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SpatialClassNLLCriterion.cu
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SpatialClassNLLCriterion.cu
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#ifndef THC_GENERIC_FILE
#define THC_GENERIC_FILE "THCUNN/generic/SpatialClassNLLCriterion.cu"
#else
void THNN_(SpatialClassNLLCriterion_shapeCheck)(
THCState *state,
THCTensor *input,
THCIndexTensor *target,
THCTensor *weights)
{
TORCH_CHECK(target->dim() == 3, 1,
"only batches of spatial targets supported (3D tensors)" \
" but got targets of size: : ", target->sizes());
TORCH_CHECK(input->dim() == 4, 2,
"only batches of spatial inputs supported (4D tensors), " \
"but got input of size: ", input->sizes());
if (THCTensor_(size)(state, input, 0) != THCIndexTensor_(size)(state, target, 0) ||
THCTensor_(size)(state, input, 2) != THCIndexTensor_(size)(state, target, 1) ||
THCTensor_(size)(state, input, 3) != THCIndexTensor_(size)(state, target, 2)) {
THCDescBuff input_size = THCTensor_(sizeDesc)(state, input);
THCDescBuff target_size = THCIndexTensor_(sizeDesc)(state, target);
THError("input and target batch or spatial sizes don't match: target %s, input %s",
target_size.str, input_size.str);
}
if (weights && THCTensor_(nElement)(state, weights) != THCTensor_(size)(state, input, 1)) {
THError("weight tensor should be defined either for all or no classes");
}
}
static void THNN_(SpatialClassNLLCriterion_gradOutput_no_reduce_shapeCheck)(
THCState *state,
THCTensor *gradOutput,
THCIndexTensor *target)
{
TORCH_CHECK(THCTensor_(nDimensionLegacyNoScalars)(state, gradOutput) == 3, 2,
"gradOutput must have same dimension as target (3) but got dimension: ", gradOutput->sizes());
if (THCTensor_(size)(state, gradOutput, 0) != THCIndexTensor_(size)(state, target, 0) ||
THCTensor_(size)(state, gradOutput, 1) != THCIndexTensor_(size)(state, target, 1) ||
THCTensor_(size)(state, gradOutput, 2) != THCIndexTensor_(size)(state, target, 2)) {
THCDescBuff gradOutput_size = THCTensor_(sizeDesc)(state, gradOutput);
THCDescBuff target_size = THCIndexTensor_(sizeDesc)(state, target);
THError("gradOutput sizes don't match target sizes: target %s, gradOutput %s",
target_size.str, gradOutput_size.str);
}
}
void THNN_(SpatialClassNLLCriterion_updateOutput)(
THCState *state,
THCTensor *input,
THCIndexTensor *target,
THCTensor *output,
int64_t reduction,
THCTensor *weights,
THCTensor *total_weight,
int64_t ignore_index)
{
// See Note [Writing Nondeterministic Operations]
// Nondeterministic because of atomicAdd usage
at::globalContext().alertNotDeterministic("SpatialClassNLLCriterion_updateOutput");
THNN_(SpatialClassNLLCriterion_shapeCheck)(state, input, target, weights);
THCTensor_(resize0d)(state, output);
THCTensor_(resize0d)(state, total_weight);
if (weights)
THCUNN_assertSameGPU(state, 5, input, target, weights, output, total_weight);
else
THCUNN_assertSameGPU(state, 4, input, target, output, total_weight);
if (reduction == at::Reduction::None) {
int64_t batch_size = THCTensor_(size)(state, input, 0);
int64_t H = THCTensor_(size)(state, input, 2);
int64_t W = THCTensor_(size)(state, input, 3);
int64_t count = batch_size * H * W;
THCTensor_(resize3d)(state, output, batch_size, H, W);
if (count == 0) {
// This guards from unnecessary operations and launching CUDA kernel with 0 blocks.
return;
}
if (weights) {
weights = THCTensor_(newContiguous)(state, weights);
}
SpatialClassNLLCriterion_updateOutput_no_reduce_kernel<scalar_t>
<<<GET_BLOCKS(count), CUDA_NUM_THREADS, 0, c10::cuda::getCurrentCUDAStream()>>>(
count,
toDeviceTensor<scalar_t, 4>(state, input),
toDeviceTensor<THCIndex_t, 3>(state, target),
toDeviceTensor<scalar_t, 3>(state, output),
weights ? THCTensor_(data)(state, weights) : NULL,
ignore_index);
if (weights) {
THCTensor_(free)(state, weights);
}
return;
}
input = THCTensor_(newContiguous)(state, input);
weights = weights ? THCTensor_(newContiguous)(state, weights) : NULL;
target = THCIndexTensor_(newContiguous)(state, target);
scalar_t *input_data = THCTensor_(data)(state, input);
scalar_t *weights_data = weights ? THCTensor_(data)(state, weights) : NULL;
THCIndex_t *target_data = THCIndexTensor_(data)(state, target);
scalar_t *output_data = THCTensor_(data)(state, output);
scalar_t *total_weight_data = THCTensor_(data)(state, total_weight);
THCTensor_(fill)(state, output, ScalarConvert<int, scalar_t>::to(0));
THCTensor_(fill)(state, total_weight, ScalarConvert<int, scalar_t>::to(0));
THCIndex_t batch_size = THCIndexTensor_(size)(state, target, 0);
if (batch_size != 0) { // This guards from unnecessary operations and launching CUDA kernel with 0 blocks.
