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Unverified Commit 2082f213 authored by Ben Graham's avatar Ben Graham Committed by GitHub
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Merge pull request #118 from facebookresearch/references 

Use references where possible
parents 1171aae3 d8c8a060
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Showing with 948 additions and 937 deletions
sparseconvnet/SCN/CUDA/IOLayers.cpp
View file @ 2082f213
...@@ -16,10 +16,10 @@ void InputLayer_bp(T *d_input_features, T *d_output_features, Int nRows, ...@@ -16,10 +16,10 @@ void InputLayer_bp(T *d_input_features, T *d_output_features, Int nRows,
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_InputLayer_updateOutput(Metadata<Dimension> &m, void cuda_InputLayer_updateOutput(Metadata<Dimension> &m,
/*long*/ at::Tensor spatialSize, /*long*/ at::Tensor &spatialSize,
/*long*/ at::Tensor input_coords, /*long*/ at::Tensor &input_coords,
/*cuda float*/ at::Tensor input_features, /*cuda float*/ at::Tensor &input_features,
/*cuda float*/ at::Tensor output_features, /*cuda float*/ at::Tensor &output_features,
long batchSize, long mode) { long batchSize, long mode) {
m.inputLayer(spatialSize, input_coords, batchSize, mode); m.inputLayer(spatialSize, input_coords, batchSize, mode);
...@@ -44,8 +44,8 @@ void cuda_InputLayer_updateOutput(Metadata<Dimension> &m, ...@@ -44,8 +44,8 @@ void cuda_InputLayer_updateOutput(Metadata<Dimension> &m,
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_InputLayer_updateGradInput( void cuda_InputLayer_updateGradInput(
Metadata<Dimension> &m, Metadata<Dimension> &m,
/*cuda float*/ at::Tensor d_input_features, /*cuda float*/ at::Tensor &d_input_features,
/*cuda float*/ at::Tensor d_output_features) { /*cuda float*/ at::Tensor &d_output_features) {
auto &rules = m.inputLayerRuleBook; auto &rules = m.inputLayerRuleBook;
Int nPlanes = d_output_features.size(1); Int nPlanes = d_output_features.size(1);
...@@ -69,8 +69,8 @@ void cuda_InputLayer_updateGradInput( ...@@ -69,8 +69,8 @@ void cuda_InputLayer_updateGradInput(
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_OutputLayer_updateOutput(Metadata<Dimension> &m, void cuda_OutputLayer_updateOutput(Metadata<Dimension> &m,
/*cuda float*/ at::Tensor input_features, /*cuda float*/ at::Tensor &input_features,
/*cuda float*/ at::Tensor output_features) { /*cuda float*/ at::Tensor &output_features) {
auto &rules = m.inputLayerRuleBook; auto &rules = m.inputLayerRuleBook;
Int nPlanes = input_features.size(1); Int nPlanes = input_features.size(1);
...@@ -93,8 +93,8 @@ void cuda_OutputLayer_updateOutput(Metadata<Dimension> &m, ...@@ -93,8 +93,8 @@ void cuda_OutputLayer_updateOutput(Metadata<Dimension> &m,
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_OutputLayer_updateGradInput( void cuda_OutputLayer_updateGradInput(
Metadata<Dimension> &m, Metadata<Dimension> &m,
/*cuda float*/ at::Tensor d_input_features, /*cuda float*/ at::Tensor &d_input_features,
/*cuda float*/ at::Tensor d_output_features) { /*cuda float*/ at::Tensor &d_output_features) {
auto &rules = m.inputLayerRuleBook; auto &rules = m.inputLayerRuleBook;
Int nPlanes = d_output_features.size(1); Int nPlanes = d_output_features.size(1);
...@@ -118,10 +118,10 @@ void cuda_OutputLayer_updateGradInput( ...@@ -118,10 +118,10 @@ void cuda_OutputLayer_updateGradInput(
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_BLInputLayer_updateOutput(Metadata<Dimension> &m, void cuda_BLInputLayer_updateOutput(Metadata<Dimension> &m,
/*long*/ at::Tensor spatialSize, /*long*/ at::Tensor &spatialSize,
/*long*/ at::Tensor input_coords, /*long*/ at::Tensor &input_coords,
/*cuda float*/ at::Tensor input_features, /*cuda float*/ at::Tensor &input_features,
/*cuda float*/ at::Tensor output_features, /*cuda float*/ at::Tensor &output_features,
long mode) { long mode) {
m.blLayer(spatialSize, input_coords, mode); m.blLayer(spatialSize, input_coords, mode);
...@@ -148,8 +148,8 @@ void cuda_BLInputLayer_updateOutput(Metadata<Dimension> &m, ...@@ -148,8 +148,8 @@ void cuda_BLInputLayer_updateOutput(Metadata<Dimension> &m,
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_BLInputLayer_updateGradInput( void cuda_BLInputLayer_updateGradInput(
Metadata<Dimension> &m, Metadata<Dimension> &m,
/*cuda float*/ at::Tensor d_input_features, /*cuda float*/ at::Tensor &d_input_features,
/*cuda float*/ at::Tensor d_output_features) { /*cuda float*/ at::Tensor &d_output_features) {
auto &rules = m.blLayerRuleBook; auto &rules = m.blLayerRuleBook;
Int nPlanes = d_output_features.size(1); Int nPlanes = d_output_features.size(1);
...@@ -176,8 +176,8 @@ void cuda_BLInputLayer_updateGradInput( ...@@ -176,8 +176,8 @@ void cuda_BLInputLayer_updateGradInput(
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_BLOutputLayer_updateOutput( void cuda_BLOutputLayer_updateOutput(
Metadata<Dimension> &m, Metadata<Dimension> &m,
/*cuda float*/ at::Tensor input_features, /*cuda float*/ at::Tensor &input_features,
/*cuda float*/ at::Tensor output_features) { /*cuda float*/ at::Tensor &output_features) {
auto &rules = m.blLayerRuleBook; auto &rules = m.blLayerRuleBook;
Int nPlanes = input_features.size(1); Int nPlanes = input_features.size(1);
...@@ -201,8 +201,8 @@ void cuda_BLOutputLayer_updateOutput( ...@@ -201,8 +201,8 @@ void cuda_BLOutputLayer_updateOutput(
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_BLOutputLayer_updateGradInput( void cuda_BLOutputLayer_updateGradInput(
Metadata<Dimension> &m, Metadata<Dimension> &m,
/*cuda float*/ at::Tensor d_input_features, /*cuda float*/ at::Tensor &d_input_features,
/*cuda float*/ at::Tensor d_output_features) { /*cuda float*/ at::Tensor &d_output_features) {
auto &rules = m.blLayerRuleBook; auto &rules = m.blLayerRuleBook;
Int nPlanes = d_output_features.size(2); Int nPlanes = d_output_features.size(2);
......
sparseconvnet/SCN/CUDA/LeakyReLU.cpp
View file @ 2082f213
...@@ -11,8 +11,8 @@ void LeakyReLU_bp(T *input_features, T *d_input_features, T *output_features, ...@@ -11,8 +11,8 @@ void LeakyReLU_bp(T *input_features, T *d_input_features, T *output_features,
Int n, T alpha); Int n, T alpha);
template <typename T> template <typename T>
void cuda_LeakyReLU_updateOutput(/*cuda float*/ at::Tensor input_features, void cuda_LeakyReLU_updateOutput(/*cuda float*/ at::Tensor &input_features,
/*cuda float*/ at::Tensor output_features, /*cuda float*/ at::Tensor &output_features,
T alpha) { T alpha) {
output_features.resize_as_(input_features); output_features.resize_as_(input_features);
auto n = input_features.numel(); auto n = input_features.numel();
...@@ -22,9 +22,9 @@ void cuda_LeakyReLU_updateOutput(/*cuda float*/ at::Tensor input_features, ...@@ -22,9 +22,9 @@ void cuda_LeakyReLU_updateOutput(/*cuda float*/ at::Tensor input_features,
template <typename T> template <typename T>
void cuda_LeakyReLU_updateGradInput( void cuda_LeakyReLU_updateGradInput(
/*cuda float*/ at::Tensor input_features, /*cuda float*/ at::Tensor &input_features,
/*cuda float*/ at::Tensor d_input_features, /*cuda float*/ at::Tensor &d_input_features,
/*cuda float*/ at::Tensor d_output_features, T alpha) { /*cuda float*/ at::Tensor &d_output_features, T alpha) {
d_input_features.resize_as_(d_output_features); d_input_features.resize_as_(d_output_features);
auto n = d_input_features.numel(); auto n = d_input_features.numel();
LeakyReLU_bp<T>(input_features.data<T>(), d_input_features.data<T>(), LeakyReLU_bp<T>(input_features.data<T>(), d_input_features.data<T>(),
......
sparseconvnet/SCN/CUDA/MaxPooling.cpp
View file @ 2082f213
...@@ -16,14 +16,14 @@ void cuda_MaxPooling_BackwardPass(T *input_features, T *d_input_features, ...@@ -16,14 +16,14 @@ void cuda_MaxPooling_BackwardPass(T *input_features, T *d_input_features,
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_MaxPooling_updateOutput( void cuda_MaxPooling_updateOutput(
/*long*/ at::Tensor inputSize, /*long*/ at::Tensor outputSize, /*long*/ at::Tensor &inputSize, /*long*/ at::Tensor &outputSize,
/*long*/ at::Tensor poolSize, /*long*/ at::Tensor &poolSize,
/*long*/ at::Tensor poolStride, Metadata<Dimension> &m, /*long*/ at::Tensor &poolStride, Metadata<Dimension> &m,
/*cuda float*/ at::Tensor input_features, /*cuda float*/ at::Tensor &input_features,
/*cuda float*/ at::Tensor output_features, long nFeaturesToDrop) { /*cuda float*/ at::Tensor &output_features, long nFeaturesToDrop) {
Int nPlanes = input_features.size(1) - nFeaturesToDrop; Int nPlanes = input_features.size(1) - nFeaturesToDrop;
auto _rules = const auto &_rules =
m.getRuleBook(inputSize, outputSize, poolSize, poolStride, true); m.getRuleBook(inputSize, outputSize, poolSize, poolStride, true);
Int nActive = m.getNActive(outputSize); Int nActive = m.getNActive(outputSize);
output_features.resize_({nActive, nPlanes}); output_features.resize_({nActive, nPlanes});
...@@ -36,16 +36,16 @@ void cuda_MaxPooling_updateOutput( ...@@ -36,16 +36,16 @@ void cuda_MaxPooling_updateOutput(
} }
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_MaxPooling_updateGradInput( void cuda_MaxPooling_updateGradInput(
/*long*/ at::Tensor inputSize, /*long*/ at::Tensor outputSize, /*long*/ at::Tensor &inputSize, /*long*/ at::Tensor &outputSize,
/*long*/ at::Tensor poolSize, /*long*/ at::Tensor &poolSize,
/*long*/ at::Tensor poolStride, Metadata<Dimension> &m, /*long*/ at::Tensor &poolStride, Metadata<Dimension> &m,
/*cuda float*/ at::Tensor input_features, /*cuda float*/ at::Tensor &input_features,
/*cuda float*/ at::Tensor d_input_features, /*cuda float*/ at::Tensor &d_input_features,
/*cuda float*/ at::Tensor output_features, /*cuda float*/ at::Tensor &output_features,
/*cuda float*/ at::Tensor d_output_features, long nFeaturesToDrop) { /*cuda float*/ at::Tensor &d_output_features, long nFeaturesToDrop) {
Int nPlanes = input_features.size(1) - nFeaturesToDrop; Int nPlanes = input_features.size(1) - nFeaturesToDrop;
auto _rules = const auto &_rules =
m.getRuleBook(inputSize, outputSize, poolSize, poolStride, true); m.getRuleBook(inputSize, outputSize, poolSize, poolStride, true);
d_input_features.resize_as_(input_features); d_input_features.resize_as_(input_features);
d_input_features.zero_(); d_input_features.zero_();
...@@ -60,14 +60,14 @@ void cuda_MaxPooling_updateGradInput( ...@@ -60,14 +60,14 @@ void cuda_MaxPooling_updateGradInput(
} }
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_RandomizedStrideMaxPooling_updateOutput( void cuda_RandomizedStrideMaxPooling_updateOutput(
/*long*/ at::Tensor inputSize, /*long*/ at::Tensor outputSize, /*long*/ at::Tensor &inputSize, /*long*/ at::Tensor &outputSize,
/*long*/ at::Tensor poolSize, /*long*/ at::Tensor &poolSize,
/*long*/ at::Tensor poolStride, Metadata<Dimension> &m, /*long*/ at::Tensor &poolStride, Metadata<Dimension> &m,
/*cuda float*/ at::Tensor input_features, /*cuda float*/ at::Tensor &input_features,
/*cuda float*/ at::Tensor output_features, long nFeaturesToDrop) { /*cuda float*/ at::Tensor &output_features, long nFeaturesToDrop) {
Int nPlanes = input_features.size(1) - nFeaturesToDrop; Int nPlanes = input_features.size(1) - nFeaturesToDrop;
auto _rules = m.getRandomizedStrideRuleBook(inputSize, outputSize, poolSize, const auto &_rules = m.getRandomizedStrideRuleBook(inputSize, outputSize, poolSize,
poolStride, true); poolStride, true);
Int nActive = m.getNActive(outputSize); Int nActive = m.getNActive(outputSize);
output_features.resize_({nActive, nPlanes}); output_features.resize_({nActive, nPlanes});
...@@ -80,16 +80,16 @@ void cuda_RandomizedStrideMaxPooling_updateOutput( ...@@ -80,16 +80,16 @@ void cuda_RandomizedStrideMaxPooling_updateOutput(
} }
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_RandomizedStrideMaxPooling_updateGradInput( void cuda_RandomizedStrideMaxPooling_updateGradInput(
/*long*/ at::Tensor inputSize, /*long*/ at::Tensor outputSize, /*long*/ at::Tensor &inputSize, /*long*/ at::Tensor &outputSize,
/*long*/ at::Tensor poolSize, /*long*/ at::Tensor &poolSize,
/*long*/ at::Tensor poolStride, Metadata<Dimension> &m, /*long*/ at::Tensor &poolStride, Metadata<Dimension> &m,
/*cuda float*/ at::Tensor input_features, /*cuda float*/ at::Tensor &input_features,
/*cuda float*/ at::Tensor d_input_features, /*cuda float*/ at::Tensor &d_input_features,
/*cuda float*/ at::Tensor output_features, /*cuda float*/ at::Tensor &output_features,
/*cuda float*/ at::Tensor d_output_features, long nFeaturesToDrop) { /*cuda float*/ at::Tensor &d_output_features, long nFeaturesToDrop) {
Int nPlanes = input_features.size(1) - nFeaturesToDrop; Int nPlanes = input_features.size(1) - nFeaturesToDrop;
auto _rules = m.getRandomizedStrideRuleBook(inputSize, outputSize, poolSize, const auto &_rules = m.getRandomizedStrideRuleBook(inputSize, outputSize, poolSize,
poolStride, true); poolStride, true);
d_input_features.resize_as_(input_features); d_input_features.resize_as_(input_features);
d_input_features.zero_(); d_input_features.zero_();
......
sparseconvnet/SCN/CUDA/SparseToDense.cpp
View file @ 2082f213
...@@ -15,9 +15,9 @@ void cuda_SparseToDense_BackwardPass(T *d_input_features, T *d_output_features, ...@@ -15,9 +15,9 @@ void cuda_SparseToDense_BackwardPass(T *d_input_features, T *d_output_features,
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_SparseToDense_updateOutput( void cuda_SparseToDense_updateOutput(
/*long*/ at::Tensor inputSize, Metadata<Dimension> &m, /*long*/ at::Tensor &inputSize, Metadata<Dimension> &m,
/*cuda float*/ at::Tensor input_features, /*cuda float*/ at::Tensor &input_features,
/*cuda float*/ at::Tensor output_features, long nPlanes) { /*cuda float*/ at::Tensor &output_features, long nPlanes) {
{ {
std::array<long, Dimension + 2> sz; std::array<long, Dimension + 2> sz;
...@@ -30,7 +30,7 @@ void cuda_SparseToDense_updateOutput( ...@@ -30,7 +30,7 @@ void cuda_SparseToDense_updateOutput(
output_features.zero_(); output_features.zero_();
} }
if (input_features.ndimension() == 2) { if (input_features.ndimension() == 2) {
auto _rules = m.getSparseToDenseRuleBook(inputSize, true); const auto &_rules = m.getSparseToDenseRuleBook(inputSize, true);
Int _nPlanes = input_features.size(1); Int _nPlanes = input_features.size(1);
auto iF = input_features.data<T>(); auto iF = input_features.data<T>();
auto oF = output_features.data<T>(); auto oF = output_features.data<T>();
...@@ -40,16 +40,16 @@ void cuda_SparseToDense_updateOutput( ...@@ -40,16 +40,16 @@ void cuda_SparseToDense_updateOutput(
} }
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_SparseToDense_updateGradInput( void cuda_SparseToDense_updateGradInput(
/*long*/ at::Tensor inputSize, Metadata<Dimension> &m, /*long*/ at::Tensor &inputSize, Metadata<Dimension> &m,
/*cuda float*/ at::Tensor input_features, /*cuda float*/ at::Tensor &input_features,
/*cuda float*/ at::Tensor d_input_features, /*cuda float*/ at::Tensor &d_input_features,
/*cuda float*/ at::Tensor d_output_features) { /*cuda float*/ at::Tensor &d_output_features) {
d_input_features.resize_as_(input_features); d_input_features.resize_as_(input_features);
d_input_features.zero_(); d_input_features.zero_();
if (input_features.ndimension() == 2) { if (input_features.ndimension() == 2) {
auto _rules = m.getSparseToDenseRuleBook(inputSize, true); const auto &_rules = m.getSparseToDenseRuleBook(inputSize, true);
long spatialVolume = inputSize.prod().data<long>()[0]; long spatialVolume = inputSize.prod().data<long>()[0];
Int _nPlanes = d_input_features.size(1); Int _nPlanes = d_input_features.size(1);
auto diF = d_input_features.data<T>(); auto diF = d_input_features.data<T>();
......
sparseconvnet/SCN/CUDA/UnPooling.cpp
View file @ 2082f213
...@@ -15,14 +15,14 @@ void cuda_UnPooling_BackwardPass(T *d_input_features, T *d_output_features, ...@@ -15,14 +15,14 @@ void cuda_UnPooling_BackwardPass(T *d_input_features, T *d_output_features,
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_UnPooling_updateOutput( void cuda_UnPooling_updateOutput(
/*long*/ at::Tensor inputSize, /*long*/ at::Tensor outputSize, /*long*/ at::Tensor &inputSize, /*long*/ at::Tensor &outputSize,
/*long*/ at::Tensor poolSize, /*long*/ at::Tensor &poolSize,
/*long*/ at::Tensor poolStride, Metadata<Dimension> &m, /*long*/ at::Tensor &poolStride, Metadata<Dimension> &m,
/*cuda float*/ at::Tensor input_features, /*cuda float*/ at::Tensor &input_features,
/*cuda float*/ at::Tensor output_features, long nFeaturesToDrop) { /*cuda float*/ at::Tensor &output_features, long nFeaturesToDrop) {
Int nPlanes = input_features.size(1) - nFeaturesToDrop; Int nPlanes = input_features.size(1) - nFeaturesToDrop;
auto _rules = const auto &_rules =
m.getRuleBook(outputSize, inputSize, poolSize, poolStride, true); m.getRuleBook(outputSize, inputSize, poolSize, poolStride, true);
Int nActive = m.getNActive(outputSize); Int nActive = m.getNActive(outputSize);
output_features.resize_({nActive, input_features.size(1) - nFeaturesToDrop}); output_features.resize_({nActive, input_features.size(1) - nFeaturesToDrop});
...@@ -37,14 +37,14 @@ void cuda_UnPooling_updateOutput( ...@@ -37,14 +37,14 @@ void cuda_UnPooling_updateOutput(
template <typename T, Int Dimension> template <typename T, Int Dimension>
void cuda_UnPooling_updateGradInput( void cuda_UnPooling_updateGradInput(
/*long*/ at::Tensor inputSize, /*long*/ at::Tensor outputSize, /*long*/ at::Tensor &inputSize, /*long*/ at::Tensor &outputSize,
/*long*/ at::Tensor poolSize, /*long*/ at::Tensor &poolSize,
/*long*/ at::Tensor poolStride, Metadata<Dimension> &m, /*long*/ at::Tensor &poolStride, Metadata<Dimension> &m,
/*cuda float*/ at::Tensor d_input_features, /*cuda float*/ at::Tensor &d_input_features,
/*cuda float*/ at::Tensor d_output_features, long nFeaturesToDrop) { /*cuda float*/ at::Tensor &d_output_features, long nFeaturesToDrop) {
Int nPlanes = d_input_features.size(1) - nFeaturesToDrop; Int nPlanes = d_input_features.size(1) - nFeaturesToDrop;
auto _rules = const auto &_rules =
m.getRuleBook(outputSize, inputSize, poolSize, poolStride, true); m.getRuleBook(outputSize, inputSize, poolSize, poolStride, true);
auto diF = d_input_features.data<T>() + nFeaturesToDrop; auto diF = d_input_features.data<T>() + nFeaturesToDrop;
......
