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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/SpatialClassNLLCriterion.c"
#else
#define INITIAL_CHECK \
THArgCheck(THIndexTensor_(nDimension)(target) == 3, 3, \
"only batches of spatial targets supported (3D tensors)" \
" but got targets of dimension: %d", \
THIndexTensor_(nDimension)(target)); \
THArgCheck(THTensor_(nDimension)(input) == 4, 2, \
"only batches of spatial inputs supported (4D tensors), " \
"but got input of dimension: %d", THTensor_(nDimension)(input)); \
if (weights && THTensor_(nElement)(weights) != THTensor_(size)(input, 1)) { \
THError("weight tensor should be defined either for all or no classes"); \
} \
\
{ \
long input0 = THTensor_(size)(input, 0); \
long input1 = THTensor_(size)(input, 1); \
long input2 = THTensor_(size)(input, 2); \
long input3 = THTensor_(size)(input, 3); \
long target0 = THIndexTensor_(size)(target, 0); \
long target1 = THIndexTensor_(size)(target, 1); \
long target2 = THIndexTensor_(size)(target, 2); \
THAssertMsg(input0 == target0 && input2 == target1 && input3 == target2, \
"size mismatch (got input: %ldx%ldx%ldx%ld, target: %ldx%ldx%ld)", \
input0, input1, input2, input3, target0, target1, target2); \
}
void THNN_(SpatialClassNLLCriterion_updateOutput)(
THNNState *state,
THTensor *input,
THIndexTensor *target,
THTensor *output,
bool sizeAverage,
THTensor *weights,
THTensor *total_weight)
{
INITIAL_CHECK;
input = THTensor_(newContiguous)(input);
target = THIndexTensor_(newContiguous)(target);
weights = weights ? THTensor_(newContiguous)(weights) : NULL;
real *input_data = THTensor_(data)(input);
THIndex_t *target_data = THIndexTensor_(data)(target);
real *weights_data = weights ? THTensor_(data)(weights) : NULL;
real *output_data = THTensor_(data)(output);
real *total_weight_data = THTensor_(data)(total_weight);
long batch_size = THTensor_(size)(input, 0);
long n_classes = THTensor_(size)(input, 1);
long map_size = THTensor_(size)(input, 2) * THTensor_(size)(input, 3);
long sample_size = map_size * n_classes;
real total_weight_acc = 0;
real output_acc = 0;
for (int b = 0; b < batch_size; b++) {
for (int elem = 0; elem < map_size; elem++) {
int cur_target = target_data[b * map_size + elem] - TH_INDEX_BASE;
THAssert(cur_target >= 0 && cur_target < n_classes);
real cur_weight = weights ? weights_data[cur_target] : 1.0f;
total_weight_acc += cur_weight;
output_acc -= input_data[b * sample_size + cur_target * map_size + elem] * cur_weight;
}
}
*total_weight_data = total_weight_acc;
*output_data = output_acc;
if (sizeAverage && *total_weight_data)
*output_data /= *total_weight_data;
THTensor_(free)(input);
THIndexTensor_(free)(target);
if (weights)
THTensor_(free)(weights);
}
void THNN_(SpatialClassNLLCriterion_updateGradInput)(
THNNState *state,
THTensor *input,
THIndexTensor *target,
THTensor *gradInput,
bool sizeAverage,
THTensor *weights,
THTensor *total_weight)
{
INITIAL_CHECK;
THArgCheck(THTensor_(isContiguous)(gradInput), 4,
"gradInput must be contiguous");
real *total_weight_data = THTensor_(data)(total_weight);
if (*total_weight_data <= 0)
return;
target = THIndexTensor_(newContiguous)(target);
weights = weights ? THTensor_(newContiguous)(weights) : NULL;
THIndex_t *target_data = THIndexTensor_(data)(target);
real *weights_data = weights ? THTensor_(data)(weights) : NULL;
real *gradInput_data = THTensor_(data)(gradInput);
long batch_size = THTensor_(size)(input, 0);
long n_classes = THTensor_(size)(input, 1);
long map_size = THTensor_(size)(input, 2) * THTensor_(size)(input, 3);
long sample_size = map_size * n_classes;
real normalize = sizeAverage ? *total_weight_data : 1.0f;
int b;
#pragma omp parallel for
for (b = 0; b < batch_size; b++) {
int elem;
for (elem = 0; elem < map_size; elem++) {
int cur_target = target_data[b * map_size + elem] - TH_INDEX_BASE;
THAssert(cur_target >= 0 && cur_target < n_classes);
gradInput_data[b * sample_size + cur_target * map_size + elem] =
-(weights ? weights_data[cur_target] : 1.0f) / normalize;
}
}
THIndexTensor_(free)(target);
if (weights)
THTensor_(free)(weights);
}
#undef INITIAL_CHECK
#endif
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