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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/TemporalMaxPooling.c"
#else
static int nn_(TemporalMaxPooling_updateOutput)(lua_State *L)
{
THTensor *input = luaT_checkudata(L, 2, torch_Tensor);
int kW = luaT_getfieldcheckint(L, 1, "kW");
int dW = luaT_getfieldcheckint(L, 1, "dW");
THTensor *indices = luaT_getfieldcheckudata(L, 1, "indices", torch_Tensor);
THTensor *output = luaT_getfieldcheckudata(L, 1, "output", torch_Tensor);
long niframe;
long framesize;
long noframe;
real *input_data;
real *output_data;
real *indices_data;
long t, y;
luaL_argcheck(L, input->nDimension == 2, 2, "2D tensor expected");
luaL_argcheck(L, input->size[0] >= kW, 2, "input sequence smaller than kernel size");
/* sizes */
niframe = input->size[0];
framesize = input->size[1];
noframe = (niframe - kW) / dW + 1;
/* get contiguous input */
input = THTensor_(newContiguous)(input);
/* resize output */
THTensor_(resize2d)(output, noframe, framesize);
/* indices will contain index locations for each output point */
THTensor_(resize2d)(indices, noframe, framesize);
/* get raw pointers */
input_data = THTensor_(data)(input);
output_data = THTensor_(data)(output);
indices_data = THTensor_(data)(indices);
for(t = 0; t < noframe; t++)
{
real *ip = input_data + t*framesize*dW;
real *op = output_data + t*framesize;
real *xp = indices_data + t*framesize;
#pragma omp parallel for private(y)
for(y = 0; y < framesize; y++)
{
/* compute local max: */
long maxindex = -1;
real maxval = -THInf;
long x;
for(x = 0; x < kW; x++)
{
real val = ip[x*framesize+y];
if (val > maxval)
{
maxval = val;
maxindex = x;
}
}
/* set output to local max */
op[y] = maxval;
xp[y] = (real)maxindex;
}
}
/* cleanup */
THTensor_(free)(input);
return 1;
}
static int nn_(TemporalMaxPooling_updateGradInput)(lua_State *L)
{
THTensor *input = luaT_checkudata(L, 2, torch_Tensor);
THTensor *gradOutput = luaT_checkudata(L, 3, torch_Tensor);
int dW = luaT_getfieldcheckint(L, 1, "dW");
THTensor *indices = luaT_getfieldcheckudata(L, 1, "indices", torch_Tensor);
THTensor *gradInput = luaT_getfieldcheckudata(L, 1, "gradInput", torch_Tensor);
int noframe;
long framesize;
real *gradInput_data;
real *gradOutput_data;
real *indices_data;
long t, y;
/* get contiguous gradOutput */
gradOutput = THTensor_(newContiguous)(gradOutput);
/* resize and zero */
THTensor_(resizeAs)(gradInput, input);
THTensor_(zero)(gradInput);
/* sizes */
noframe = gradOutput->size[0];
framesize = gradOutput->size[1];
/* get raw pointers */
gradInput_data = THTensor_(data)(gradInput);
gradOutput_data = THTensor_(data)(gradOutput);
indices_data = THTensor_(data)(indices);
for(t = 0; t < noframe; t++)
{
real *gip = gradInput_data + t*framesize*dW;
real *gop = gradOutput_data + t*framesize;
real *xp = indices_data + t*framesize;
#pragma omp parallel for private(y)
for(y = 0; y < framesize; y++)
{
/* compute local max: */
long maxindex = (long)xp[y];
gip[maxindex*framesize+y] += gop[y];
}
}
/* cleanup */
THTensor_(free)(gradOutput);
return 1;
}
static const struct luaL_Reg nn_(TemporalMaxPooling__) [] = {
{"TemporalMaxPooling_updateOutput", nn_(TemporalMaxPooling_updateOutput)},
{"TemporalMaxPooling_updateGradInput", nn_(TemporalMaxPooling_updateGradInput)},
{NULL, NULL}
};
static void nn_(TemporalMaxPooling_init)(lua_State *L)
{
luaT_pushmetatable(L, torch_Tensor);
luaT_registeratname(L, nn_(TemporalMaxPooling__), "nn");
lua_pop(L,1);
}
#endif
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