sparseconvnet/SCN/Metadata/ConvolutionRules.h
View file @ 2082f213
...@@ -15,7 +15,6 @@ void Convolution_InputSgToRulesAndOutputSg(SparseGrid<dimension> &inputGrid, ...@@ -15,7 +15,6 @@ void Convolution_InputSgToRulesAndOutputSg(SparseGrid<dimension> &inputGrid,
long *stride, long *inputSpatialSize, long *stride, long *inputSpatialSize,
long *outputSpatialSize) { long *outputSpatialSize) {
rules.resize(volume<dimension>(size)); rules.resize(volume<dimension>(size));
for (auto const &inIter : inputGrid.mp) { for (auto const &inIter : inputGrid.mp) {
auto outRegion = OutputRegionCalculator<dimension>( auto outRegion = OutputRegionCalculator<dimension>(
inIter.first, size, stride, outputSpatialSize); inIter.first, size, stride, outputSpatialSize);
......
sparseconvnet/SCN/Metadata/Metadata.cpp
View file @ 2082f213
...@@ -22,7 +22,7 @@ template <Int dimension> SparseGrid<dimension>::SparseGrid() : ctr(0) { ...@@ -22,7 +22,7 @@ template <Int dimension> SparseGrid<dimension>::SparseGrid() : ctr(0) {
mp.set_empty_key(empty_key); mp.set_empty_key(empty_key);
} }
template <typename T> T *OptionalTensorData(at::Tensor tensor) { template <typename T> T *OptionalTensorData(at::Tensor &tensor) {
return tensor.numel() ? tensor.data<T>() : nullptr; return tensor.numel() ? tensor.data<T>() : nullptr;
} }
...@@ -30,7 +30,7 @@ template <Int dimension> ...@@ -30,7 +30,7 @@ template <Int dimension>
void addPointToSparseGridMapAndFeatures(SparseGridMap<dimension> &mp, void addPointToSparseGridMapAndFeatures(SparseGridMap<dimension> &mp,
Point<dimension> p, Int &nActive, Point<dimension> p, Int &nActive,
long nPlanes, long nPlanes,
/*float*/ at::Tensor features, /*float*/ at::Tensor &features,
float *vec, bool overwrite) { float *vec, bool overwrite) {
auto mapVal = mp.insert(std::make_pair(p, nActive)); auto mapVal = mp.insert(std::make_pair(p, nActive));
...@@ -65,16 +65,17 @@ template <Int dimension> void Metadata<dimension>::clear() { ...@@ -65,16 +65,17 @@ template <Int dimension> void Metadata<dimension>::clear() {
blLayerRuleBook.clear(); blLayerRuleBook.clear();
} }
template <Int dimension> template <Int dimension>
Int Metadata<dimension>::getNActive(/*long*/ at::Tensor spatialSize) { Int Metadata<dimension>::getNActive(/*long*/ at::Tensor &spatialSize) {
return nActive[LongTensorToPoint<dimension>(spatialSize)]; return nActive[LongTensorToPoint<dimension>(spatialSize)];
}; };
template <Int dimension> template <Int dimension>
SparseGrids<dimension> & SparseGrids<dimension> &
Metadata<dimension>::getSparseGrid(/*long*/ at::Tensor spatialSize) { Metadata<dimension>::getSparseGrid(/*long*/ at::Tensor &spatialSize) {
return grids[LongTensorToPoint<dimension>(spatialSize)]; return grids[LongTensorToPoint<dimension>(spatialSize)];
}; };
template <Int dimension> template <Int dimension>
void Metadata<dimension>::setInputSpatialSize(/*long*/ at::Tensor spatialSize) { void Metadata<dimension>::setInputSpatialSize(
/*long*/ at::Tensor &spatialSize) {
inputSpatialSize = LongTensorToPoint<dimension>(spatialSize); inputSpatialSize = LongTensorToPoint<dimension>(spatialSize);
inputSGs = &grids[inputSpatialSize]; inputSGs = &grids[inputSpatialSize];
inputNActive = &nActive[inputSpatialSize]; inputNActive = &nActive[inputSpatialSize];
...@@ -85,10 +86,10 @@ template <Int dimension> void Metadata<dimension>::batchAddSample() { ...@@ -85,10 +86,10 @@ template <Int dimension> void Metadata<dimension>::batchAddSample() {
inputSG = &inputSGs->back(); inputSG = &inputSGs->back();
} }
template <Int dimension> template <Int dimension>
void Metadata<dimension>::setInputSpatialLocation(/*float*/ at::Tensor features, void Metadata<dimension>::setInputSpatialLocation(
/*long*/ at::Tensor location, /*float*/ at::Tensor &features,
/*float*/ at::Tensor vec, /*long*/ at::Tensor &location,
bool overwrite) { /*float*/ at::Tensor &vec, bool overwrite) {
auto p = LongTensorToPoint<dimension>(location); auto p = LongTensorToPoint<dimension>(location);
SparseGridMap<dimension> &mp = inputSG->mp; SparseGridMap<dimension> &mp = inputSG->mp;
Int &nActive = *inputNActive; Int &nActive = *inputNActive;
...@@ -98,9 +99,9 @@ void Metadata<dimension>::setInputSpatialLocation(/*float*/ at::Tensor features, ...@@ -98,9 +99,9 @@ void Metadata<dimension>::setInputSpatialLocation(/*float*/ at::Tensor features,
} }
template <Int dimension> template <Int dimension>
void Metadata<dimension>::setInputSpatialLocations( void Metadata<dimension>::setInputSpatialLocations(
/*float*/ at::Tensor features, /*float*/ at::Tensor &features,
/*long*/ at::Tensor locations, /*long*/ at::Tensor &locations,
/*float*/ at::Tensor vecs, bool overwrite) { /*float*/ at::Tensor &vecs, bool overwrite) {
/* assert(locations.ndimension() == 2 and "locations must be 2 /* assert(locations.ndimension() == 2 and "locations must be 2
* dimensional!"); */ * dimensional!"); */
/* assert(vecs.ndimension() == 2 and "vecs must be 2 dimensional!"); */ /* assert(vecs.ndimension() == 2 and "vecs must be 2 dimensional!"); */
...@@ -147,7 +148,7 @@ void Metadata<dimension>::setInputSpatialLocations( ...@@ -147,7 +148,7 @@ void Metadata<dimension>::setInputSpatialLocations(
template <Int dimension> template <Int dimension>
at::Tensor at::Tensor
Metadata<dimension>::getSpatialLocations(/*long*/ at::Tensor spatialSize) { Metadata<dimension>::getSpatialLocations(/*long*/ at::Tensor &spatialSize) {
Int nActive = getNActive(spatialSize); Int nActive = getNActive(spatialSize);
auto &SGs = getSparseGrid(spatialSize); auto &SGs = getSparseGrid(spatialSize);
Int batchSize = SGs.size(); Int batchSize = SGs.size();
...@@ -169,8 +170,8 @@ Metadata<dimension>::getSpatialLocations(/*long*/ at::Tensor spatialSize) { ...@@ -169,8 +170,8 @@ Metadata<dimension>::getSpatialLocations(/*long*/ at::Tensor spatialSize) {
} }
template <Int dimension> template <Int dimension>
void Metadata<dimension>::createMetadataForDenseToSparse( void Metadata<dimension>::createMetadataForDenseToSparse(
/*long*/ at::Tensor spatialSize, /*long*/ at::Tensor &spatialSize,
/*long*/ at::Tensor nz_, long batchSize) { /*long*/ at::Tensor &nz_, long batchSize) {
clear(); clear();
setInputSpatialSize(spatialSize); setInputSpatialSize(spatialSize);
inputSGs->resize(batchSize); inputSGs->resize(batchSize);
...@@ -208,9 +209,9 @@ void Metadata<dimension>::createMetadataForDenseToSparse( ...@@ -208,9 +209,9 @@ void Metadata<dimension>::createMetadataForDenseToSparse(
template <Int dimension> template <Int dimension>
void Metadata<dimension>::sparsifyMetadata(Metadata<dimension> &mOut, void Metadata<dimension>::sparsifyMetadata(Metadata<dimension> &mOut,
/*long*/ at::Tensor spatialSize, /*long*/ at::Tensor &spatialSize,
/*byte*/ at::Tensor filter, /*byte*/ at::Tensor &filter,
/*long*/ at::Tensor cuSum) { /*long*/ at::Tensor &cuSum) {
// Create a new SparseGrids with fewer entries. // Create a new SparseGrids with fewer entries.
mOut.clear(); mOut.clear();
auto p = LongTensorToPoint<dimension>(spatialSize); auto p = LongTensorToPoint<dimension>(spatialSize);
...@@ -240,7 +241,7 @@ void Metadata<dimension>::sparsifyMetadata(Metadata<dimension> &mOut, ...@@ -240,7 +241,7 @@ void Metadata<dimension>::sparsifyMetadata(Metadata<dimension> &mOut,
template <Int dimension> template <Int dimension>
void Metadata<dimension>::appendMetadata(Metadata<dimension> &mAdd, void Metadata<dimension>::appendMetadata(Metadata<dimension> &mAdd,
/*long*/ at::Tensor spatialSize) { /*long*/ at::Tensor &spatialSize) {
auto p = LongTensorToPoint<dimension>(spatialSize); auto p = LongTensorToPoint<dimension>(spatialSize);
auto &sgs1 = grids[p]; auto &sgs1 = grids[p];
auto &sgs2 = mAdd.grids[p]; auto &sgs2 = mAdd.grids[p];
...@@ -257,7 +258,7 @@ void Metadata<dimension>::appendMetadata(Metadata<dimension> &mAdd, ...@@ -257,7 +258,7 @@ void Metadata<dimension>::appendMetadata(Metadata<dimension> &mAdd,
template <Int dimension> template <Int dimension>
std::vector<at::Tensor> std::vector<at::Tensor>
Metadata<dimension>::sparsifyCompare(Metadata<dimension> &mGT, Metadata<dimension>::sparsifyCompare(Metadata<dimension> &mGT,
/*long*/ at::Tensor spatialSize) { /*long*/ at::Tensor &spatialSize) {
auto p = LongTensorToPoint<dimension>(spatialSize); auto p = LongTensorToPoint<dimension>(spatialSize);
at::Tensor gt = torch::zeros({nActive[p]}, at::kByte); at::Tensor gt = torch::zeros({nActive[p]}, at::kByte);
at::Tensor ref_map = torch::empty({mGT.nActive[p]}, at::kLong); at::Tensor ref_map = torch::empty({mGT.nActive[p]}, at::kLong);
...@@ -288,10 +289,10 @@ Metadata<dimension>::sparsifyCompare(Metadata<dimension> &mGT, ...@@ -288,10 +289,10 @@ Metadata<dimension>::sparsifyCompare(Metadata<dimension> &mGT,
// size[dimension] == #feature planes // size[dimension] == #feature planes
template <Int dimension> template <Int dimension>
void Metadata<dimension>::addSampleFromThresholdedTensor( void Metadata<dimension>::addSampleFromThresholdedTensor(
/*float*/ at::Tensor features_, /*float*/ at::Tensor &features_,
/*float*/ at::Tensor tensor_, /*float*/ at::Tensor &tensor_,
/*long*/ at::Tensor offset_, /*long*/ at::Tensor &offset_,
/*long*/ at::Tensor spatialSize_, float threshold) { /*long*/ at::Tensor &spatialSize_, float threshold) {
auto &nActive = *inputNActive; auto &nActive = *inputNActive;
auto &SGs = *inputSGs; auto &SGs = *inputSGs;
...@@ -404,8 +405,8 @@ template <Int dimension> void Metadata<dimension>::generateRuleBooks2s2() { ...@@ -404,8 +405,8 @@ template <Int dimension> void Metadata<dimension>::generateRuleBooks2s2() {
} }
template <Int dimension> template <Int dimension>
void Metadata<dimension>::inputLayer(/*long*/ at::Tensor spatialSize, void Metadata<dimension>::inputLayer(/*long*/ at::Tensor &spatialSize,
/*long*/ at::Tensor coords, Int batchSize, /*long*/ at::Tensor &coords, Int batchSize,
Int mode) { Int mode) {
assert(spatialSize.ndimension() == 1); assert(spatialSize.ndimension() == 1);
assert(spatialSize.size(0) == dimension); assert(spatialSize.size(0) == dimension);
...@@ -417,8 +418,8 @@ void Metadata<dimension>::inputLayer(/*long*/ at::Tensor spatialSize, ...@@ -417,8 +418,8 @@ void Metadata<dimension>::inputLayer(/*long*/ at::Tensor spatialSize,
*inputNActive); *inputNActive);
} }
template <Int dimension> template <Int dimension>
void Metadata<dimension>::blLayer(/*long*/ at::Tensor spatialSize, void Metadata<dimension>::blLayer(/*long*/ at::Tensor &spatialSize,
/*long*/ at::Tensor coords, Int mode) { /*long*/ at::Tensor &coords, Int mode) {
assert(spatialSize.ndimension() == 1); assert(spatialSize.ndimension() == 1);
assert(spatialSize.size(0) == dimension); assert(spatialSize.size(0) == dimension);
assert(coords.ndimension() == 3); assert(coords.ndimension() == 3);
...@@ -429,8 +430,8 @@ void Metadata<dimension>::blLayer(/*long*/ at::Tensor spatialSize, ...@@ -429,8 +430,8 @@ void Metadata<dimension>::blLayer(/*long*/ at::Tensor spatialSize,
} }
template <Int dimension> template <Int dimension>
RuleBook &Metadata<dimension>::getSubmanifoldRuleBook( RuleBook &Metadata<dimension>::getSubmanifoldRuleBook(
/*long*/ at::Tensor spatialSize, /*long*/ at::Tensor &spatialSize,
/*long*/ at::Tensor size, bool openMP) { /*long*/ at::Tensor &size, bool openMP) {
auto p = TwoLongTensorsToPoint<dimension>(spatialSize, size); auto p = TwoLongTensorsToPoint<dimension>(spatialSize, size);
auto &rb = submanifoldRuleBooks[p]; auto &rb = submanifoldRuleBooks[p];
if (rb.empty()) { if (rb.empty()) {
...@@ -444,7 +445,7 @@ RuleBook &Metadata<dimension>::getSubmanifoldRuleBook( ...@@ -444,7 +445,7 @@ RuleBook &Metadata<dimension>::getSubmanifoldRuleBook(
} }
template <Int dimension> template <Int dimension>
RuleBook &Metadata<dimension>::getPermutohedralSubmanifoldRuleBook( RuleBook &Metadata<dimension>::getPermutohedralSubmanifoldRuleBook(
/*long*/ at::Tensor spatialSize, bool openMP) { /*long*/ at::Tensor &spatialSize, bool openMP) {
auto p = LongTensorToPoint<dimension>(spatialSize); auto p = LongTensorToPoint<dimension>(spatialSize);
auto &rb = permutohedralRuleBooks[p]; auto &rb = permutohedralRuleBooks[p];
if (rb.empty()) { if (rb.empty()) {
...@@ -458,7 +459,7 @@ RuleBook &Metadata<dimension>::getPermutohedralSubmanifoldRuleBook( ...@@ -458,7 +459,7 @@ RuleBook &Metadata<dimension>::getPermutohedralSubmanifoldRuleBook(
} }
template <Int dimension> template <Int dimension>
RuleBook &Metadata<dimension>::getActivePoolingRuleBook( RuleBook &Metadata<dimension>::getActivePoolingRuleBook(
/*long*/ at::Tensor spatialSize) { /*long*/ at::Tensor &spatialSize) {
auto spatialSz = LongTensorToPoint<dimension>(spatialSize); auto spatialSz = LongTensorToPoint<dimension>(spatialSize);
auto &SGs = grids[spatialSz]; auto &SGs = grids[spatialSz];
auto &rb = activePoolingRuleBooks[spatialSz]; auto &rb = activePoolingRuleBooks[spatialSz];
...@@ -468,7 +469,7 @@ RuleBook &Metadata<dimension>::getActivePoolingRuleBook( ...@@ -468,7 +469,7 @@ RuleBook &Metadata<dimension>::getActivePoolingRuleBook(
} }
template <Int dimension> template <Int dimension>
RuleBook &Metadata<dimension>::getSparseToDenseRuleBook( RuleBook &Metadata<dimension>::getSparseToDenseRuleBook(
/*long*/ at::Tensor spatialSize, bool openMP) { /*long*/ at::Tensor &spatialSize, bool openMP) {
auto ss = LongTensorToPoint<dimension>(spatialSize); auto ss = LongTensorToPoint<dimension>(spatialSize);
auto &SGs = grids[ss]; auto &SGs = grids[ss];
auto &rb = sparseToDenseRuleBooks[ss]; auto &rb = sparseToDenseRuleBooks[ss];
...@@ -484,10 +485,10 @@ RuleBook &Metadata<dimension>::getSparseToDenseRuleBook( ...@@ -484,10 +485,10 @@ RuleBook &Metadata<dimension>::getSparseToDenseRuleBook(
} }
template <Int dimension> template <Int dimension>
RuleBook &Metadata<dimension>::getRuleBook( RuleBook &Metadata<dimension>::getRuleBook(
/*long*/ at::Tensor inputSpatialSize, /*long*/ at::Tensor &inputSpatialSize,
/*long*/ at::Tensor outputSpatialSize, /*long*/ at::Tensor &outputSpatialSize,
/*long*/ at::Tensor size, /*long*/ at::Tensor &size,
/*long*/ at::Tensor stride, bool openMP) { /*long*/ at::Tensor &stride, bool openMP) {
auto p = ThreeLongTensorsToPoint<dimension>(inputSpatialSize, size, stride); auto p = ThreeLongTensorsToPoint<dimension>(inputSpatialSize, size, stride);
auto &rb = ruleBooks[p]; auto &rb = ruleBooks[p];
if (rb.empty()) { if (rb.empty()) {
...@@ -511,10 +512,10 @@ RuleBook &Metadata<dimension>::getRuleBook( ...@@ -511,10 +512,10 @@ RuleBook &Metadata<dimension>::getRuleBook(
} }
template <Int dimension> template <Int dimension>
RuleBook &Metadata<dimension>::getFullConvolutionRuleBook( RuleBook &Metadata<dimension>::getFullConvolutionRuleBook(
/*long*/ at::Tensor inputSpatialSize, /*long*/ at::Tensor &inputSpatialSize,
/*long*/ at::Tensor outputSpatialSize, /*long*/ at::Tensor &outputSpatialSize,
/*long*/ at::Tensor size, /*long*/ at::Tensor &size,
/*long*/ at::Tensor stride, Metadata<dimension> &newM) { /*long*/ at::Tensor &stride, Metadata<dimension> &newM) {
auto &rb = newM.fullConvolutionRuleBook; auto &rb = newM.fullConvolutionRuleBook;
if (rb.empty()) { if (rb.empty()) {
newM.clear(); newM.clear();
...@@ -533,10 +534,10 @@ RuleBook &Metadata<dimension>::getFullConvolutionRuleBook( ...@@ -533,10 +534,10 @@ RuleBook &Metadata<dimension>::getFullConvolutionRuleBook(
template <Int dimension> template <Int dimension>
RuleBook &Metadata<dimension>::getRandomizedStrideRuleBook( RuleBook &Metadata<dimension>::getRandomizedStrideRuleBook(
/*long*/ at::Tensor inputSpatialSize, /*long*/ at::Tensor &inputSpatialSize,
/*long*/ at::Tensor outputSpatialSize, /*long*/ at::Tensor &outputSpatialSize,
/*long*/ at::Tensor size, /*long*/ at::Tensor &size,
/*long*/ at::Tensor stride, bool openMP) { /*long*/ at::Tensor &stride, bool openMP) {
auto p = ThreeLongTensorsToPoint<dimension>(inputSpatialSize, size, stride); auto p = ThreeLongTensorsToPoint<dimension>(inputSpatialSize, size, stride);
auto &rb = ruleBooks[p]; auto &rb = ruleBooks[p];
if (rb.empty()) { if (rb.empty()) {
...@@ -577,7 +578,7 @@ at::Tensor vvl2t(std::vector<std::vector<long>> v) { ...@@ -577,7 +578,7 @@ at::Tensor vvl2t(std::vector<std::vector<long>> v) {
template <Int dimension> template <Int dimension>
std::vector<at::Tensor> std::vector<at::Tensor>
Metadata<dimension>::compareSparseHelper(Metadata<dimension> &mR, Metadata<dimension>::compareSparseHelper(Metadata<dimension> &mR,
/* long */ at::Tensor spatialSize) { /* long */ at::Tensor &spatialSize) {
auto p = LongTensorToPoint<dimension>(spatialSize); auto p = LongTensorToPoint<dimension>(spatialSize);
auto &sgsL = grids[p]; auto &sgsL = grids[p];
auto &sgsR = mR.grids[p]; auto &sgsR = mR.grids[p];
...@@ -624,7 +625,7 @@ at::Tensor vvl2t_(std::vector<std::vector<Int>> v) { ...@@ -624,7 +625,7 @@ at::Tensor vvl2t_(std::vector<std::vector<Int>> v) {
template <Int dimension> template <Int dimension>
at::Tensor at::Tensor
Metadata<dimension>::copyFeaturesHelper(Metadata<dimension> &mR, Metadata<dimension>::copyFeaturesHelper(Metadata<dimension> &mR,
/* long */ at::Tensor spatialSize) { /* long */ at::Tensor &spatialSize) {
auto p = LongTensorToPoint<dimension>(spatialSize); auto p = LongTensorToPoint<dimension>(spatialSize);
auto &sgsL = grids[p]; auto &sgsL = grids[p];
auto &sgsR = mR.grids[p]; auto &sgsR = mR.grids[p];
......
