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#ifndef THC_GENERIC_FILE
#define THC_GENERIC_FILE "generic/ClassNLLCriterion.cu"
#else
void THNN_(ClassNLLCriterion_updateOutput)(
THCState *state,
THCTensor *input,
THCIndexTensor *target,
THCTensor *output,
bool sizeAverage,
THCTensor *weights,
THCTensor *total_weight) {
if (THCIndexTensor_(nDimension)(state, target) > 1) {
THError("multi-target not supported");
}
int n_dims = THCTensor_(nDimension)(state, input);
int n_classes = THCTensor_(size)(state, input, n_dims - 1);
if (weights) {
THCUNN_assertSameGPU_generic(
state, 5, input, target, weights, output, total_weight
);
} else {
THCUNN_assertSameGPU_generic(
state, 4, input, target, output, total_weight
);
}
if (THCTensor_(nDimension)(state, input) > 2) {
THArgCheck(0, 2, "vector or matrix expected");
}
if (weights && THCTensor_(nElement)(state, weights) != n_classes) {
THError("weight tensor should be defined either for all or no classes");
}
input = THCTensor_(newContiguous)(state, input);
weights = weights ? THCTensor_(newContiguous)(state, weights) : NULL;
target = THCIndexTensor_(newContiguous)(state, target);
real *input_data = THCTensor_(data)(state, input);
real *weights_data = weights ? THCTensor_(data)(state, weights) : NULL;
THCIndex_t *target_data = THCIndexTensor_(data)(state, target);
real *output_data = THCTensor_(data)(state, output);
real *total_weight_data = THCTensor_(data)(state, total_weight);
if (THCTensor_(nDimension)(state, input) == 1) {
cunn_ClassNLLCriterion_updateOutput_kernel1<real>
<<<1, 1, 0, THCState_getCurrentStream(state)>>>(
output_data,
total_weight_data,
input_data,
target_data,
weights_data,
sizeAverage,
n_classes
);
} else if (THCTensor_(nDimension)(state, input) == 2) {
cunn_ClassNLLCriterion_updateOutput_kernel<real, accreal>
<<<1, NTHREADS, 0, THCState_getCurrentStream(state)>>>(
output_data,
total_weight_data,
input_data,
target_data,
weights_data,
sizeAverage,
THCTensor_(size)(state, input, 0),
THCTensor_(size)(state, input, 1),
n_classes
);
}
THCudaCheck(cudaGetLastError());
if (weights) {
THCTensor_(free)(state, weights);
}
THCIndexTensor_(free)(state, target);
THCTensor_(free)(state, input);
}
void THNN_(ClassNLLCriterion_updateGradInput)(
THCState *state,
THCTensor *input,
THCIndexTensor *target,
THCTensor *gradInput,
bool sizeAverage,
THCTensor *weights,
THCTensor *total_weight) {
if (THCIndexTensor_(nDimension)(state, target) > 1) {
THError("multi-target not supported");
}
int n_dims = THCTensor_(nDimension)(state, input);
int n_classes = THCTensor_(size)(state, input, n_dims - 1);
THArgCheck(THCTensor_(isContiguous)(state, gradInput), 4, "gradInput must be contiguous");
if (weights) {
THCUNN_assertSameGPU(
state, 5, weights, input, target, gradInput, total_weight
);
}
else {
THCUNN_assertSameGPU(
state, 4, input, target, gradInput, total_weight
);
}
if (THCTensor_(nDimension)(state, input) > 2) {
THArgCheck(0, 2, "vector or matrix expected");
}
if (weights && THCTensor_(nElement)(state, weights) != n_classes) {
THError("weight tensor should be defined either for all or no classes");
}
weights = weights ? THCTensor_(newContiguous)(state, weights) : NULL;
target = THCIndexTensor_(newContiguous)(state, target);
real *weights_data = weights ? THCTensor_(data)(state, weights) : NULL;
real *gradInput_data = THCTensor_(data)(state, gradInput);
THCIndex_t *target_data = THCIndexTensor_(data)(state, target);
real *total_weight_data = THCTensor_(data)(state, total_weight);
if (THCTensor_(nDimension)(state, input) == 1) {
cunn_ClassNLLCriterion_updateGradInput_kernel1<real>
<<<1, 1, 0, THCState_getCurrentStream(state)>>>(
gradInput_data,
weights_data,
target_data,
total_weight_data,
sizeAverage,
n_classes
);
} else {
cunn_ClassNLLCriterion_updateGradInput_kernel<real>
<<<1, NTHREADS, 0, THCState_getCurrentStream(state)>>>(
gradInput_data,
target_data,
weights_data,
total_weight_data,
sizeAverage,
THCTensor_(size)(state, input, 0),
THCTensor_(size)(state, input, 1),
n_classes
);
}
THCudaCheck(cudaGetLastError());
if (weights) {
THCTensor_(free)(state, weights);
}
THCIndexTensor_(free)(state, target);
}
#endif
|
