = indy_p + i*owidth + j;
// compute local max:
long maxindex = -1;
real maxval = -THInf;
long tcntr = 0;
int x,y;
for(y = iys; y < iye; y++) {
for(x = ixs; x < ixe; x++) {
real val = *(input_p + y*iwidth + x);
if (val > maxval) {
maxval = val;
maxindex = tcntr;
}
tcntr++;
}
}
// set output to local max
*op = maxval;
// store location of max (x,y)
long kW = ixe-ixs;
*indyp = (int)(maxindex / kW)+1;
*indxp = (maxindex % kW) +1;
}
}
}
// cleanup
THTensor_(free)(input);
return 1;
}
static int nn_(SpatialMaxSampling_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);
THTensor *indices = luaT_getfieldcheckudata(L, 1, "indices", torch_Tensor);
int owidth = luaT_getfieldcheckint(L, 1, "owidth");
int oheight = luaT_getfieldcheckint(L, 1, "oheight");
// sizes
int ichannels = input->size[0];
int iheight = input->size[1];
int iwidth = input->size[2];
int ochannels = ichannels;
float dW = (float)iwidth/owidth;
float dH = (float)iheight/oheight;
// get contiguous gradOutput
gradOutput = THTensor_(newContiguous)(gradOutput);
// resize input
THTensor_(resizeAs)(gradInput, input);
THTensor_(zero)(gradInput);
// get raw pointers
real *gradInput_data = THTensor_(data)(gradInput);
real *gradOutput_data = THTensor_(data)(gradOutput);
real *indices_data = THTensor_(data)(indices);
// backprop all
long k;
for (k = 0; k < ichannels; k++) {
// pointers to slices
real *gradOutput_p = gradOutput_data + k*owidth*oheight;
real *gradInput_p = gradInput_data + k*iwidth*iheight;
real *indy_p = indices_data + k*owidth*oheight;
real *indx_p = indices_data + (k+ochannels)*owidth*oheight;
// calculate max points
int i,j;
for(i = 0; i < oheight; i++) {
for(j = 0; j < owidth; j++) {
// compute nearest offsets
long iys = (long)(i*dH);
long ixs = (long)(j*dW);
// retrieve position of max
real *indyp = indy_p + i*owidth + j;
real *indxp = indx_p + i*owidth + j;
long maxi = (*indyp) - 1 + iys;
long maxj = (*indxp) - 1 + ixs;
// update gradient
*(gradInput_p + maxi*iwidth + maxj) += *(gradOutput_p + i*owidth + j);
}
}
}
// cleanup
THTensor_(free)(gradOutput);
return 1;
}
static const struct luaL_Reg nn_(SpatialMaxSampling__) [] = {
{"SpatialMaxSampling_updateOutput", nn_(SpatialMaxSampling_updateOutput)},
{"SpatialMaxSampling_updateGradInput", nn_(SpatialMaxSampling_updateGradInput)},
{NULL, NULL}
};
static void nn_(SpatialMaxSampling_init)(lua_State *L)
{
luaT_pushmetatable(L, torch_Tensor);
luaT_registeratname(L, nn_(SpatialMaxSampling__), "nn");
lua_pop(L,1);
}
#endif
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