sparseconvnet/SCN/Metadata/Metadata.h
View file @ 2082f213
...@@ -38,7 +38,7 @@ template <Int dimension> ...@@ -38,7 +38,7 @@ template <Int dimension>
void addPointToSparseGridMapAndFeatures(SparseGridMap<dimension> &mp, void addPointToSparseGridMapAndFeatures(SparseGridMap<dimension> &mp,
Point<dimension> p, Int &nActive, Point<dimension> p, Int &nActive,
long nPlanes, long nPlanes,
/*float*/ at::Tensor features, /*float*/ at::Tensor &features,
float *vec, bool overwrite); float *vec, bool overwrite);
template <Int dimension> class Metadata { template <Int dimension> class Metadata {
...@@ -81,45 +81,47 @@ public: ...@@ -81,45 +81,47 @@ public:
Metadata(); Metadata();
void clear(); void clear();
Int getNActive(/*long*/ at::Tensor spatialSize); Int getNActive(/*long*/ at::Tensor &spatialSize);
SparseGrids<dimension> &getSparseGrid(/*long*/ at::Tensor spatialSize); SparseGrids<dimension> &getSparseGrid(/*long*/ at::Tensor &spatialSize);
void setInputSpatialSize(/*long*/ at::Tensor spatialSize); void setInputSpatialSize(/*long*/ at::Tensor &spatialSize);
void batchAddSample(); void batchAddSample();
void setInputSpatialLocation(/*float*/ at::Tensor features, void setInputSpatialLocation(/*float*/ at::Tensor &features,
/*long*/ at::Tensor location, /*long*/ at::Tensor &location,
/*float*/ at::Tensor vec, bool overwrite); /*float*/ at::Tensor &vec, bool overwrite);
void setInputSpatialLocations(/*float*/ at::Tensor features, void setInputSpatialLocations(/*float*/ at::Tensor &features,
/*long*/ at::Tensor locations, /*long*/ at::Tensor &locations,
/*float*/ at::Tensor vecs, bool overwrite); /*float*/ at::Tensor &vecs, bool overwrite);
at::Tensor getSpatialLocations(/*long*/ at::Tensor spatialSize); at::Tensor getSpatialLocations(/*long*/ at::Tensor &spatialSize);
void createMetadataForDenseToSparse(/*long*/ at::Tensor spatialSize, void createMetadataForDenseToSparse(/*long*/ at::Tensor &spatialSize,
/*long*/ at::Tensor nz_, long batchSize); /*long*/ at::Tensor &nz_, long batchSize);
void sparsifyMetadata(Metadata<dimension> &mOut, void sparsifyMetadata(Metadata<dimension> &mOut,
/*long*/ at::Tensor spatialSize, /*long*/ at::Tensor &spatialSize,
/*byte*/ at::Tensor filter, /*byte*/ at::Tensor &filter,
/*long*/ at::Tensor cuSum); /*long*/ at::Tensor &cuSum);
void appendMetadata(Metadata<dimension> &mAdd, void appendMetadata(Metadata<dimension> &mAdd,
/*long*/ at::Tensor spatialSize); /*long*/ at::Tensor &spatialSize);
/* std::vector<at::Tensor> sparsifyCompare(Metadata<dimension> &mReference, */ /* std::vector<at::Tensor &> sparsifyCompare(Metadata<dimension> &mReference,
*/
/* Metadata<dimension> &mSparsified, /* Metadata<dimension> &mSparsified,
*/ */
/* /\*long*\/ at::Tensor spatialSize); /* /\*long*\/ at::Tensor &
* spatialSize);
*/ */
std::vector<at::Tensor> sparsifyCompare(Metadata<dimension> &mReference, std::vector<at::Tensor> sparsifyCompare(Metadata<dimension> &mReference,
/*long*/ at::Tensor spatialSize); /*long*/ at::Tensor &spatialSize);
// tensor is size[0] x .. x size[dimension-1] x size[dimension] // tensor is size[0] x .. x size[dimension-1] x size[dimension]
// size[0] x .. x size[dimension-1] == spatial volume // size[0] x .. x size[dimension-1] == spatial volume
// size[dimension] == #feature planes // size[dimension] == #feature planes
void addSampleFromThresholdedTensor(/*float*/ at::Tensor features_, void addSampleFromThresholdedTensor(/*float*/ at::Tensor &features_,
/*float*/ at::Tensor tensor_, /*float*/ at::Tensor &tensor_,
/*long*/ at::Tensor offset_, /*long*/ at::Tensor &offset_,
/*long*/ at::Tensor spatialSize_, /*long*/ at::Tensor &spatialSize_,
float threshold); float threshold);
// 3x3 submanifold convolutions, 3x3/2x2 pooling or strided convolutions // 3x3 submanifold convolutions, 3x3/2x2 pooling or strided convolutions
...@@ -128,41 +130,42 @@ public: ...@@ -128,41 +130,42 @@ public:
// 3x3 submanifold convolutions, 2x2 pooling or strided convolutions // 3x3 submanifold convolutions, 2x2 pooling or strided convolutions
void generateRuleBooks2s2(); void generateRuleBooks2s2();
void inputLayer(/*long*/ at::Tensor spatialSize, void inputLayer(/*long*/ at::Tensor &spatialSize,
/*long*/ at::Tensor coords, Int batchSize, Int mode); /*long*/ at::Tensor &coords, Int batchSize, Int mode);
void blLayer(/*long*/ at::Tensor spatialSize, /*long*/ at::Tensor coords, void blLayer(/*long*/ at::Tensor &spatialSize, /*long*/ at::Tensor &coords,
Int mode); Int mode);
RuleBook &getSubmanifoldRuleBook(/*long*/ at::Tensor spatialSize, RuleBook &getSubmanifoldRuleBook(/*long*/ at::Tensor &spatialSize,
/*long*/ at::Tensor size, bool openMP); /*long*/ at::Tensor &size, bool openMP);
RuleBook &getPermutohedralSubmanifoldRuleBook(/*long*/ at::Tensor spatialSize, RuleBook &
getPermutohedralSubmanifoldRuleBook(/*long*/ at::Tensor &spatialSize,
bool openMP); bool openMP);
RuleBook &getActivePoolingRuleBook(/*long*/ at::Tensor spatialSize); RuleBook &getActivePoolingRuleBook(/*long*/ at::Tensor &spatialSize);
RuleBook &getSparseToDenseRuleBook(/*long*/ at::Tensor spatialSize, RuleBook &getSparseToDenseRuleBook(/*long*/ at::Tensor &spatialSize,
bool openMP); bool openMP);
RuleBook &getRuleBook(/*long*/ at::Tensor inputSpatialSize, RuleBook &getRuleBook(/*long*/ at::Tensor &inputSpatialSize,
/*long*/ at::Tensor outputSpatialSize, /*long*/ at::Tensor &outputSpatialSize,
/*long*/ at::Tensor size, /*long*/ at::Tensor &size,
/*long*/ at::Tensor stride, bool openMP); /*long*/ at::Tensor &stride, bool openMP);
RuleBook &getFullConvolutionRuleBook(/*long*/ at::Tensor inputSpatialSize, RuleBook &getFullConvolutionRuleBook(/*long*/ at::Tensor &inputSpatialSize,
/*long*/ at::Tensor outputSpatialSize, /*long*/ at::Tensor &outputSpatialSize,
/*long*/ at::Tensor size, /*long*/ at::Tensor &size,
/*long*/ at::Tensor stride, /*long*/ at::Tensor &stride,
Metadata<dimension> &newM); Metadata<dimension> &newM);
RuleBook &getRandomizedStrideRuleBook(/*long*/ at::Tensor inputSpatialSize, RuleBook &getRandomizedStrideRuleBook(/*long*/ at::Tensor &inputSpatialSize,
/*long*/ at::Tensor outputSpatialSize, /*long*/ at::Tensor &outputSpatialSize,
/*long*/ at::Tensor size, /*long*/ at::Tensor &size,
/*long*/ at::Tensor stride, /*long*/ at::Tensor &stride,
bool openMP); bool openMP);
std::vector<at::Tensor> std::vector<at::Tensor >
compareSparseHelper(Metadata<dimension> &mR, compareSparseHelper(Metadata<dimension> &mR,
/* long */ at::Tensor spatialSize); /* long */ at::Tensor &spatialSize);
at::Tensor copyFeaturesHelper(Metadata<dimension> &mR, at::Tensor copyFeaturesHelper(Metadata<dimension> &mR,
/* long */ at::Tensor spatialSize); /* long */ at::Tensor &spatialSize);
}; };
template <typename T> T *OptionalTensorData(at::Tensor tensor); template <typename T> T *OptionalTensorData(at::Tensor &tensor);
template <Int dimension> Int volume(long *point); template <Int dimension> Int volume(long *point);
#endif #endif
sparseconvnet/SCN/cuda.cu
View file @ 2082f213
...@@ -23,12 +23,12 @@ ...@@ -23,12 +23,12 @@
template void ActivePooling_ForwardPass<float>(float *input_features, template void ActivePooling_ForwardPass<float>(float *input_features,
float *output_features, float *output_features,
Int batchSize, Int maxActive, Int batchSize, Int maxActive,
Int nPlanes, Int *rules, Int nPlanes, const Int *rules,
bool average); bool average);
template void ActivePooling_BackwardPass<float>(float *d_input_features, template void ActivePooling_BackwardPass<float>(float *d_input_features,
float *d_output_features, float *d_output_features,
Int batchSize, Int maxActive, Int batchSize, Int maxActive,
Int nPlanes, Int *rules, Int nPlanes, const Int *rules,
bool average); bool average);
template void dAffineReluTrivialConvolution_forward<float>( template void dAffineReluTrivialConvolution_forward<float>(
......
sparseconvnet/SCN/misc/drawCurve.cpp
View file @ 2082f213
...@@ -7,9 +7,9 @@ ...@@ -7,9 +7,9 @@
// Helper function to draw pen strokes with // Helper function to draw pen strokes with
// nPlanes = 3, feature vector = (1,dx,dy) // nPlanes = 3, feature vector = (1,dx,dy)
void cpu_float_DrawCurve_2(Metadata<2> &m, void cpu_float_DrawCurve_2(Metadata<2> &m,
/*float*/ at::Tensor features, /*float*/ at::Tensor &features,
/*float*/ at::Tensor stroke) { /*float*/ at::Tensor &stroke) {
at::Tensor location = at::zeros(at::CPU(at::kLong), {2}); at::Tensor &location = at::zeros(at::CPU(at::kLong), {2});
auto location_ = location.data<long>(); auto location_ = location.data<long>();
auto vec = at::zeros(at::CPU(at::kFloat), {3}); auto vec = at::zeros(at::CPU(at::kFloat), {3});
......