THCIndex_t map_nelem = THCIndexTensor_(nElement)(state, target) / batch_size;
int blocks_per_sample = GET_BLOCKS(map_nelem) / 128;
blocks_per_sample = (blocks_per_sample == 0) ? 1 : blocks_per_sample;
int total_blocks = blocks_per_sample * batch_size;
cunn_SpatialClassNLLCriterion_updateOutput_kernel<scalar_t, accreal>
<<<total_blocks, CUDA_NUM_THREADS, 0, c10::cuda::getCurrentCUDAStream()>>>(
output_data,
total_weight_data,
input_data,
target_data,
weights_data,
reduction == at::Reduction::Mean,
THCTensor_(size)(state, input, 0),
THCTensor_(size)(state, input, 1),
THCTensor_(size)(state, input, 2) * THCTensor_(size)(state, input, 3),
blocks_per_sample,
ignore_index
);
THCudaCheck(cudaGetLastError());
}
if (reduction == at::Reduction::Mean) {
cunn_SpatialClassNLLCriterion_sizeAverage_kernel<<<1, 1, 0, c10::cuda::getCurrentCUDAStream()>>>(
output_data, total_weight_data, THCTensor_(nElement)(state, input)
);
THCudaCheck(cudaGetLastError());
}
if (weights)
THCTensor_(free)(state, weights);
THCIndexTensor_(free)(state, target);
THCTensor_(free)(state, input);
}
void THNN_(SpatialClassNLLCriterion_updateGradInput)(
THCState *state,
THCTensor *input,
THCIndexTensor *target,
THCTensor *gradOutput,
THCTensor *gradInput,
int64_t reduction,
THCTensor *weights,
THCTensor *total_weight,
int64_t ignore_index)
{
THNN_(SpatialClassNLLCriterion_shapeCheck)(state, input, target, weights);
THCTensor_(resizeAs)(state, gradInput, input);
THCTensor_(zero)(state, gradInput);
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 (reduction == at::Reduction::None) {
THNN_(SpatialClassNLLCriterion_gradOutput_no_reduce_shapeCheck)(
state,
gradOutput,
target);
int64_t batch_size = THCTensor_(size)(state, input, 0);
int64_t H = THCTensor_(size)(state, input, 2);
int64_t W = THCTensor_(size)(state, input, 3);
int64_t count = batch_size * H * W;
if (count == 0) {
// This guards from unnecessary operations and launching CUDA kernel with 0 blocks.
return;
}
if (weights) {
weights = THCTensor_(newContiguous)(state, weights);
}
SpatialClassNLLCriterion_updateGradInput_no_reduce_kernel<scalar_t>
<<<GET_BLOCKS(count), CUDA_NUM_THREADS, 0, c10::cuda::getCurrentCUDAStream()>>>(
count,
toDeviceTensor<THCIndex_t, 3>(state, target),
toDeviceTensor<scalar_t, 3>(state, gradOutput),
toDeviceTensor<scalar_t, 4>(state, gradInput),
weights ? THCTensor_(data)(state, weights) : NULL,
ignore_index);
if (weights) {
THCTensor_(free)(state, weights);
}
return;
}
input = THCTensor_(newContiguous)(state, input);
weights = weights ? THCTensor_(newContiguous)(state, weights) : NULL;
target = THCIndexTensor_(newContiguous)(state, target);
scalar_t *gradOutput_data = THCTensor_(data)(state, gradOutput);
scalar_t *weights_data = weights ? THCTensor_(data)(state, weights) : NULL;
scalar_t *gradInput_data = THCTensor_(data)(state, gradInput);
THCIndex_t *target_data = THCIndexTensor_(data)(state, target);
scalar_t *total_weight_data = THCTensor_(data)(state, total_weight);
THCIndex_t batch_size = THCIndexTensor_(size)(state, target, 0);
if (batch_size != 0) { // This guards from unnecessary operations and launching CUDA kernel with 0 blocks.
THCIndex_t map_nelem = THCIndexTensor_(nElement)(state, target) / batch_size;
int blocks_per_sample = GET_BLOCKS(map_nelem) / 128;
blocks_per_sample = (blocks_per_sample == 0) ? 1 : blocks_per_sample;
int total_blocks = blocks_per_sample * batch_size;
cunn_SpatialClassNLLCriterion_updateGradInput_kernel
<<<total_blocks, CUDA_NUM_THREADS, 0, c10::cuda::getCurrentCUDAStream()>>>(
gradInput_data,
gradOutput_data,
target_data,
weights_data,
total_weight_data,
reduction == at::Reduction::Mean,
THCTensor_(size)(state, input, 0),
THCTensor_(size)(state, input, 1),
THCTensor_(size)(state, input, 2) *THCTensor_(size)(state, input, 3),
blocks_per_sample,
ignore_index
);
THCudaCheck(cudaGetLastError());
}
if (weights)
THCTensor_(free)(state, weights);
THCIndexTensor_(free)(state, target);
THCTensor_(free)(state, input);
}
#endif