sparseconvnet/SCN/pybind.cpp
View file @ 2082f213
...@@ -31,168 +31,168 @@ template <Int Dimension> void dimension(py::module &m, const char *name) { ...@@ -31,168 +31,168 @@ template <Int Dimension> void dimension(py::module &m, const char *name) {
.def("compareSparseHelper", &Metadata<Dimension>::compareSparseHelper) .def("compareSparseHelper", &Metadata<Dimension>::compareSparseHelper)
.def("copyFeaturesHelper", &Metadata<Dimension>::copyFeaturesHelper); .def("copyFeaturesHelper", &Metadata<Dimension>::copyFeaturesHelper);
m.def("ActivePooling_updateOutput", m.def("ActivePooling_updateOutput",
(void (*)(at::Tensor, Metadata<Dimension> &, at::Tensor, at::Tensor, (void (*)(at::Tensor&, Metadata<Dimension> &, at::Tensor&, at::Tensor&,
bool)) & bool)) &
ActivePooling_updateOutput, ActivePooling_updateOutput,
""); "");
m.def("ActivePooling_updateGradInput", m.def("ActivePooling_updateGradInput",
(void (*)(at::Tensor, Metadata<Dimension> &, at::Tensor, at::Tensor, (void (*)(at::Tensor&, Metadata<Dimension> &, at::Tensor&, at::Tensor&,
at::Tensor, bool)) & at::Tensor&, bool)) &
ActivePooling_updateGradInput, ActivePooling_updateGradInput,
""); "");
m.def("AveragePooling_updateOutput", m.def("AveragePooling_updateOutput",
(void (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (void (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, at::Tensor, at::Tensor, long)) & Metadata<Dimension> &, at::Tensor&, at::Tensor&, long)) &
AveragePooling_updateOutput, AveragePooling_updateOutput,
""); "");
m.def("AveragePooling_updateGradInput", m.def("AveragePooling_updateGradInput",
(void (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (void (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, at::Tensor, at::Tensor, at::Tensor, Metadata<Dimension> &, at::Tensor&, at::Tensor&, at::Tensor&,
long)) & long)) &
AveragePooling_updateGradInput, AveragePooling_updateGradInput,
""); "");
m.def("Convolution_updateOutput", m.def("Convolution_updateOutput",
(double (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (double (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, at::Tensor, at::Tensor, at::Tensor, Metadata<Dimension> &, at::Tensor&, at::Tensor&, at::Tensor&,
at::Tensor)) & at::Tensor&)) &
Convolution_updateOutput, Convolution_updateOutput,
""); "");
m.def("Convolution_backward", m.def("Convolution_backward",
(void (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (void (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, at::Tensor, at::Tensor, at::Tensor, Metadata<Dimension> &, at::Tensor&, at::Tensor&, at::Tensor&,
at::Tensor, at::Tensor, at::Tensor)) & at::Tensor&, at::Tensor&, at::Tensor&)) &
Convolution_backward, Convolution_backward,
""); "");
m.def("RandomizedStrideConvolution_updateOutput", m.def("RandomizedStrideConvolution_updateOutput",
(double (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (double (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, at::Tensor, at::Tensor, at::Tensor, Metadata<Dimension> &, at::Tensor&, at::Tensor&, at::Tensor&,
at::Tensor)) & at::Tensor&)) &
RandomizedStrideConvolution_updateOutput, RandomizedStrideConvolution_updateOutput,
""); "");
m.def("RandomizedStrideConvolution_backward", m.def("RandomizedStrideConvolution_backward",
(void (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (void (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, at::Tensor, at::Tensor, at::Tensor, Metadata<Dimension> &, at::Tensor&, at::Tensor&, at::Tensor&,
at::Tensor, at::Tensor, at::Tensor)) & at::Tensor&, at::Tensor&, at::Tensor&)) &
RandomizedStrideConvolution_backward, RandomizedStrideConvolution_backward,
""); "");
m.def("Deconvolution_updateOutput", m.def("Deconvolution_updateOutput",
(double (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (double (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, at::Tensor, at::Tensor, at::Tensor, Metadata<Dimension> &, at::Tensor&, at::Tensor&, at::Tensor&,
at::Tensor)) & at::Tensor&)) &
Deconvolution_updateOutput, Deconvolution_updateOutput,
""); "");
m.def("Deconvolution_backward", m.def("Deconvolution_backward",
(void (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (void (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, at::Tensor, at::Tensor, at::Tensor, Metadata<Dimension> &, at::Tensor&, at::Tensor&, at::Tensor&,
at::Tensor, at::Tensor, at::Tensor)) & at::Tensor&, at::Tensor&, at::Tensor&)) &
Deconvolution_backward, Deconvolution_backward,
""); "");
m.def("FullConvolution_updateOutput", m.def("FullConvolution_updateOutput",
(double (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (double (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, Metadata<Dimension> &, at::Tensor, Metadata<Dimension> &, Metadata<Dimension> &, at::Tensor&,
at::Tensor, at::Tensor, at::Tensor)) & at::Tensor&, at::Tensor&, at::Tensor&)) &
FullConvolution_updateOutput, FullConvolution_updateOutput,
""); "");
m.def("FullConvolution_backward", m.def("FullConvolution_backward",
(void (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (void (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, Metadata<Dimension> &, at::Tensor, Metadata<Dimension> &, Metadata<Dimension> &, at::Tensor&,
at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor)) & at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&)) &
FullConvolution_backward, FullConvolution_backward,
""); "");
m.def("MaxPooling_updateOutput", m.def("MaxPooling_updateOutput",
(void (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (void (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, at::Tensor, at::Tensor, long)) & Metadata<Dimension> &, at::Tensor&, at::Tensor&, long)) &
MaxPooling_updateOutput, MaxPooling_updateOutput,
""); "");
m.def("MaxPooling_updateGradInput", m.def("MaxPooling_updateGradInput",
(void (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (void (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, at::Tensor, at::Tensor, at::Tensor, Metadata<Dimension> &, at::Tensor&, at::Tensor&, at::Tensor&,
at::Tensor, long)) & at::Tensor&, long)) &
MaxPooling_updateGradInput, MaxPooling_updateGradInput,
""); "");
m.def("RandomizedStrideMaxPooling_updateOutput", m.def("RandomizedStrideMaxPooling_updateOutput",
(void (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (void (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, at::Tensor, at::Tensor, long)) & Metadata<Dimension> &, at::Tensor&, at::Tensor&, long)) &
RandomizedStrideMaxPooling_updateOutput, RandomizedStrideMaxPooling_updateOutput,
""); "");
m.def("RandomizedStrideMaxPooling_updateGradInput", m.def("RandomizedStrideMaxPooling_updateGradInput",
(void (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (void (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, at::Tensor, at::Tensor, at::Tensor, Metadata<Dimension> &, at::Tensor&, at::Tensor&, at::Tensor&,
at::Tensor, long)) & at::Tensor&, long)) &
RandomizedStrideMaxPooling_updateGradInput, RandomizedStrideMaxPooling_updateGradInput,
""); "");
m.def("SparseToDense_updateOutput", m.def("SparseToDense_updateOutput",
(void (*)(at::Tensor, Metadata<Dimension> &, at::Tensor, at::Tensor, (void (*)(at::Tensor&, Metadata<Dimension> &, at::Tensor&, at::Tensor&,
long)) & long)) &
SparseToDense_updateOutput, SparseToDense_updateOutput,
""); "");
m.def("SparseToDense_updateGradInput", m.def("SparseToDense_updateGradInput",
(void (*)(at::Tensor, Metadata<Dimension> &, at::Tensor, at::Tensor, (void (*)(at::Tensor&, Metadata<Dimension> &, at::Tensor&, at::Tensor&,
at::Tensor)) & at::Tensor&)) &
SparseToDense_updateGradInput, SparseToDense_updateGradInput,
""); "");
m.def("SubmanifoldConvolution_updateOutput", m.def("SubmanifoldConvolution_updateOutput",
(double (*)(at::Tensor, at::Tensor, Metadata<Dimension> &, at::Tensor, (double (*)(at::Tensor&, at::Tensor&, Metadata<Dimension> &, at::Tensor&,
at::Tensor, at::Tensor, at::Tensor)) & at::Tensor&, at::Tensor&, at::Tensor&)) &
SubmanifoldConvolution_updateOutput, SubmanifoldConvolution_updateOutput,
""); "");
m.def("SubmanifoldConvolution_backward", m.def("SubmanifoldConvolution_backward",
(void (*)(at::Tensor, at::Tensor, Metadata<Dimension> &, at::Tensor, (void (*)(at::Tensor&, at::Tensor&, Metadata<Dimension> &, at::Tensor&,
at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor)) & at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&)) &
SubmanifoldConvolution_backward, SubmanifoldConvolution_backward,
""); "");
m.def("PermutohedralSubmanifoldConvolution_updateOutput", m.def("PermutohedralSubmanifoldConvolution_updateOutput",
(double (*)(at::Tensor, Metadata<Dimension> &, at::Tensor, at::Tensor, (double (*)(at::Tensor&, Metadata<Dimension> &, at::Tensor&, at::Tensor&,
at::Tensor, at::Tensor)) & at::Tensor&, at::Tensor&)) &
PermutohedralSubmanifoldConvolution_updateOutput, PermutohedralSubmanifoldConvolution_updateOutput,
""); "");
m.def("PermutohedralSubmanifoldConvolution_backward", m.def("PermutohedralSubmanifoldConvolution_backward",
(void (*)(at::Tensor, Metadata<Dimension> &, at::Tensor, at::Tensor, (void (*)(at::Tensor&, Metadata<Dimension> &, at::Tensor&, at::Tensor&,
at::Tensor, at::Tensor, at::Tensor, at::Tensor)) & at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&)) &
PermutohedralSubmanifoldConvolution_backward, PermutohedralSubmanifoldConvolution_backward,
""); "");
m.def("InputLayer_updateOutput", m.def("InputLayer_updateOutput",
(void (*)(Metadata<Dimension> &, at::Tensor, at::Tensor, at::Tensor, (void (*)(Metadata<Dimension> &, at::Tensor&, at::Tensor&, at::Tensor&,
at::Tensor, long, long)) & at::Tensor&, long, long)) &
InputLayer_updateOutput, InputLayer_updateOutput,
""); "");
m.def("InputLayer_updateGradInput", m.def("InputLayer_updateGradInput",
(void (*)(Metadata<Dimension> &, at::Tensor, at::Tensor)) & (void (*)(Metadata<Dimension> &, at::Tensor&, at::Tensor&)) &
InputLayer_updateGradInput, InputLayer_updateGradInput,
""); "");
m.def("OutputLayer_updateOutput", m.def("OutputLayer_updateOutput",
(void (*)(Metadata<Dimension> &, at::Tensor, at::Tensor)) & (void (*)(Metadata<Dimension> &, at::Tensor&, at::Tensor&)) &
OutputLayer_updateOutput, OutputLayer_updateOutput,
""); "");
m.def("OutputLayer_updateGradInput", m.def("OutputLayer_updateGradInput",
(void (*)(Metadata<Dimension> &, at::Tensor, at::Tensor)) & (void (*)(Metadata<Dimension> &, at::Tensor&, at::Tensor&)) &
OutputLayer_updateGradInput, OutputLayer_updateGradInput,
""); "");
m.def("BLInputLayer_updateOutput", m.def("BLInputLayer_updateOutput",
(void (*)(Metadata<Dimension> &, at::Tensor, at::Tensor, at::Tensor, (void (*)(Metadata<Dimension> &, at::Tensor&, at::Tensor&, at::Tensor&,
at::Tensor, long)) & at::Tensor&, long)) &
BLInputLayer_updateOutput, BLInputLayer_updateOutput,
""); "");
m.def("BLInputLayer_updateGradInput", m.def("BLInputLayer_updateGradInput",
(void (*)(Metadata<Dimension> &, at::Tensor, at::Tensor)) & (void (*)(Metadata<Dimension> &, at::Tensor&, at::Tensor&)) &
BLInputLayer_updateGradInput, BLInputLayer_updateGradInput,
""); "");
m.def("BLOutputLayer_updateOutput", m.def("BLOutputLayer_updateOutput",
(void (*)(Metadata<Dimension> &, at::Tensor, at::Tensor)) & (void (*)(Metadata<Dimension> &, at::Tensor&, at::Tensor&)) &
BLOutputLayer_updateOutput, BLOutputLayer_updateOutput,
""); "");
m.def("BLOutputLayer_updateGradInput", m.def("BLOutputLayer_updateGradInput",
(void (*)(Metadata<Dimension> &, at::Tensor, at::Tensor)) & (void (*)(Metadata<Dimension> &, at::Tensor&, at::Tensor&)) &
BLOutputLayer_updateGradInput, BLOutputLayer_updateGradInput,
""); "");
m.def("UnPooling_updateOutput", m.def("UnPooling_updateOutput",
(void (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (void (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, at::Tensor, at::Tensor, long)) & Metadata<Dimension> &, at::Tensor&, at::Tensor&, long)) &
UnPooling_updateOutput, UnPooling_updateOutput,
""); "");
m.def("UnPooling_updateGradInput", m.def("UnPooling_updateGradInput",
(void (*)(at::Tensor, at::Tensor, at::Tensor, at::Tensor, (void (*)(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
Metadata<Dimension> &, at::Tensor, at::Tensor, long)) & Metadata<Dimension> &, at::Tensor&, at::Tensor&, long)) &
UnPooling_updateGradInput, UnPooling_updateGradInput,
""); "");
} }
......
sparseconvnet/SCN/sparseconvnet.h
View file @ 2082f213
...@@ -6,234 +6,234 @@ ...@@ -6,234 +6,234 @@
#include "Metadata/Metadata.h" #include "Metadata/Metadata.h"
double AffineReluTrivialConvolution_updateOutput(at::Tensor input_features, double AffineReluTrivialConvolution_updateOutput(at::Tensor &input_features,
at::Tensor output_features, at::Tensor &output_features,
at::Tensor affineWeight, at::Tensor &affineWeight,
at::Tensor affineBias, at::Tensor &affineBias,
at::Tensor convWeight); at::Tensor &convWeight);
void AffineReluTrivialConvolution_backward( void AffineReluTrivialConvolution_backward(
at::Tensor input_features, at::Tensor d_input_features, at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor affineWeight, at::Tensor &d_output_features, at::Tensor &affineWeight,
at::Tensor d_affineWeight, at::Tensor affineBias, at::Tensor d_affineBias, at::Tensor &d_affineWeight, at::Tensor &affineBias, at::Tensor &d_affineBias,
at::Tensor convWeight, at::Tensor d_convWeight, bool additiveGrad); at::Tensor &convWeight, at::Tensor &d_convWeight, bool additiveGrad);
void BatchNormalization_updateOutput( void BatchNormalization_updateOutput(
at::Tensor input_features, at::Tensor output_features, at::Tensor saveMean, at::Tensor &input_features, at::Tensor &output_features, at::Tensor &saveMean,
at::Tensor saveInvStd, at::Tensor runningMean, at::Tensor runningVar, at::Tensor &saveInvStd, at::Tensor &runningMean, at::Tensor &runningVar,
at::Tensor weight, at::Tensor bias, double eps, double momentum, bool train, at::Tensor &weight, at::Tensor &bias, double eps, double momentum, bool train,
double leakiness); double leakiness);
void BatchNormalization_backward( void BatchNormalization_backward(
at::Tensor input_features, at::Tensor d_input_features, at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor output_features, at::Tensor d_output_features, at::Tensor &output_features, at::Tensor &d_output_features,
at::Tensor saveMean, at::Tensor saveInvStd, at::Tensor runningMean, at::Tensor &saveMean, at::Tensor &saveInvStd, at::Tensor &runningMean,
at::Tensor runningVar, at::Tensor weight, at::Tensor bias, at::Tensor &runningVar, at::Tensor &weight, at::Tensor &bias,
at::Tensor d_weight, at::Tensor d_bias, double leakiness); at::Tensor &d_weight, at::Tensor &d_bias, double leakiness);
void BatchwiseMultiplicativeDropout_updateOutput(at::Tensor input_features, void BatchwiseMultiplicativeDropout_updateOutput(at::Tensor &input_features,
at::Tensor output_features, at::Tensor &output_features,
at::Tensor noise, at::Tensor &noise,
double alpha); double alpha);
void BatchwiseMultiplicativeDropout_updateGradInput( void BatchwiseMultiplicativeDropout_updateGradInput(
at::Tensor input_features, at::Tensor d_input_features, at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor noise, double alpha); at::Tensor &d_output_features, at::Tensor &noise, double alpha);
void LeakyReLU_updateOutput(at::Tensor input_features, void LeakyReLU_updateOutput(at::Tensor &input_features,
at::Tensor output_features, double alpha); at::Tensor &output_features, double alpha);
void LeakyReLU_updateGradInput(at::Tensor input_features, void LeakyReLU_updateGradInput(at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, double alpha); at::Tensor &d_output_features, double alpha);
double NetworkInNetwork_updateOutput(at::Tensor input_features, double NetworkInNetwork_updateOutput(at::Tensor &input_features,
at::Tensor output_features, at::Tensor &output_features,
at::Tensor weight, at::Tensor bias); at::Tensor &weight, at::Tensor &bias);
void NetworkInNetwork_updateGradInput(at::Tensor d_input_features, void NetworkInNetwork_updateGradInput(at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor &d_output_features,
at::Tensor weight); at::Tensor &weight);
void NetworkInNetwork_accGradParameters(at::Tensor input_features, void NetworkInNetwork_accGradParameters(at::Tensor &input_features,
at::Tensor d_output_features, at::Tensor &d_output_features,
at::Tensor d_weight, at::Tensor d_bias); at::Tensor &d_weight, at::Tensor &d_bias);
template <Int Dimension> template <Int Dimension>
void ActivePooling_updateOutput(at::Tensor inputSize, Metadata<Dimension> &m, void ActivePooling_updateOutput(at::Tensor &inputSize, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, bool average); at::Tensor &output_features, bool average);
template <Int Dimension> template <Int Dimension>
void ActivePooling_updateGradInput(at::Tensor inputSize, Metadata<Dimension> &m, void ActivePooling_updateGradInput(at::Tensor &inputSize, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, bool average); at::Tensor &d_output_features, bool average);
template <Int Dimension> template <Int Dimension>
void AveragePooling_updateOutput(at::Tensor inputSize, at::Tensor outputSize, void AveragePooling_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor poolSize, at::Tensor poolStride, at::Tensor &poolSize, at::Tensor &poolStride,
Metadata<Dimension> &m, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, at::Tensor &output_features,
long nFeaturesToDrop); long nFeaturesToDrop);
template <Int Dimension> template <Int Dimension>
void AveragePooling_updateGradInput(at::Tensor inputSize, at::Tensor outputSize, void AveragePooling_updateGradInput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor poolSize, at::Tensor poolStride, at::Tensor &poolSize, at::Tensor &poolStride,
Metadata<Dimension> &m, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor &d_output_features,
long nFeaturesToDrop); long nFeaturesToDrop);
template <Int Dimension> template <Int Dimension>
double Convolution_updateOutput(at::Tensor inputSize, at::Tensor outputSize, double Convolution_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor filterSize, at::Tensor filterStride, at::Tensor &filterSize, at::Tensor &filterStride,
Metadata<Dimension> &m, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, at::Tensor weight, at::Tensor &output_features, at::Tensor &weight,
at::Tensor bias); at::Tensor &bias);
template <Int Dimension> template <Int Dimension>
void Convolution_backward(at::Tensor inputSize, at::Tensor outputSize, void Convolution_backward(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor filterSize, at::Tensor filterStride, at::Tensor &filterSize, at::Tensor &filterStride,
Metadata<Dimension> &m, at::Tensor input_features, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor weight, at::Tensor &d_output_features, at::Tensor &weight,
at::Tensor d_weight, at::Tensor d_bias); at::Tensor &d_weight, at::Tensor &d_bias);
template <Int Dimension> template <Int Dimension>
double SubmanifoldConvolution_updateOutput(at::Tensor inputSize, double SubmanifoldConvolution_updateOutput(at::Tensor &inputSize,
at::Tensor filterSize, at::Tensor &filterSize,
Metadata<Dimension> &m, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, at::Tensor &output_features,
at::Tensor weight, at::Tensor bias); at::Tensor &weight, at::Tensor &bias);
template <Int Dimension> template <Int Dimension>
void SubmanifoldConvolution_backward( void SubmanifoldConvolution_backward(
at::Tensor inputSize, at::Tensor filterSize, Metadata<Dimension> &m, at::Tensor &inputSize, at::Tensor &filterSize, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor d_input_features, at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor weight, at::Tensor d_weight, at::Tensor &d_output_features, at::Tensor &weight, at::Tensor &d_weight,
at::Tensor d_bias); at::Tensor &d_bias);
template <Int Dimension> template <Int Dimension>
double PermutohedralSubmanifoldConvolution_updateOutput( double PermutohedralSubmanifoldConvolution_updateOutput(
at::Tensor inputSize, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &inputSize, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, at::Tensor weight, at::Tensor bias); at::Tensor &output_features, at::Tensor &weight, at::Tensor &bias);
template <Int Dimension> template <Int Dimension>
void PermutohedralSubmanifoldConvolution_backward( void PermutohedralSubmanifoldConvolution_backward(
at::Tensor inputSize, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &inputSize, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor d_output_features, at::Tensor &d_input_features, at::Tensor &d_output_features,
at::Tensor weight, at::Tensor d_weight, at::Tensor d_bias); at::Tensor &weight, at::Tensor &d_weight, at::Tensor &d_bias);
template <Int Dimension> template <Int Dimension>
double FullConvolution_updateOutput( double FullConvolution_updateOutput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize,
at::Tensor filterStride, Metadata<Dimension> &mIn, at::Tensor &filterStride, Metadata<Dimension> &mIn,
Metadata<Dimension> &mOut, at::Tensor input_features, Metadata<Dimension> &mOut, at::Tensor &input_features,
at::Tensor output_features, at::Tensor weight, at::Tensor bias); at::Tensor &output_features, at::Tensor &weight, at::Tensor &bias);
template <Int Dimension> template <Int Dimension>
void FullConvolution_backward(at::Tensor inputSize, at::Tensor outputSize, void FullConvolution_backward(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor filterSize, at::Tensor filterStride, at::Tensor &filterSize, at::Tensor &filterStride,
Metadata<Dimension> &mIn, Metadata<Dimension> &mIn,
Metadata<Dimension> &mOut, Metadata<Dimension> &mOut,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor weight, at::Tensor &d_output_features, at::Tensor &weight,
at::Tensor d_weight, at::Tensor d_bias); at::Tensor &d_weight, at::Tensor &d_bias);
template <Int Dimension> template <Int Dimension>
double RandomizedStrideConvolution_updateOutput( double RandomizedStrideConvolution_updateOutput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize,
at::Tensor filterStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &filterStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, at::Tensor weight, at::Tensor bias); at::Tensor &output_features, at::Tensor &weight, at::Tensor &bias);
template <Int Dimension> template <Int Dimension>
void RandomizedStrideConvolution_backward( void RandomizedStrideConvolution_backward(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize,
at::Tensor filterStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &filterStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor d_output_features, at::Tensor &d_input_features, at::Tensor &d_output_features,
at::Tensor weight, at::Tensor d_weight, at::Tensor d_bias); at::Tensor &weight, at::Tensor &d_weight, at::Tensor &d_bias);
template <Int Dimension> template <Int Dimension>
double Deconvolution_updateOutput( double Deconvolution_updateOutput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize,
at::Tensor filterStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &filterStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, at::Tensor weight, at::Tensor bias); at::Tensor &output_features, at::Tensor &weight, at::Tensor &bias);
template <Int Dimension> template <Int Dimension>
void Deconvolution_backward(at::Tensor inputSize, at::Tensor outputSize, void Deconvolution_backward(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor filterSize, at::Tensor filterStride, at::Tensor &filterSize, at::Tensor &filterStride,
Metadata<Dimension> &m, at::Tensor input_features, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor weight, at::Tensor &d_output_features, at::Tensor &weight,
at::Tensor d_weight, at::Tensor d_bias); at::Tensor &d_weight, at::Tensor &d_bias);
template <Int Dimension> template <Int Dimension>
void InputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor spatialSize, void InputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor &spatialSize,
at::Tensor input_coords, at::Tensor input_features, at::Tensor &input_coords, at::Tensor &input_features,
at::Tensor output_features, long batchSize, at::Tensor &output_features, long batchSize,
long mode); long mode);
template <Int Dimension> template <Int Dimension>
void InputLayer_updateGradInput(Metadata<Dimension> &m, void InputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features); at::Tensor &d_output_features);
template <Int Dimension> template <Int Dimension>
void OutputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor input_features, void OutputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features); at::Tensor &output_features);
template <Int Dimension> template <Int Dimension>
void OutputLayer_updateGradInput(Metadata<Dimension> &m, void OutputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features); at::Tensor &d_output_features);
template <Int Dimension> template <Int Dimension>
void BLInputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor spatialSize, void BLInputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor &spatialSize,
at::Tensor input_coords, at::Tensor &input_coords,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, long mode); at::Tensor &output_features, long mode);
template <Int Dimension> template <Int Dimension>
void BLInputLayer_updateGradInput(Metadata<Dimension> &m, void BLInputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features); at::Tensor &d_output_features);
template <Int Dimension> template <Int Dimension>
void BLOutputLayer_updateOutput(Metadata<Dimension> &m, void BLOutputLayer_updateOutput(Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features); at::Tensor &output_features);
template <Int Dimension> template <Int Dimension>
void BLOutputLayer_updateGradInput(Metadata<Dimension> &m, void BLOutputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features); at::Tensor &d_output_features);
template <Int Dimension> template <Int Dimension>
void MaxPooling_updateOutput(at::Tensor inputSize, at::Tensor outputSize, void MaxPooling_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor poolSize, at::Tensor poolStride, at::Tensor &poolSize, at::Tensor &poolStride,
Metadata<Dimension> &m, at::Tensor input_features, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, long nFeaturesToDrop); at::Tensor &output_features, long nFeaturesToDrop);
template <Int Dimension> template <Int Dimension>
void MaxPooling_updateGradInput( void MaxPooling_updateGradInput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize,
at::Tensor poolStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &poolStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor output_features, at::Tensor &d_input_features, at::Tensor &output_features,
at::Tensor d_output_features, long nFeaturesToDrop); at::Tensor &d_output_features, long nFeaturesToDrop);
template <Int Dimension> template <Int Dimension>
void RandomizedStrideMaxPooling_updateOutput( void RandomizedStrideMaxPooling_updateOutput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize,
at::Tensor poolStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &poolStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, long nFeaturesToDrop); at::Tensor &output_features, long nFeaturesToDrop);
template <Int Dimension> template <Int Dimension>
void RandomizedStrideMaxPooling_updateGradInput( void RandomizedStrideMaxPooling_updateGradInput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize,
at::Tensor poolStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &poolStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor output_features, at::Tensor &d_input_features, at::Tensor &output_features,
at::Tensor d_output_features, long nFeaturesToDrop); at::Tensor &d_output_features, long nFeaturesToDrop);
template <Int Dimension> template <Int Dimension>
void SparseToDense_updateOutput(at::Tensor inputSize, Metadata<Dimension> &m, void SparseToDense_updateOutput(at::Tensor &inputSize, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, long nPlanes); at::Tensor &output_features, long nPlanes);
template <Int Dimension> template <Int Dimension>
void SparseToDense_updateGradInput(at::Tensor inputSize, Metadata<Dimension> &m, void SparseToDense_updateGradInput(at::Tensor &inputSize, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features); at::Tensor &d_output_features);
template <Int Dimension> template <Int Dimension>
void UnPooling_updateOutput(at::Tensor inputSize, at::Tensor outputSize, void UnPooling_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor poolSize, at::Tensor poolStride, at::Tensor &poolSize, at::Tensor &poolStride,
Metadata<Dimension> &m, at::Tensor input_features, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, long nFeaturesToDrop); at::Tensor &output_features, long nFeaturesToDrop);
template <Int Dimension> template <Int Dimension>
void UnPooling_updateGradInput(at::Tensor inputSize, at::Tensor outputSize, void UnPooling_updateGradInput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor poolSize, at::Tensor poolStride, at::Tensor &poolSize, at::Tensor &poolStride,
Metadata<Dimension> &m, Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor &d_output_features,
long nFeaturesToDrop); long nFeaturesToDrop);
void CopyFeaturesHelper_updateOutput(at::Tensor rules, at::Tensor context, void CopyFeaturesHelper_updateOutput(at::Tensor &rules, at::Tensor &context,
at::Tensor Context); at::Tensor &Context);
void CopyFeaturesHelper_updateGradInput(at::Tensor rules, at::Tensor dcontext, void CopyFeaturesHelper_updateGradInput(at::Tensor &rules, at::Tensor &dcontext,
at::Tensor dContext); at::Tensor &dContext);
sparseconvnet/SCN/sparseconvnet_cpu.cpp
View file @ 2082f213
...@@ -33,20 +33,20 @@ template class Metadata<6>; ...@@ -33,20 +33,20 @@ template class Metadata<6>;
#include "CPU/SparseToDense.cpp" #include "CPU/SparseToDense.cpp"
#include "CPU/UnPooling.cpp" #include "CPU/UnPooling.cpp"
double AffineReluTrivialConvolution_updateOutput(at::Tensor input_features, double AffineReluTrivialConvolution_updateOutput(at::Tensor &input_features,
at::Tensor output_features, at::Tensor &output_features,
at::Tensor affineWeight, at::Tensor &affineWeight,
at::Tensor affineBias, at::Tensor &affineBias,
at::Tensor convWeight) { at::Tensor &convWeight) {
return cpu_AffineReluTrivialConvolution_updateOutput<float>( return cpu_AffineReluTrivialConvolution_updateOutput<float>(
input_features, output_features, affineWeight, affineBias, convWeight); input_features, output_features, affineWeight, affineBias, convWeight);
} }
void AffineReluTrivialConvolution_backward( void AffineReluTrivialConvolution_backward(
at::Tensor input_features, at::Tensor d_input_features, at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor affineWeight, at::Tensor &d_output_features, at::Tensor &affineWeight,
at::Tensor d_affineWeight, at::Tensor affineBias, at::Tensor d_affineBias, at::Tensor &d_affineWeight, at::Tensor &affineBias, at::Tensor &d_affineBias,
at::Tensor convWeight, at::Tensor d_convWeight, bool additiveGrad) { at::Tensor &convWeight, at::Tensor &d_convWeight, bool additiveGrad) {
cpu_AffineReluTrivialConvolution_backward<float>( cpu_AffineReluTrivialConvolution_backward<float>(
input_features, d_input_features, d_output_features, affineWeight, input_features, d_input_features, d_output_features, affineWeight,
d_affineWeight, affineBias, d_affineBias, convWeight, d_convWeight, d_affineWeight, affineBias, d_affineBias, convWeight, d_convWeight,
...@@ -54,9 +54,9 @@ void AffineReluTrivialConvolution_backward( ...@@ -54,9 +54,9 @@ void AffineReluTrivialConvolution_backward(
} }
void BatchNormalization_updateOutput( void BatchNormalization_updateOutput(
at::Tensor input_features, at::Tensor output_features, at::Tensor saveMean, at::Tensor &input_features, at::Tensor &output_features, at::Tensor &saveMean,
at::Tensor saveInvStd, at::Tensor runningMean, at::Tensor runningVar, at::Tensor &saveInvStd, at::Tensor &runningMean, at::Tensor &runningVar,
at::Tensor weight, at::Tensor bias, double eps, double momentum, bool train, at::Tensor &weight, at::Tensor &bias, double eps, double momentum, bool train,
double leakiness) { double leakiness) {
cpu_BatchNormalization_updateOutput<float>( cpu_BatchNormalization_updateOutput<float>(
input_features, output_features, saveMean, saveInvStd, runningMean, input_features, output_features, saveMean, saveInvStd, runningMean,
...@@ -64,182 +64,182 @@ void BatchNormalization_updateOutput( ...@@ -64,182 +64,182 @@ void BatchNormalization_updateOutput(
} }
void BatchNormalization_backward( void BatchNormalization_backward(
at::Tensor input_features, at::Tensor d_input_features, at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor output_features, at::Tensor d_output_features, at::Tensor &output_features, at::Tensor &d_output_features,
at::Tensor saveMean, at::Tensor saveInvStd, at::Tensor runningMean, at::Tensor &saveMean, at::Tensor &saveInvStd, at::Tensor &runningMean,
at::Tensor runningVar, at::Tensor weight, at::Tensor bias, at::Tensor &runningVar, at::Tensor &weight, at::Tensor &bias,
at::Tensor d_weight, at::Tensor d_bias, double leakiness) { at::Tensor &d_weight, at::Tensor &d_bias, double leakiness) {
cpu_BatchNormalization_backward<float>( cpu_BatchNormalization_backward<float>(
input_features, d_input_features, output_features, d_output_features, input_features, d_input_features, output_features, d_output_features,
saveMean, saveInvStd, runningMean, runningVar, weight, bias, d_weight, saveMean, saveInvStd, runningMean, runningVar, weight, bias, d_weight,
d_bias, leakiness); d_bias, leakiness);
} }
void BatchwiseMultiplicativeDropout_updateOutput(at::Tensor input_features, void BatchwiseMultiplicativeDropout_updateOutput(at::Tensor &input_features,
at::Tensor output_features, at::Tensor &output_features,
at::Tensor noise, at::Tensor &noise,
double alpha) { double alpha) {
cpu_BatchwiseMultiplicativeDropout_updateOutput<float>( cpu_BatchwiseMultiplicativeDropout_updateOutput<float>(
input_features, output_features, noise, alpha); input_features, output_features, noise, alpha);
} }
void BatchwiseMultiplicativeDropout_updateGradInput( void BatchwiseMultiplicativeDropout_updateGradInput(
at::Tensor input_features, at::Tensor d_input_features, at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor noise, double alpha) { at::Tensor &d_output_features, at::Tensor &noise, double alpha) {
cpu_BatchwiseMultiplicativeDropout_updateGradInput<float>( cpu_BatchwiseMultiplicativeDropout_updateGradInput<float>(
input_features, d_input_features, d_output_features, noise, alpha); input_features, d_input_features, d_output_features, noise, alpha);
} }
void LeakyReLU_updateOutput(at::Tensor input_features, void LeakyReLU_updateOutput(at::Tensor &input_features,
at::Tensor output_features, double alpha) { at::Tensor &output_features, double alpha) {
cpu_LeakyReLU_updateOutput<float>(input_features, output_features, alpha); cpu_LeakyReLU_updateOutput<float>(input_features, output_features, alpha);
} }
void LeakyReLU_updateGradInput(at::Tensor input_features, void LeakyReLU_updateGradInput(at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, double alpha) { at::Tensor &d_output_features, double alpha) {
cpu_LeakyReLU_updateGradInput<float>(input_features, d_input_features, cpu_LeakyReLU_updateGradInput<float>(input_features, d_input_features,
d_output_features, alpha); d_output_features, alpha);
} }
double NetworkInNetwork_updateOutput(at::Tensor input_features, double NetworkInNetwork_updateOutput(at::Tensor &input_features,
at::Tensor output_features, at::Tensor &output_features,
at::Tensor weight, at::Tensor bias) { at::Tensor &weight, at::Tensor &bias) {
return cpu_NetworkInNetwork_updateOutput<float>( return cpu_NetworkInNetwork_updateOutput<float>(
input_features, output_features, weight, bias); input_features, output_features, weight, bias);
} }
void NetworkInNetwork_updateGradInput(at::Tensor d_input_features, void NetworkInNetwork_updateGradInput(at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor &d_output_features,
at::Tensor weight) { at::Tensor &weight) {
cpu_NetworkInNetwork_updateGradInput<float>(d_input_features, cpu_NetworkInNetwork_updateGradInput<float>(d_input_features,
d_output_features, weight); d_output_features, weight);
} }
void NetworkInNetwork_accGradParameters(at::Tensor input_features, void NetworkInNetwork_accGradParameters(at::Tensor &input_features,
at::Tensor d_output_features, at::Tensor &d_output_features,
at::Tensor d_weight, at::Tensor &d_weight,
at::Tensor d_bias) { at::Tensor &d_bias) {
cpu_NetworkInNetwork_accGradParameters<float>( cpu_NetworkInNetwork_accGradParameters<float>(
input_features, d_output_features, d_weight, d_bias); input_features, d_output_features, d_weight, d_bias);
} }
template <Int Dimension> template <Int Dimension>
void ActivePooling_updateOutput(at::Tensor inputSize, Metadata<Dimension> &m, void ActivePooling_updateOutput(at::Tensor &inputSize, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, bool average) { at::Tensor &output_features, bool average) {
cpu_ActivePooling_updateOutput<float, Dimension>(inputSize, m, input_features, cpu_ActivePooling_updateOutput<float, Dimension>(inputSize, m, input_features,
output_features, average); output_features, average);
} }
template <Int Dimension> template <Int Dimension>
void ActivePooling_updateGradInput(at::Tensor inputSize, Metadata<Dimension> &m, void ActivePooling_updateGradInput(at::Tensor &inputSize, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, bool average) { at::Tensor &d_output_features, bool average) {
return cpu_ActivePooling_updateGradInput<float, Dimension>( return cpu_ActivePooling_updateGradInput<float, Dimension>(
inputSize, m, input_features, d_input_features, d_output_features, inputSize, m, input_features, d_input_features, d_output_features,
average); average);
} }
template <Int Dimension> template <Int Dimension>
void AveragePooling_updateOutput(at::Tensor inputSize, at::Tensor outputSize, void AveragePooling_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor poolSize, at::Tensor poolStride, at::Tensor &poolSize, at::Tensor &poolStride,
Metadata<Dimension> &m, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, at::Tensor &output_features,
long nFeaturesToDrop) { long nFeaturesToDrop) {
cpu_AveragePooling_updateOutput<float, Dimension>( cpu_AveragePooling_updateOutput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features, inputSize, outputSize, poolSize, poolStride, m, input_features,
output_features, nFeaturesToDrop); output_features, nFeaturesToDrop);
} }
template <Int Dimension> template <Int Dimension>
void AveragePooling_updateGradInput(at::Tensor inputSize, at::Tensor outputSize, void AveragePooling_updateGradInput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor poolSize, at::Tensor poolStride, at::Tensor &poolSize, at::Tensor &poolStride,
Metadata<Dimension> &m, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor &d_output_features,
long nFeaturesToDrop) { long nFeaturesToDrop) {
cpu_AveragePooling_updateGradInput<float, Dimension>( cpu_AveragePooling_updateGradInput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features, inputSize, outputSize, poolSize, poolStride, m, input_features,
d_input_features, d_output_features, nFeaturesToDrop); d_input_features, d_output_features, nFeaturesToDrop);
} }
template <Int Dimension> template <Int Dimension>
double Convolution_updateOutput(at::Tensor inputSize, at::Tensor outputSize, double Convolution_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor filterSize, at::Tensor filterStride, at::Tensor &filterSize, at::Tensor &filterStride,
Metadata<Dimension> &m, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, at::Tensor weight, at::Tensor &output_features, at::Tensor &weight,
at::Tensor bias) { at::Tensor &bias) {
return cpu_Convolution_updateOutput<float, Dimension>( return cpu_Convolution_updateOutput<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features, inputSize, outputSize, filterSize, filterStride, m, input_features,
output_features, weight, bias); output_features, weight, bias);
} }
template <Int Dimension> template <Int Dimension>
void Convolution_backward(at::Tensor inputSize, at::Tensor outputSize, void Convolution_backward(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor filterSize, at::Tensor filterStride, at::Tensor &filterSize, at::Tensor &filterStride,
Metadata<Dimension> &m, at::Tensor input_features, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor weight, at::Tensor &d_output_features, at::Tensor &weight,
at::Tensor d_weight, at::Tensor d_bias) { at::Tensor &d_weight, at::Tensor &d_bias) {
cpu_Convolution_backward<float, Dimension>( cpu_Convolution_backward<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features, inputSize, outputSize, filterSize, filterStride, m, input_features,
d_input_features, d_output_features, weight, d_weight, d_bias); d_input_features, d_output_features, weight, d_weight, d_bias);
} }
template <Int Dimension> template <Int Dimension>
double SubmanifoldConvolution_updateOutput(at::Tensor inputSize, double SubmanifoldConvolution_updateOutput(at::Tensor &inputSize,
at::Tensor filterSize, at::Tensor &filterSize,
Metadata<Dimension> &m, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, at::Tensor &output_features,
at::Tensor weight, at::Tensor bias) { at::Tensor &weight, at::Tensor &bias) {
return cpu_SubmanifoldConvolution_updateOutput<float, Dimension>( return cpu_SubmanifoldConvolution_updateOutput<float, Dimension>(
inputSize, filterSize, m, input_features, output_features, weight, bias); inputSize, filterSize, m, input_features, output_features, weight, bias);
} }
template <Int Dimension> template <Int Dimension>
void SubmanifoldConvolution_backward( void SubmanifoldConvolution_backward(
at::Tensor inputSize, at::Tensor filterSize, Metadata<Dimension> &m, at::Tensor &inputSize, at::Tensor &filterSize, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor d_input_features, at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor weight, at::Tensor d_weight, at::Tensor &d_output_features, at::Tensor &weight, at::Tensor &d_weight,
at::Tensor d_bias) { at::Tensor &d_bias) {
cpu_SubmanifoldConvolution_backward<float, Dimension>( cpu_SubmanifoldConvolution_backward<float, Dimension>(
inputSize, filterSize, m, input_features, d_input_features, inputSize, filterSize, m, input_features, d_input_features,
d_output_features, weight, d_weight, d_bias); d_output_features, weight, d_weight, d_bias);
} }
template <Int Dimension> template <Int Dimension>
double PermutohedralSubmanifoldConvolution_updateOutput( double PermutohedralSubmanifoldConvolution_updateOutput(
at::Tensor inputSize, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &inputSize, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, at::Tensor weight, at::Tensor bias) { at::Tensor &output_features, at::Tensor &weight, at::Tensor &bias) {
return cpu_PermutohedralSubmanifoldConvolution_updateOutput<float, Dimension>( return cpu_PermutohedralSubmanifoldConvolution_updateOutput<float, Dimension>(
inputSize, m, input_features, output_features, weight, bias); inputSize, m, input_features, output_features, weight, bias);
} }
template <Int Dimension> template <Int Dimension>
void PermutohedralSubmanifoldConvolution_backward( void PermutohedralSubmanifoldConvolution_backward(
at::Tensor inputSize, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &inputSize, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor d_output_features, at::Tensor &d_input_features, at::Tensor &d_output_features,
at::Tensor weight, at::Tensor d_weight, at::Tensor d_bias) { at::Tensor &weight, at::Tensor &d_weight, at::Tensor &d_bias) {
cpu_PermutohedralSubmanifoldConvolution_backward<float, Dimension>( cpu_PermutohedralSubmanifoldConvolution_backward<float, Dimension>(
inputSize, m, input_features, d_input_features, d_output_features, weight, inputSize, m, input_features, d_input_features, d_output_features, weight,
d_weight, d_bias); d_weight, d_bias);
} }
template <Int Dimension> template <Int Dimension>
double FullConvolution_updateOutput( double FullConvolution_updateOutput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize,
at::Tensor filterStride, Metadata<Dimension> &mIn, at::Tensor &filterStride, Metadata<Dimension> &mIn,
Metadata<Dimension> &mOut, at::Tensor input_features, Metadata<Dimension> &mOut, at::Tensor &input_features,
at::Tensor output_features, at::Tensor weight, at::Tensor bias) { at::Tensor &output_features, at::Tensor &weight, at::Tensor &bias) {
return cpu_FullConvolution_updateOutput<float, Dimension>( return cpu_FullConvolution_updateOutput<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, mIn, mOut, inputSize, outputSize, filterSize, filterStride, mIn, mOut,
input_features, output_features, weight, bias); input_features, output_features, weight, bias);
} }
template <Int Dimension> template <Int Dimension>
void FullConvolution_backward(at::Tensor inputSize, at::Tensor outputSize, void FullConvolution_backward(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor filterSize, at::Tensor filterStride, at::Tensor &filterSize, at::Tensor &filterStride,
Metadata<Dimension> &mIn, Metadata<Dimension> &mIn,
Metadata<Dimension> &mOut, Metadata<Dimension> &mOut,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor weight, at::Tensor &d_output_features, at::Tensor &weight,
at::Tensor d_weight, at::Tensor d_bias) { at::Tensor &d_weight, at::Tensor &d_bias) {
cpu_FullConvolution_backward<float, Dimension>( cpu_FullConvolution_backward<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, mIn, mOut, inputSize, outputSize, filterSize, filterStride, mIn, mOut,
input_features, d_input_features, d_output_features, weight, d_weight, input_features, d_input_features, d_output_features, weight, d_weight,
...@@ -247,47 +247,47 @@ void FullConvolution_backward(at::Tensor inputSize, at::Tensor outputSize, ...@@ -247,47 +247,47 @@ void FullConvolution_backward(at::Tensor inputSize, at::Tensor outputSize,
} }
template <Int Dimension> template <Int Dimension>
double RandomizedStrideConvolution_updateOutput( double RandomizedStrideConvolution_updateOutput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize,
at::Tensor filterStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &filterStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, at::Tensor weight, at::Tensor bias) { at::Tensor &output_features, at::Tensor &weight, at::Tensor &bias) {
return cpu_RandomizedStrideConvolution_updateOutput<float, Dimension>( return cpu_RandomizedStrideConvolution_updateOutput<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features, inputSize, outputSize, filterSize, filterStride, m, input_features,
output_features, weight, bias); output_features, weight, bias);
} }
template <Int Dimension> template <Int Dimension>
void RandomizedStrideConvolution_backward( void RandomizedStrideConvolution_backward(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize,
at::Tensor filterStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &filterStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor d_output_features, at::Tensor &d_input_features, at::Tensor &d_output_features,
at::Tensor weight, at::Tensor d_weight, at::Tensor d_bias) { at::Tensor &weight, at::Tensor &d_weight, at::Tensor &d_bias) {
cpu_RandomizedStrideConvolution_backward<float, Dimension>( cpu_RandomizedStrideConvolution_backward<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features, inputSize, outputSize, filterSize, filterStride, m, input_features,
d_input_features, d_output_features, weight, d_weight, d_bias); d_input_features, d_output_features, weight, d_weight, d_bias);
} }
template <Int Dimension> template <Int Dimension>
double Deconvolution_updateOutput( double Deconvolution_updateOutput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize,
at::Tensor filterStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &filterStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, at::Tensor weight, at::Tensor bias) { at::Tensor &output_features, at::Tensor &weight, at::Tensor &bias) {
return cpu_Deconvolution_updateOutput<float, Dimension>( return cpu_Deconvolution_updateOutput<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features, inputSize, outputSize, filterSize, filterStride, m, input_features,
output_features, weight, bias); output_features, weight, bias);
} }
template <Int Dimension> template <Int Dimension>
void Deconvolution_backward(at::Tensor inputSize, at::Tensor outputSize, void Deconvolution_backward(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor filterSize, at::Tensor filterStride, at::Tensor &filterSize, at::Tensor &filterStride,
Metadata<Dimension> &m, at::Tensor input_features, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor weight, at::Tensor &d_output_features, at::Tensor &weight,
at::Tensor d_weight, at::Tensor d_bias) { at::Tensor &d_weight, at::Tensor &d_bias) {
cpu_Deconvolution_backward<float, Dimension>( cpu_Deconvolution_backward<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features, inputSize, outputSize, filterSize, filterStride, m, input_features,
d_input_features, d_output_features, weight, d_weight, d_bias); d_input_features, d_output_features, weight, d_weight, d_bias);
} }
template <Int Dimension> template <Int Dimension>
void InputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor spatialSize, void InputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor &spatialSize,
at::Tensor input_coords, at::Tensor input_features, at::Tensor &input_coords, at::Tensor &input_features,
at::Tensor output_features, long batchSize, at::Tensor &output_features, long batchSize,
long mode) { long mode) {
cpu_InputLayer_updateOutput<float, Dimension>(m, spatialSize, input_coords, cpu_InputLayer_updateOutput<float, Dimension>(m, spatialSize, input_coords,
input_features, output_features, input_features, output_features,
...@@ -295,121 +295,121 @@ void InputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor spatialSize, ...@@ -295,121 +295,121 @@ void InputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor spatialSize,
} }
template <Int Dimension> template <Int Dimension>
void InputLayer_updateGradInput(Metadata<Dimension> &m, void InputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features) { at::Tensor &d_output_features) {
cpu_InputLayer_updateGradInput<float, Dimension>(m, d_input_features, cpu_InputLayer_updateGradInput<float, Dimension>(m, d_input_features,
d_output_features); d_output_features);
} }
template <Int Dimension> template <Int Dimension>
void OutputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor input_features, void OutputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features) { at::Tensor &output_features) {
cpu_OutputLayer_updateOutput<float, Dimension>(m, input_features, cpu_OutputLayer_updateOutput<float, Dimension>(m, input_features,
output_features); output_features);
} }
template <Int Dimension> template <Int Dimension>
void OutputLayer_updateGradInput(Metadata<Dimension> &m, void OutputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features) { at::Tensor &d_output_features) {
cpu_OutputLayer_updateGradInput<float, Dimension>(m, d_input_features, cpu_OutputLayer_updateGradInput<float, Dimension>(m, d_input_features,
d_output_features); d_output_features);
} }
template <Int Dimension> template <Int Dimension>
void BLInputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor spatialSize, void BLInputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor &spatialSize,
at::Tensor input_coords, at::Tensor &input_coords,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, long mode) { at::Tensor &output_features, long mode) {
cpu_BLInputLayer_updateOutput<float, Dimension>( cpu_BLInputLayer_updateOutput<float, Dimension>(
m, spatialSize, input_coords, input_features, output_features, mode); m, spatialSize, input_coords, input_features, output_features, mode);
} }
template <Int Dimension> template <Int Dimension>
void BLInputLayer_updateGradInput(Metadata<Dimension> &m, void BLInputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features) { at::Tensor &d_output_features) {
cpu_BLInputLayer_updateGradInput<float, Dimension>(m, d_input_features, cpu_BLInputLayer_updateGradInput<float, Dimension>(m, d_input_features,
d_output_features); d_output_features);
} }
template <Int Dimension> template <Int Dimension>
void BLOutputLayer_updateOutput(Metadata<Dimension> &m, void BLOutputLayer_updateOutput(Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features) { at::Tensor &output_features) {
cpu_BLOutputLayer_updateOutput<float, Dimension>(m, input_features, cpu_BLOutputLayer_updateOutput<float, Dimension>(m, input_features,
output_features); output_features);
} }
template <Int Dimension> template <Int Dimension>
void BLOutputLayer_updateGradInput(Metadata<Dimension> &m, void BLOutputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features) { at::Tensor &d_output_features) {
cpu_BLOutputLayer_updateGradInput<float, Dimension>(m, d_input_features, cpu_BLOutputLayer_updateGradInput<float, Dimension>(m, d_input_features,
d_output_features); d_output_features);
} }
template <Int Dimension> template <Int Dimension>
void MaxPooling_updateOutput(at::Tensor inputSize, at::Tensor outputSize, void MaxPooling_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor poolSize, at::Tensor poolStride, at::Tensor &poolSize, at::Tensor &poolStride,
Metadata<Dimension> &m, at::Tensor input_features, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, long nFeaturesToDrop) { at::Tensor &output_features, long nFeaturesToDrop) {
cpu_MaxPooling_updateOutput<float, Dimension>( cpu_MaxPooling_updateOutput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features, inputSize, outputSize, poolSize, poolStride, m, input_features,
output_features, nFeaturesToDrop); output_features, nFeaturesToDrop);
} }
template <Int Dimension> template <Int Dimension>
void MaxPooling_updateGradInput( void MaxPooling_updateGradInput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize,
at::Tensor poolStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &poolStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor output_features, at::Tensor &d_input_features, at::Tensor &output_features,
at::Tensor d_output_features, long nFeaturesToDrop) { at::Tensor &d_output_features, long nFeaturesToDrop) {
cpu_MaxPooling_updateGradInput<float, Dimension>( cpu_MaxPooling_updateGradInput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features, inputSize, outputSize, poolSize, poolStride, m, input_features,
d_input_features, output_features, d_output_features, nFeaturesToDrop); d_input_features, output_features, d_output_features, nFeaturesToDrop);
} }
template <Int Dimension> template <Int Dimension>
void RandomizedStrideMaxPooling_updateOutput( void RandomizedStrideMaxPooling_updateOutput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize,
at::Tensor poolStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &poolStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, long nFeaturesToDrop) { at::Tensor &output_features, long nFeaturesToDrop) {
cpu_RandomizedStrideMaxPooling_updateOutput<float, Dimension>( cpu_RandomizedStrideMaxPooling_updateOutput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features, inputSize, outputSize, poolSize, poolStride, m, input_features,
output_features, nFeaturesToDrop); output_features, nFeaturesToDrop);
} }
template <Int Dimension> template <Int Dimension>
void RandomizedStrideMaxPooling_updateGradInput( void RandomizedStrideMaxPooling_updateGradInput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize,
at::Tensor poolStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &poolStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor output_features, at::Tensor &d_input_features, at::Tensor &output_features,
at::Tensor d_output_features, long nFeaturesToDrop) { at::Tensor &d_output_features, long nFeaturesToDrop) {
cpu_RandomizedStrideMaxPooling_updateGradInput<float, Dimension>( cpu_RandomizedStrideMaxPooling_updateGradInput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features, inputSize, outputSize, poolSize, poolStride, m, input_features,
d_input_features, output_features, d_output_features, nFeaturesToDrop); d_input_features, output_features, d_output_features, nFeaturesToDrop);
} }
template <Int Dimension> template <Int Dimension>
void SparseToDense_updateOutput(at::Tensor inputSize, Metadata<Dimension> &m, void SparseToDense_updateOutput(at::Tensor &inputSize, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, long nPlanes) { at::Tensor &output_features, long nPlanes) {
cpu_SparseToDense_updateOutput<float, Dimension>(inputSize, m, input_features, cpu_SparseToDense_updateOutput<float, Dimension>(inputSize, m, input_features,
output_features, nPlanes); output_features, nPlanes);
} }
template <Int Dimension> template <Int Dimension>
void SparseToDense_updateGradInput(at::Tensor inputSize, Metadata<Dimension> &m, void SparseToDense_updateGradInput(at::Tensor &inputSize, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features) { at::Tensor &d_output_features) {
cpu_SparseToDense_updateGradInput<float, Dimension>( cpu_SparseToDense_updateGradInput<float, Dimension>(
inputSize, m, input_features, d_input_features, d_output_features); inputSize, m, input_features, d_input_features, d_output_features);
} }
template <Int Dimension> template <Int Dimension>
void UnPooling_updateOutput(at::Tensor inputSize, at::Tensor outputSize, void UnPooling_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor poolSize, at::Tensor poolStride, at::Tensor &poolSize, at::Tensor &poolStride,
Metadata<Dimension> &m, at::Tensor input_features, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, long nFeaturesToDrop) { at::Tensor &output_features, long nFeaturesToDrop) {
cpu_UnPooling_updateOutput<float, Dimension>( cpu_UnPooling_updateOutput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features, inputSize, outputSize, poolSize, poolStride, m, input_features,
output_features, nFeaturesToDrop); output_features, nFeaturesToDrop);
} }
template <Int Dimension> template <Int Dimension>
void UnPooling_updateGradInput(at::Tensor inputSize, at::Tensor outputSize, void UnPooling_updateGradInput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor poolSize, at::Tensor poolStride, at::Tensor &poolSize, at::Tensor &poolStride,
Metadata<Dimension> &m, Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor &d_output_features,
long nFeaturesToDrop) { long nFeaturesToDrop) {
cpu_UnPooling_updateGradInput<float, Dimension>( cpu_UnPooling_updateGradInput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, d_input_features, inputSize, outputSize, poolSize, poolStride, m, d_input_features,
...@@ -418,148 +418,148 @@ void UnPooling_updateGradInput(at::Tensor inputSize, at::Tensor outputSize, ...@@ -418,148 +418,148 @@ void UnPooling_updateGradInput(at::Tensor inputSize, at::Tensor outputSize,
#define FOO \ #define FOO \
template void ActivePooling_updateOutput<DIMENSION>( \ template void ActivePooling_updateOutput<DIMENSION>( \
at::Tensor inputSize, Metadata<DIMENSION> & m, \ at::Tensor &inputSize, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, bool average); \ at::Tensor &input_features, at::Tensor &output_features, bool average); \
template void ActivePooling_updateGradInput<DIMENSION>( \ template void ActivePooling_updateGradInput<DIMENSION>( \
at::Tensor inputSize, Metadata<DIMENSION> & m, \ at::Tensor &inputSize, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor &input_features, at::Tensor &d_input_features, \
at::Tensor d_output_features, bool average); \ at::Tensor &d_output_features, bool average); \
template void AveragePooling_updateOutput<DIMENSION>( \ template void AveragePooling_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor &poolStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, \ at::Tensor &input_features, at::Tensor &output_features, \
long nFeaturesToDrop); \ long nFeaturesToDrop); \
template void AveragePooling_updateGradInput<DIMENSION>( \ template void AveragePooling_updateGradInput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor &poolStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor &input_features, at::Tensor &d_input_features, \
at::Tensor d_output_features, long nFeaturesToDrop); \ at::Tensor &d_output_features, long nFeaturesToDrop); \
template double Convolution_updateOutput<DIMENSION>( \ template double Convolution_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize, \
at::Tensor filterStride, Metadata<DIMENSION> & m, \ at::Tensor &filterStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, \ at::Tensor &input_features, at::Tensor &output_features, \
at::Tensor weight, at::Tensor bias); \ at::Tensor &weight, at::Tensor &bias); \
template void Convolution_backward<DIMENSION>( \ template void Convolution_backward<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize, \
at::Tensor filterStride, Metadata<DIMENSION> & m, \ at::Tensor &filterStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor &input_features, at::Tensor &d_input_features, \
at::Tensor d_output_features, at::Tensor weight, at::Tensor d_weight, \ at::Tensor &d_output_features, at::Tensor &weight, at::Tensor &d_weight, \
at::Tensor d_bias); \ at::Tensor &d_bias); \
template double SubmanifoldConvolution_updateOutput<DIMENSION>( \ template double SubmanifoldConvolution_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor filterSize, Metadata<DIMENSION> & m, \ at::Tensor &inputSize, at::Tensor &filterSize, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, \ at::Tensor &input_features, at::Tensor &output_features, \
at::Tensor weight, at::Tensor bias); \ at::Tensor &weight, at::Tensor &bias); \
template void SubmanifoldConvolution_backward<DIMENSION>( \ template void SubmanifoldConvolution_backward<DIMENSION>( \
at::Tensor inputSize, at::Tensor filterSize, Metadata<DIMENSION> & m, \ at::Tensor &inputSize, at::Tensor &filterSize, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor &input_features, at::Tensor &d_input_features, \
at::Tensor d_output_features, at::Tensor weight, at::Tensor d_weight, \ at::Tensor &d_output_features, at::Tensor &weight, at::Tensor &d_weight, \
at::Tensor d_bias); \ at::Tensor &d_bias); \
template double PermutohedralSubmanifoldConvolution_updateOutput<DIMENSION>( \ template double PermutohedralSubmanifoldConvolution_updateOutput<DIMENSION>( \
at::Tensor inputSize, Metadata<DIMENSION> & m, \ at::Tensor &inputSize, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, \ at::Tensor &input_features, at::Tensor &output_features, \
at::Tensor weight, at::Tensor bias); \ at::Tensor &weight, at::Tensor &bias); \
template void PermutohedralSubmanifoldConvolution_backward<DIMENSION>( \ template void PermutohedralSubmanifoldConvolution_backward<DIMENSION>( \
at::Tensor inputSize, Metadata<DIMENSION> & m, \ at::Tensor &inputSize, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor &input_features, at::Tensor &d_input_features, \
at::Tensor d_output_features, at::Tensor weight, at::Tensor d_weight, \ at::Tensor &d_output_features, at::Tensor &weight, at::Tensor &d_weight, \
at::Tensor d_bias); \ at::Tensor &d_bias); \
template double FullConvolution_updateOutput<DIMENSION>( \ template double FullConvolution_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize, \
at::Tensor filterStride, Metadata<DIMENSION> & mIn, \ at::Tensor &filterStride, Metadata<DIMENSION> & mIn, \
Metadata<DIMENSION> & mOut, at::Tensor input_features, \ Metadata<DIMENSION> & mOut, at::Tensor &input_features, \
at::Tensor output_features, at::Tensor weight, at::Tensor bias); \ at::Tensor &output_features, at::Tensor &weight, at::Tensor &bias); \
template void FullConvolution_backward<DIMENSION>( \ template void FullConvolution_backward<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize, \
at::Tensor filterStride, Metadata<DIMENSION> & mIn, \ at::Tensor &filterStride, Metadata<DIMENSION> & mIn, \
Metadata<DIMENSION> & mOut, at::Tensor input_features, \ Metadata<DIMENSION> & mOut, at::Tensor &input_features, \
at::Tensor d_input_features, at::Tensor d_output_features, \ at::Tensor &d_input_features, at::Tensor &d_output_features, \
at::Tensor weight, at::Tensor d_weight, at::Tensor d_bias); \ at::Tensor &weight, at::Tensor &d_weight, at::Tensor &d_bias); \
template double RandomizedStrideConvolution_updateOutput<DIMENSION>( \ template double RandomizedStrideConvolution_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize, \
at::Tensor filterStride, Metadata<DIMENSION> & m, \ at::Tensor &filterStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, \ at::Tensor &input_features, at::Tensor &output_features, \
at::Tensor weight, at::Tensor bias); \ at::Tensor &weight, at::Tensor &bias); \
template void RandomizedStrideConvolution_backward<DIMENSION>( \ template void RandomizedStrideConvolution_backward<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize, \
at::Tensor filterStride, Metadata<DIMENSION> & m, \ at::Tensor &filterStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor &input_features, at::Tensor &d_input_features, \
at::Tensor d_output_features, at::Tensor weight, at::Tensor d_weight, \ at::Tensor &d_output_features, at::Tensor &weight, at::Tensor &d_weight, \
at::Tensor d_bias); \ at::Tensor &d_bias); \
template double Deconvolution_updateOutput<DIMENSION>( \ template double Deconvolution_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize, \
at::Tensor filterStride, Metadata<DIMENSION> & m, \ at::Tensor &filterStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, \ at::Tensor &input_features, at::Tensor &output_features, \
at::Tensor weight, at::Tensor bias); \ at::Tensor &weight, at::Tensor &bias); \
template void Deconvolution_backward<DIMENSION>( \ template void Deconvolution_backward<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize, \
at::Tensor filterStride, Metadata<DIMENSION> & m, \ at::Tensor &filterStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor &input_features, at::Tensor &d_input_features, \
at::Tensor d_output_features, at::Tensor weight, at::Tensor d_weight, \ at::Tensor &d_output_features, at::Tensor &weight, at::Tensor &d_weight, \
at::Tensor d_bias); \ at::Tensor &d_bias); \
template void InputLayer_updateOutput<DIMENSION>( \ template void InputLayer_updateOutput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor spatialSize, \ Metadata<DIMENSION> & m, at::Tensor &spatialSize, \
at::Tensor input_coords, at::Tensor input_features, \ at::Tensor &input_coords, at::Tensor &input_features, \
at::Tensor output_features, long batchSize, long mode); \ at::Tensor &output_features, long batchSize, long mode); \
template void InputLayer_updateGradInput<DIMENSION>( \ template void InputLayer_updateGradInput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor d_input_features, \ Metadata<DIMENSION> & m, at::Tensor &d_input_features, \
at::Tensor d_output_features); \ at::Tensor &d_output_features); \
template void OutputLayer_updateOutput<DIMENSION>( \ template void OutputLayer_updateOutput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor input_features, \ Metadata<DIMENSION> & m, at::Tensor &input_features, \
at::Tensor output_features); \ at::Tensor &output_features); \
template void OutputLayer_updateGradInput<DIMENSION>( \ template void OutputLayer_updateGradInput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor d_input_features, \ Metadata<DIMENSION> & m, at::Tensor &d_input_features, \
at::Tensor d_output_features); \ at::Tensor &d_output_features); \
template void BLInputLayer_updateOutput<DIMENSION>( \ template void BLInputLayer_updateOutput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor spatialSize, \ Metadata<DIMENSION> & m, at::Tensor &spatialSize, \
at::Tensor input_coords, at::Tensor input_features, \ at::Tensor &input_coords, at::Tensor &input_features, \
at::Tensor output_features, long mode); \ at::Tensor &output_features, long mode); \
template void BLInputLayer_updateGradInput<DIMENSION>( \ template void BLInputLayer_updateGradInput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor d_input_features, \ Metadata<DIMENSION> & m, at::Tensor &d_input_features, \
at::Tensor d_output_features); \ at::Tensor &d_output_features); \
template void BLOutputLayer_updateOutput<DIMENSION>( \ template void BLOutputLayer_updateOutput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor input_features, \ Metadata<DIMENSION> & m, at::Tensor &input_features, \
at::Tensor output_features); \ at::Tensor &output_features); \
template void BLOutputLayer_updateGradInput<DIMENSION>( \ template void BLOutputLayer_updateGradInput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor d_input_features, \ Metadata<DIMENSION> & m, at::Tensor &d_input_features, \
at::Tensor d_output_features); \ at::Tensor &d_output_features); \
template void MaxPooling_updateOutput<DIMENSION>( \ template void MaxPooling_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor &poolStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, \ at::Tensor &input_features, at::Tensor &output_features, \
long nFeaturesToDrop); \ long nFeaturesToDrop); \
template void MaxPooling_updateGradInput<DIMENSION>( \ template void MaxPooling_updateGradInput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor &poolStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor &input_features, at::Tensor &d_input_features, \
at::Tensor output_features, at::Tensor d_output_features, \ at::Tensor &output_features, at::Tensor &d_output_features, \
long nFeaturesToDrop); \ long nFeaturesToDrop); \
template void RandomizedStrideMaxPooling_updateOutput<DIMENSION>( \ template void RandomizedStrideMaxPooling_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor &poolStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, \ at::Tensor &input_features, at::Tensor &output_features, \
long nFeaturesToDrop); \ long nFeaturesToDrop); \
template void RandomizedStrideMaxPooling_updateGradInput<DIMENSION>( \ template void RandomizedStrideMaxPooling_updateGradInput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor &poolStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor &input_features, at::Tensor &d_input_features, \
at::Tensor output_features, at::Tensor d_output_features, \ at::Tensor &output_features, at::Tensor &d_output_features, \
long nFeaturesToDrop); \ long nFeaturesToDrop); \
template void SparseToDense_updateOutput<DIMENSION>( \ template void SparseToDense_updateOutput<DIMENSION>( \
at::Tensor inputSize, Metadata<DIMENSION> & m, \ at::Tensor &inputSize, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, long nPlanes); \ at::Tensor &input_features, at::Tensor &output_features, long nPlanes); \
template void SparseToDense_updateGradInput<DIMENSION>( \ template void SparseToDense_updateGradInput<DIMENSION>( \
at::Tensor inputSize, Metadata<DIMENSION> & m, \ at::Tensor &inputSize, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor &input_features, at::Tensor &d_input_features, \
at::Tensor d_output_features); \ at::Tensor &d_output_features); \
template void UnPooling_updateOutput<DIMENSION>( \ template void UnPooling_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor &poolStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, \ at::Tensor &input_features, at::Tensor &output_features, \
long nFeaturesToDrop); \ long nFeaturesToDrop); \
template void UnPooling_updateGradInput<DIMENSION>( \ template void UnPooling_updateGradInput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor &poolStride, Metadata<DIMENSION> & m, \
at::Tensor d_input_features, at::Tensor d_output_features, \ at::Tensor &d_input_features, at::Tensor &d_output_features, \
long nFeaturesToDrop); long nFeaturesToDrop);
#define DIMENSION 1 #define DIMENSION 1
...@@ -581,11 +581,11 @@ FOO; ...@@ -581,11 +581,11 @@ FOO;
FOO; FOO;
#undef DIMENSION #undef DIMENSION
void CopyFeaturesHelper_updateOutput(at::Tensor rules, at::Tensor context, void CopyFeaturesHelper_updateOutput(at::Tensor &rules, at::Tensor &context,
at::Tensor Context) { at::Tensor &Context) {
cpu_CopyFeaturesHelper_updateOutput<float>(rules, context, Context); cpu_CopyFeaturesHelper_updateOutput<float>(rules, context, Context);
} }
void CopyFeaturesHelper_updateGradInput(at::Tensor rules, at::Tensor dcontext, void CopyFeaturesHelper_updateGradInput(at::Tensor &rules, at::Tensor &dcontext,
at::Tensor dContext) { at::Tensor &dContext) {
cpu_CopyFeaturesHelper_updateGradInput<float>(rules, dcontext, dContext); cpu_CopyFeaturesHelper_updateGradInput<float>(rules, dcontext, dContext);
} }
sparseconvnet/SCN/sparseconvnet_cuda.cpp
View file @ 2082f213
...@@ -48,11 +48,11 @@ template class Metadata<6>; ...@@ -48,11 +48,11 @@ template class Metadata<6>;
#include "CUDA/SparseToDense.cpp" #include "CUDA/SparseToDense.cpp"
#include "CUDA/UnPooling.cpp" #include "CUDA/UnPooling.cpp"
double AffineReluTrivialConvolution_updateOutput(at::Tensor input_features, double AffineReluTrivialConvolution_updateOutput(at::Tensor &input_features,
at::Tensor output_features, at::Tensor &output_features,
at::Tensor affineWeight, at::Tensor &affineWeight,
at::Tensor affineBias, at::Tensor &affineBias,
at::Tensor convWeight) { at::Tensor &convWeight) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
return cuda_AffineReluTrivialConvolution_updateOutput<float>( return cuda_AffineReluTrivialConvolution_updateOutput<float>(
input_features, output_features, affineWeight, affineBias, convWeight); input_features, output_features, affineWeight, affineBias, convWeight);
...@@ -62,10 +62,11 @@ double AffineReluTrivialConvolution_updateOutput(at::Tensor input_features, ...@@ -62,10 +62,11 @@ double AffineReluTrivialConvolution_updateOutput(at::Tensor input_features,
} }
void AffineReluTrivialConvolution_backward( void AffineReluTrivialConvolution_backward(
at::Tensor input_features, at::Tensor d_input_features, at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor affineWeight, at::Tensor &d_output_features, at::Tensor &affineWeight,
at::Tensor d_affineWeight, at::Tensor affineBias, at::Tensor d_affineBias, at::Tensor &d_affineWeight, at::Tensor &affineBias,
at::Tensor convWeight, at::Tensor d_convWeight, bool additiveGrad) { at::Tensor &d_affineBias, at::Tensor &convWeight, at::Tensor &d_convWeight,
bool additiveGrad) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_AffineReluTrivialConvolution_backward<float>( cuda_AffineReluTrivialConvolution_backward<float>(
input_features, d_input_features, d_output_features, affineWeight, input_features, d_input_features, d_output_features, affineWeight,
...@@ -79,10 +80,10 @@ void AffineReluTrivialConvolution_backward( ...@@ -79,10 +80,10 @@ void AffineReluTrivialConvolution_backward(
} }
void BatchNormalization_updateOutput( void BatchNormalization_updateOutput(
at::Tensor input_features, at::Tensor output_features, at::Tensor saveMean, at::Tensor &input_features, at::Tensor &output_features,
at::Tensor saveInvStd, at::Tensor runningMean, at::Tensor runningVar, at::Tensor &saveMean, at::Tensor &saveInvStd, at::Tensor &runningMean,
at::Tensor weight, at::Tensor bias, double eps, double momentum, bool train, at::Tensor &runningVar, at::Tensor &weight, at::Tensor &bias, double eps,
double leakiness) { double momentum, bool train, double leakiness) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
cuda_BatchNormalization_updateOutput<float>( cuda_BatchNormalization_updateOutput<float>(
input_features, output_features, saveMean, saveInvStd, runningMean, input_features, output_features, saveMean, saveInvStd, runningMean,
...@@ -94,11 +95,11 @@ void BatchNormalization_updateOutput( ...@@ -94,11 +95,11 @@ void BatchNormalization_updateOutput(
} }
void BatchNormalization_backward( void BatchNormalization_backward(
at::Tensor input_features, at::Tensor d_input_features, at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor output_features, at::Tensor d_output_features, at::Tensor &output_features, at::Tensor &d_output_features,
at::Tensor saveMean, at::Tensor saveInvStd, at::Tensor runningMean, at::Tensor &saveMean, at::Tensor &saveInvStd, at::Tensor &runningMean,
at::Tensor runningVar, at::Tensor weight, at::Tensor bias, at::Tensor &runningVar, at::Tensor &weight, at::Tensor &bias,
at::Tensor d_weight, at::Tensor d_bias, double leakiness) { at::Tensor &d_weight, at::Tensor &d_bias, double leakiness) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_BatchNormalization_backward<float>( cuda_BatchNormalization_backward<float>(
input_features, d_input_features, output_features, d_output_features, input_features, d_input_features, output_features, d_output_features,
...@@ -111,9 +112,9 @@ void BatchNormalization_backward( ...@@ -111,9 +112,9 @@ void BatchNormalization_backward(
d_bias, leakiness); d_bias, leakiness);
} }
void BatchwiseMultiplicativeDropout_updateOutput(at::Tensor input_features, void BatchwiseMultiplicativeDropout_updateOutput(at::Tensor &input_features,
at::Tensor output_features, at::Tensor &output_features,
at::Tensor noise, at::Tensor &noise,
double alpha) { double alpha) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
cuda_BatchwiseMultiplicativeDropout_updateOutput<float>( cuda_BatchwiseMultiplicativeDropout_updateOutput<float>(
...@@ -124,8 +125,8 @@ void BatchwiseMultiplicativeDropout_updateOutput(at::Tensor input_features, ...@@ -124,8 +125,8 @@ void BatchwiseMultiplicativeDropout_updateOutput(at::Tensor input_features,
} }
void BatchwiseMultiplicativeDropout_updateGradInput( void BatchwiseMultiplicativeDropout_updateGradInput(
at::Tensor input_features, at::Tensor d_input_features, at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor noise, double alpha) { at::Tensor &d_output_features, at::Tensor &noise, double alpha) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_BatchwiseMultiplicativeDropout_updateGradInput<float>( cuda_BatchwiseMultiplicativeDropout_updateGradInput<float>(
input_features, d_input_features, d_output_features, noise, alpha); input_features, d_input_features, d_output_features, noise, alpha);
...@@ -134,17 +135,17 @@ void BatchwiseMultiplicativeDropout_updateGradInput( ...@@ -134,17 +135,17 @@ void BatchwiseMultiplicativeDropout_updateGradInput(
input_features, d_input_features, d_output_features, noise, alpha); input_features, d_input_features, d_output_features, noise, alpha);
} }
void LeakyReLU_updateOutput(at::Tensor input_features, void LeakyReLU_updateOutput(at::Tensor &input_features,
at::Tensor output_features, double alpha) { at::Tensor &output_features, double alpha) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
cuda_LeakyReLU_updateOutput<float>(input_features, output_features, alpha); cuda_LeakyReLU_updateOutput<float>(input_features, output_features, alpha);
else else
cpu_LeakyReLU_updateOutput<float>(input_features, output_features, alpha); cpu_LeakyReLU_updateOutput<float>(input_features, output_features, alpha);
} }
void LeakyReLU_updateGradInput(at::Tensor input_features, void LeakyReLU_updateGradInput(at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, double alpha) { at::Tensor &d_output_features, double alpha) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_LeakyReLU_updateGradInput<float>(input_features, d_input_features, cuda_LeakyReLU_updateGradInput<float>(input_features, d_input_features,
d_output_features, alpha); d_output_features, alpha);
...@@ -153,9 +154,9 @@ void LeakyReLU_updateGradInput(at::Tensor input_features, ...@@ -153,9 +154,9 @@ void LeakyReLU_updateGradInput(at::Tensor input_features,
d_output_features, alpha); d_output_features, alpha);
} }
double NetworkInNetwork_updateOutput(at::Tensor input_features, double NetworkInNetwork_updateOutput(at::Tensor &input_features,
at::Tensor output_features, at::Tensor &output_features,
at::Tensor weight, at::Tensor bias) { at::Tensor &weight, at::Tensor &bias) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
return cuda_NetworkInNetwork_updateOutput<float>( return cuda_NetworkInNetwork_updateOutput<float>(
input_features, output_features, weight, bias); input_features, output_features, weight, bias);
...@@ -164,9 +165,9 @@ double NetworkInNetwork_updateOutput(at::Tensor input_features, ...@@ -164,9 +165,9 @@ double NetworkInNetwork_updateOutput(at::Tensor input_features,
input_features, output_features, weight, bias); input_features, output_features, weight, bias);
} }
void NetworkInNetwork_updateGradInput(at::Tensor d_input_features, void NetworkInNetwork_updateGradInput(at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor &d_output_features,
at::Tensor weight) { at::Tensor &weight) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_NetworkInNetwork_updateGradInput<float>(d_input_features, cuda_NetworkInNetwork_updateGradInput<float>(d_input_features,
d_output_features, weight); d_output_features, weight);
...@@ -175,10 +176,10 @@ void NetworkInNetwork_updateGradInput(at::Tensor d_input_features, ...@@ -175,10 +176,10 @@ void NetworkInNetwork_updateGradInput(at::Tensor d_input_features,
d_output_features, weight); d_output_features, weight);
} }
void NetworkInNetwork_accGradParameters(at::Tensor input_features, void NetworkInNetwork_accGradParameters(at::Tensor &input_features,
at::Tensor d_output_features, at::Tensor &d_output_features,
at::Tensor d_weight, at::Tensor &d_weight,
at::Tensor d_bias) { at::Tensor &d_bias) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_NetworkInNetwork_accGradParameters<float>( cuda_NetworkInNetwork_accGradParameters<float>(
input_features, d_output_features, d_weight, d_bias); input_features, d_output_features, d_weight, d_bias);
...@@ -187,9 +188,9 @@ void NetworkInNetwork_accGradParameters(at::Tensor input_features, ...@@ -187,9 +188,9 @@ void NetworkInNetwork_accGradParameters(at::Tensor input_features,
input_features, d_output_features, d_weight, d_bias); input_features, d_output_features, d_weight, d_bias);
} }
template <Int Dimension> template <Int Dimension>
void ActivePooling_updateOutput(at::Tensor inputSize, Metadata<Dimension> &m, void ActivePooling_updateOutput(at::Tensor &inputSize, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, bool average) { at::Tensor &output_features, bool average) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
cuda_ActivePooling_updateOutput<float, Dimension>( cuda_ActivePooling_updateOutput<float, Dimension>(
inputSize, m, input_features, output_features, average); inputSize, m, input_features, output_features, average);
...@@ -199,10 +200,9 @@ void ActivePooling_updateOutput(at::Tensor inputSize, Metadata<Dimension> &m, ...@@ -199,10 +200,9 @@ void ActivePooling_updateOutput(at::Tensor inputSize, Metadata<Dimension> &m,
} }
template <Int Dimension> template <Int Dimension>
void ActivePooling_updateGradInput(at::Tensor inputSize, Metadata<Dimension> &m, void ActivePooling_updateGradInput(
at::Tensor input_features, at::Tensor &inputSize, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features, at::Tensor &d_output_features, bool average) {
at::Tensor d_output_features, bool average) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
return cuda_ActivePooling_updateGradInput<float, Dimension>( return cuda_ActivePooling_updateGradInput<float, Dimension>(
inputSize, m, input_features, d_input_features, d_output_features, inputSize, m, input_features, d_input_features, d_output_features,
...@@ -213,11 +213,11 @@ void ActivePooling_updateGradInput(at::Tensor inputSize, Metadata<Dimension> &m, ...@@ -213,11 +213,11 @@ void ActivePooling_updateGradInput(at::Tensor inputSize, Metadata<Dimension> &m,
average); average);
} }
template <Int Dimension> template <Int Dimension>
void AveragePooling_updateOutput(at::Tensor inputSize, at::Tensor outputSize, void AveragePooling_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor poolSize, at::Tensor poolStride, at::Tensor &poolSize, at::Tensor &poolStride,
Metadata<Dimension> &m, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, at::Tensor &output_features,
long nFeaturesToDrop) { long nFeaturesToDrop) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
cuda_AveragePooling_updateOutput<float, Dimension>( cuda_AveragePooling_updateOutput<float, Dimension>(
...@@ -229,12 +229,10 @@ void AveragePooling_updateOutput(at::Tensor inputSize, at::Tensor outputSize, ...@@ -229,12 +229,10 @@ void AveragePooling_updateOutput(at::Tensor inputSize, at::Tensor outputSize,
output_features, nFeaturesToDrop); output_features, nFeaturesToDrop);
} }
template <Int Dimension> template <Int Dimension>
void AveragePooling_updateGradInput(at::Tensor inputSize, at::Tensor outputSize, void AveragePooling_updateGradInput(
at::Tensor poolSize, at::Tensor poolStride, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize,
Metadata<Dimension> &m, at::Tensor &poolStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor input_features, at::Tensor &d_input_features, at::Tensor &d_output_features,
at::Tensor d_input_features,
at::Tensor d_output_features,
long nFeaturesToDrop) { long nFeaturesToDrop) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_AveragePooling_updateGradInput<float, Dimension>( cuda_AveragePooling_updateGradInput<float, Dimension>(
...@@ -246,12 +244,12 @@ void AveragePooling_updateGradInput(at::Tensor inputSize, at::Tensor outputSize, ...@@ -246,12 +244,12 @@ void AveragePooling_updateGradInput(at::Tensor inputSize, at::Tensor outputSize,
d_input_features, d_output_features, nFeaturesToDrop); d_input_features, d_output_features, nFeaturesToDrop);
} }
template <Int Dimension> template <Int Dimension>
double Convolution_updateOutput(at::Tensor inputSize, at::Tensor outputSize, double
at::Tensor filterSize, at::Tensor filterStride, Convolution_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
Metadata<Dimension> &m, at::Tensor &filterSize, at::Tensor &filterStride,
at::Tensor input_features, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, at::Tensor weight, at::Tensor &output_features, at::Tensor &weight,
at::Tensor bias) { at::Tensor &bias) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
return cuda_Convolution_updateOutput<float, Dimension>( return cuda_Convolution_updateOutput<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features, inputSize, outputSize, filterSize, filterStride, m, input_features,
...@@ -262,12 +260,12 @@ double Convolution_updateOutput(at::Tensor inputSize, at::Tensor outputSize, ...@@ -262,12 +260,12 @@ double Convolution_updateOutput(at::Tensor inputSize, at::Tensor outputSize,
output_features, weight, bias); output_features, weight, bias);
} }
template <Int Dimension> template <Int Dimension>
void Convolution_backward(at::Tensor inputSize, at::Tensor outputSize, void Convolution_backward(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor filterSize, at::Tensor filterStride, at::Tensor &filterSize, at::Tensor &filterStride,
Metadata<Dimension> &m, at::Tensor input_features, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor weight, at::Tensor &d_output_features, at::Tensor &weight,
at::Tensor d_weight, at::Tensor d_bias) { at::Tensor &d_weight, at::Tensor &d_bias) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_Convolution_backward<float, Dimension>( cuda_Convolution_backward<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features, inputSize, outputSize, filterSize, filterStride, m, input_features,
...@@ -278,12 +276,10 @@ void Convolution_backward(at::Tensor inputSize, at::Tensor outputSize, ...@@ -278,12 +276,10 @@ void Convolution_backward(at::Tensor inputSize, at::Tensor outputSize,
d_input_features, d_output_features, weight, d_weight, d_bias); d_input_features, d_output_features, weight, d_weight, d_bias);
} }
template <Int Dimension> template <Int Dimension>
double SubmanifoldConvolution_updateOutput(at::Tensor inputSize, double SubmanifoldConvolution_updateOutput(
at::Tensor filterSize, at::Tensor &inputSize, at::Tensor &filterSize, Metadata<Dimension> &m,
Metadata<Dimension> &m, at::Tensor &input_features, at::Tensor &output_features, at::Tensor &weight,
at::Tensor input_features, at::Tensor &bias) {
at::Tensor output_features,
at::Tensor weight, at::Tensor bias) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
return cuda_SubmanifoldConvolution_updateOutput<float, Dimension>( return cuda_SubmanifoldConvolution_updateOutput<float, Dimension>(
inputSize, filterSize, m, input_features, output_features, weight, inputSize, filterSize, m, input_features, output_features, weight,
...@@ -295,10 +291,10 @@ double SubmanifoldConvolution_updateOutput(at::Tensor inputSize, ...@@ -295,10 +291,10 @@ double SubmanifoldConvolution_updateOutput(at::Tensor inputSize,
} }
template <Int Dimension> template <Int Dimension>
void SubmanifoldConvolution_backward( void SubmanifoldConvolution_backward(
at::Tensor inputSize, at::Tensor filterSize, Metadata<Dimension> &m, at::Tensor &inputSize, at::Tensor &filterSize, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor d_input_features, at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor weight, at::Tensor d_weight, at::Tensor &d_output_features, at::Tensor &weight, at::Tensor &d_weight,
at::Tensor d_bias) { at::Tensor &d_bias) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_SubmanifoldConvolution_backward<float, Dimension>( cuda_SubmanifoldConvolution_backward<float, Dimension>(
inputSize, filterSize, m, input_features, d_input_features, inputSize, filterSize, m, input_features, d_input_features,
...@@ -310,8 +306,8 @@ void SubmanifoldConvolution_backward( ...@@ -310,8 +306,8 @@ void SubmanifoldConvolution_backward(
} }
template <Int Dimension> template <Int Dimension>
double PermutohedralSubmanifoldConvolution_updateOutput( double PermutohedralSubmanifoldConvolution_updateOutput(
at::Tensor inputSize, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &inputSize, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, at::Tensor weight, at::Tensor bias) { at::Tensor &output_features, at::Tensor &weight, at::Tensor &bias) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
return cuda_PermutohedralSubmanifoldConvolution_updateOutput<float, return cuda_PermutohedralSubmanifoldConvolution_updateOutput<float,
Dimension>( Dimension>(
...@@ -323,9 +319,9 @@ double PermutohedralSubmanifoldConvolution_updateOutput( ...@@ -323,9 +319,9 @@ double PermutohedralSubmanifoldConvolution_updateOutput(
} }
template <Int Dimension> template <Int Dimension>
void PermutohedralSubmanifoldConvolution_backward( void PermutohedralSubmanifoldConvolution_backward(
at::Tensor inputSize, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &inputSize, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor d_output_features, at::Tensor &d_input_features, at::Tensor &d_output_features,
at::Tensor weight, at::Tensor d_weight, at::Tensor d_bias) { at::Tensor &weight, at::Tensor &d_weight, at::Tensor &d_bias) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_PermutohedralSubmanifoldConvolution_backward<float, Dimension>( cuda_PermutohedralSubmanifoldConvolution_backward<float, Dimension>(
inputSize, m, input_features, d_input_features, d_output_features, inputSize, m, input_features, d_input_features, d_output_features,
...@@ -337,10 +333,10 @@ void PermutohedralSubmanifoldConvolution_backward( ...@@ -337,10 +333,10 @@ void PermutohedralSubmanifoldConvolution_backward(
} }
template <Int Dimension> template <Int Dimension>
double FullConvolution_updateOutput( double FullConvolution_updateOutput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize,
at::Tensor filterStride, Metadata<Dimension> &mIn, at::Tensor &filterStride, Metadata<Dimension> &mIn,
Metadata<Dimension> &mOut, at::Tensor input_features, Metadata<Dimension> &mOut, at::Tensor &input_features,
at::Tensor output_features, at::Tensor weight, at::Tensor bias) { at::Tensor &output_features, at::Tensor &weight, at::Tensor &bias) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
return cuda_FullConvolution_updateOutput<float, Dimension>( return cuda_FullConvolution_updateOutput<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, mIn, mOut, inputSize, outputSize, filterSize, filterStride, mIn, mOut,
...@@ -351,14 +347,14 @@ double FullConvolution_updateOutput( ...@@ -351,14 +347,14 @@ double FullConvolution_updateOutput(
input_features, output_features, weight, bias); input_features, output_features, weight, bias);
} }
template <Int Dimension> template <Int Dimension>
void FullConvolution_backward(at::Tensor inputSize, at::Tensor outputSize, void FullConvolution_backward(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor filterSize, at::Tensor filterStride, at::Tensor &filterSize, at::Tensor &filterStride,
Metadata<Dimension> &mIn, Metadata<Dimension> &mIn,
Metadata<Dimension> &mOut, Metadata<Dimension> &mOut,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor weight, at::Tensor &d_output_features, at::Tensor &weight,
at::Tensor d_weight, at::Tensor d_bias) { at::Tensor &d_weight, at::Tensor &d_bias) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_FullConvolution_backward<float, Dimension>( cuda_FullConvolution_backward<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, mIn, mOut, inputSize, outputSize, filterSize, filterStride, mIn, mOut,
...@@ -372,9 +368,10 @@ void FullConvolution_backward(at::Tensor inputSize, at::Tensor outputSize, ...@@ -372,9 +368,10 @@ void FullConvolution_backward(at::Tensor inputSize, at::Tensor outputSize,
} }
template <Int Dimension> template <Int Dimension>
double RandomizedStrideConvolution_updateOutput( double RandomizedStrideConvolution_updateOutput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize,
at::Tensor filterStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &filterStride, Metadata<Dimension> &m,
at::Tensor output_features, at::Tensor weight, at::Tensor bias) { at::Tensor &input_features, at::Tensor &output_features, at::Tensor &weight,
at::Tensor &bias) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
return cuda_RandomizedStrideConvolution_updateOutput<float, Dimension>( return cuda_RandomizedStrideConvolution_updateOutput<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features, inputSize, outputSize, filterSize, filterStride, m, input_features,
...@@ -386,10 +383,11 @@ double RandomizedStrideConvolution_updateOutput( ...@@ -386,10 +383,11 @@ double RandomizedStrideConvolution_updateOutput(
} }
template <Int Dimension> template <Int Dimension>
void RandomizedStrideConvolution_backward( void RandomizedStrideConvolution_backward(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &filterSize,
at::Tensor filterStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &filterStride, Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor d_output_features, at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor weight, at::Tensor d_weight, at::Tensor d_bias) { at::Tensor &d_output_features, at::Tensor &weight, at::Tensor &d_weight,
at::Tensor &d_bias) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_RandomizedStrideConvolution_backward<float, Dimension>( cuda_RandomizedStrideConvolution_backward<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features, inputSize, outputSize, filterSize, filterStride, m, input_features,
...@@ -400,10 +398,12 @@ void RandomizedStrideConvolution_backward( ...@@ -400,10 +398,12 @@ void RandomizedStrideConvolution_backward(
d_input_features, d_output_features, weight, d_weight, d_bias); d_input_features, d_output_features, weight, d_weight, d_bias);
} }
template <Int Dimension> template <Int Dimension>
double Deconvolution_updateOutput( double
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, Deconvolution_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor filterStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &filterSize, at::Tensor &filterStride,
at::Tensor output_features, at::Tensor weight, at::Tensor bias) { Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor &output_features, at::Tensor &weight,
at::Tensor &bias) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
return cuda_Deconvolution_updateOutput<float, Dimension>( return cuda_Deconvolution_updateOutput<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features, inputSize, outputSize, filterSize, filterStride, m, input_features,
...@@ -414,12 +414,12 @@ double Deconvolution_updateOutput( ...@@ -414,12 +414,12 @@ double Deconvolution_updateOutput(
output_features, weight, bias); output_features, weight, bias);
} }
template <Int Dimension> template <Int Dimension>
void Deconvolution_backward(at::Tensor inputSize, at::Tensor outputSize, void Deconvolution_backward(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor filterSize, at::Tensor filterStride, at::Tensor &filterSize, at::Tensor &filterStride,
Metadata<Dimension> &m, at::Tensor input_features, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor weight, at::Tensor &d_output_features, at::Tensor &weight,
at::Tensor d_weight, at::Tensor d_bias) { at::Tensor &d_weight, at::Tensor &d_bias) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_Deconvolution_backward<float, Dimension>( cuda_Deconvolution_backward<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features, inputSize, outputSize, filterSize, filterStride, m, input_features,
...@@ -430,9 +430,10 @@ void Deconvolution_backward(at::Tensor inputSize, at::Tensor outputSize, ...@@ -430,9 +430,10 @@ void Deconvolution_backward(at::Tensor inputSize, at::Tensor outputSize,
d_input_features, d_output_features, weight, d_weight, d_bias); d_input_features, d_output_features, weight, d_weight, d_bias);
} }
template <Int Dimension> template <Int Dimension>
void InputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor spatialSize, void InputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor &spatialSize,
at::Tensor input_coords, at::Tensor input_features, at::Tensor &input_coords,
at::Tensor output_features, long batchSize, at::Tensor &input_features,
at::Tensor &output_features, long batchSize,
long mode) { long mode) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
cuda_InputLayer_updateOutput<float, Dimension>( cuda_InputLayer_updateOutput<float, Dimension>(
...@@ -445,8 +446,8 @@ void InputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor spatialSize, ...@@ -445,8 +446,8 @@ void InputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor spatialSize,
} }
template <Int Dimension> template <Int Dimension>
void InputLayer_updateGradInput(Metadata<Dimension> &m, void InputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features) { at::Tensor &d_output_features) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_InputLayer_updateGradInput<float, Dimension>(m, d_input_features, cuda_InputLayer_updateGradInput<float, Dimension>(m, d_input_features,
d_output_features); d_output_features);
...@@ -455,8 +456,9 @@ void InputLayer_updateGradInput(Metadata<Dimension> &m, ...@@ -455,8 +456,9 @@ void InputLayer_updateGradInput(Metadata<Dimension> &m,
d_output_features); d_output_features);
} }
template <Int Dimension> template <Int Dimension>
void OutputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor input_features, void OutputLayer_updateOutput(Metadata<Dimension> &m,
at::Tensor output_features) { at::Tensor &input_features,
at::Tensor &output_features) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
cuda_OutputLayer_updateOutput<float, Dimension>(m, input_features, cuda_OutputLayer_updateOutput<float, Dimension>(m, input_features,
output_features); output_features);
...@@ -466,8 +468,8 @@ void OutputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor input_features, ...@@ -466,8 +468,8 @@ void OutputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor input_features,
} }
template <Int Dimension> template <Int Dimension>
void OutputLayer_updateGradInput(Metadata<Dimension> &m, void OutputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features) { at::Tensor &d_output_features) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_OutputLayer_updateGradInput<float, Dimension>(m, d_input_features, cuda_OutputLayer_updateGradInput<float, Dimension>(m, d_input_features,
d_output_features); d_output_features);
...@@ -476,10 +478,10 @@ void OutputLayer_updateGradInput(Metadata<Dimension> &m, ...@@ -476,10 +478,10 @@ void OutputLayer_updateGradInput(Metadata<Dimension> &m,
d_output_features); d_output_features);
} }
template <Int Dimension> template <Int Dimension>
void BLInputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor spatialSize, void BLInputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor &spatialSize,
at::Tensor input_coords, at::Tensor &input_coords,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, long mode) { at::Tensor &output_features, long mode) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
cuda_BLInputLayer_updateOutput<float, Dimension>( cuda_BLInputLayer_updateOutput<float, Dimension>(
m, spatialSize, input_coords, input_features, output_features, mode); m, spatialSize, input_coords, input_features, output_features, mode);
...@@ -489,8 +491,8 @@ void BLInputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor spatialSize, ...@@ -489,8 +491,8 @@ void BLInputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor spatialSize,
} }
template <Int Dimension> template <Int Dimension>
void BLInputLayer_updateGradInput(Metadata<Dimension> &m, void BLInputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features) { at::Tensor &d_output_features) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_BLInputLayer_updateGradInput<float, Dimension>(m, d_input_features, cuda_BLInputLayer_updateGradInput<float, Dimension>(m, d_input_features,
d_output_features); d_output_features);
...@@ -500,8 +502,8 @@ void BLInputLayer_updateGradInput(Metadata<Dimension> &m, ...@@ -500,8 +502,8 @@ void BLInputLayer_updateGradInput(Metadata<Dimension> &m,
} }
template <Int Dimension> template <Int Dimension>
void BLOutputLayer_updateOutput(Metadata<Dimension> &m, void BLOutputLayer_updateOutput(Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features) { at::Tensor &output_features) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
cuda_BLOutputLayer_updateOutput<float, Dimension>(m, input_features, cuda_BLOutputLayer_updateOutput<float, Dimension>(m, input_features,
output_features); output_features);
...@@ -511,8 +513,8 @@ void BLOutputLayer_updateOutput(Metadata<Dimension> &m, ...@@ -511,8 +513,8 @@ void BLOutputLayer_updateOutput(Metadata<Dimension> &m,
} }
template <Int Dimension> template <Int Dimension>
void BLOutputLayer_updateGradInput(Metadata<Dimension> &m, void BLOutputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features) { at::Tensor &d_output_features) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_BLOutputLayer_updateGradInput<float, Dimension>(m, d_input_features, cuda_BLOutputLayer_updateGradInput<float, Dimension>(m, d_input_features,
d_output_features); d_output_features);
...@@ -521,10 +523,11 @@ void BLOutputLayer_updateGradInput(Metadata<Dimension> &m, ...@@ -521,10 +523,11 @@ void BLOutputLayer_updateGradInput(Metadata<Dimension> &m,
d_output_features); d_output_features);
} }
template <Int Dimension> template <Int Dimension>
void MaxPooling_updateOutput(at::Tensor inputSize, at::Tensor outputSize, void MaxPooling_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor poolSize, at::Tensor poolStride, at::Tensor &poolSize, at::Tensor &poolStride,
Metadata<Dimension> &m, at::Tensor input_features, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, long nFeaturesToDrop) { at::Tensor &output_features,
long nFeaturesToDrop) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
cuda_MaxPooling_updateOutput<float, Dimension>( cuda_MaxPooling_updateOutput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features, inputSize, outputSize, poolSize, poolStride, m, input_features,
...@@ -536,10 +539,10 @@ void MaxPooling_updateOutput(at::Tensor inputSize, at::Tensor outputSize, ...@@ -536,10 +539,10 @@ void MaxPooling_updateOutput(at::Tensor inputSize, at::Tensor outputSize,
} }
template <Int Dimension> template <Int Dimension>
void MaxPooling_updateGradInput( void MaxPooling_updateGradInput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize,
at::Tensor poolStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &poolStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor output_features, at::Tensor &d_input_features, at::Tensor &output_features,
at::Tensor d_output_features, long nFeaturesToDrop) { at::Tensor &d_output_features, long nFeaturesToDrop) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_MaxPooling_updateGradInput<float, Dimension>( cuda_MaxPooling_updateGradInput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features, inputSize, outputSize, poolSize, poolStride, m, input_features,
...@@ -551,9 +554,9 @@ void MaxPooling_updateGradInput( ...@@ -551,9 +554,9 @@ void MaxPooling_updateGradInput(
} }
template <Int Dimension> template <Int Dimension>
void RandomizedStrideMaxPooling_updateOutput( void RandomizedStrideMaxPooling_updateOutput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize,
at::Tensor poolStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &poolStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, long nFeaturesToDrop) { at::Tensor &output_features, long nFeaturesToDrop) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
cuda_RandomizedStrideMaxPooling_updateOutput<float, Dimension>( cuda_RandomizedStrideMaxPooling_updateOutput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features, inputSize, outputSize, poolSize, poolStride, m, input_features,
...@@ -565,10 +568,10 @@ void RandomizedStrideMaxPooling_updateOutput( ...@@ -565,10 +568,10 @@ void RandomizedStrideMaxPooling_updateOutput(
} }
template <Int Dimension> template <Int Dimension>
void RandomizedStrideMaxPooling_updateGradInput( void RandomizedStrideMaxPooling_updateGradInput(
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize,
at::Tensor poolStride, Metadata<Dimension> &m, at::Tensor input_features, at::Tensor &poolStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor d_input_features, at::Tensor output_features, at::Tensor &d_input_features, at::Tensor &output_features,
at::Tensor d_output_features, long nFeaturesToDrop) { at::Tensor &d_output_features, long nFeaturesToDrop) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_RandomizedStrideMaxPooling_updateGradInput<float, Dimension>( cuda_RandomizedStrideMaxPooling_updateGradInput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features, inputSize, outputSize, poolSize, poolStride, m, input_features,
...@@ -579,9 +582,9 @@ void RandomizedStrideMaxPooling_updateGradInput( ...@@ -579,9 +582,9 @@ void RandomizedStrideMaxPooling_updateGradInput(
d_input_features, output_features, d_output_features, nFeaturesToDrop); d_input_features, output_features, d_output_features, nFeaturesToDrop);
} }
template <Int Dimension> template <Int Dimension>
void SparseToDense_updateOutput(at::Tensor inputSize, Metadata<Dimension> &m, void SparseToDense_updateOutput(at::Tensor &inputSize, Metadata<Dimension> &m,
at::Tensor input_features, at::Tensor &input_features,
at::Tensor output_features, long nPlanes) { at::Tensor &output_features, long nPlanes) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
cuda_SparseToDense_updateOutput<float, Dimension>( cuda_SparseToDense_updateOutput<float, Dimension>(
inputSize, m, input_features, output_features, nPlanes); inputSize, m, input_features, output_features, nPlanes);
...@@ -590,10 +593,11 @@ void SparseToDense_updateOutput(at::Tensor inputSize, Metadata<Dimension> &m, ...@@ -590,10 +593,11 @@ void SparseToDense_updateOutput(at::Tensor inputSize, Metadata<Dimension> &m,
inputSize, m, input_features, output_features, nPlanes); inputSize, m, input_features, output_features, nPlanes);
} }
template <Int Dimension> template <Int Dimension>
void SparseToDense_updateGradInput(at::Tensor inputSize, Metadata<Dimension> &m, void SparseToDense_updateGradInput(at::Tensor &inputSize,
at::Tensor input_features, Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &input_features,
at::Tensor d_output_features) { at::Tensor &d_input_features,
at::Tensor &d_output_features) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_SparseToDense_updateGradInput<float, Dimension>( cuda_SparseToDense_updateGradInput<float, Dimension>(
inputSize, m, input_features, d_input_features, d_output_features); inputSize, m, input_features, d_input_features, d_output_features);
...@@ -602,10 +606,10 @@ void SparseToDense_updateGradInput(at::Tensor inputSize, Metadata<Dimension> &m, ...@@ -602,10 +606,10 @@ void SparseToDense_updateGradInput(at::Tensor inputSize, Metadata<Dimension> &m,
inputSize, m, input_features, d_input_features, d_output_features); inputSize, m, input_features, d_input_features, d_output_features);
} }
template <Int Dimension> template <Int Dimension>
void UnPooling_updateOutput(at::Tensor inputSize, at::Tensor outputSize, void UnPooling_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor poolSize, at::Tensor poolStride, at::Tensor &poolSize, at::Tensor &poolStride,
Metadata<Dimension> &m, at::Tensor input_features, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor output_features, long nFeaturesToDrop) { at::Tensor &output_features, long nFeaturesToDrop) {
if (input_features.type().is_cuda()) if (input_features.type().is_cuda())
cuda_UnPooling_updateOutput<float, Dimension>( cuda_UnPooling_updateOutput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features, inputSize, outputSize, poolSize, poolStride, m, input_features,
...@@ -616,11 +620,11 @@ void UnPooling_updateOutput(at::Tensor inputSize, at::Tensor outputSize, ...@@ -616,11 +620,11 @@ void UnPooling_updateOutput(at::Tensor inputSize, at::Tensor outputSize,
output_features, nFeaturesToDrop); output_features, nFeaturesToDrop);
} }
template <Int Dimension> template <Int Dimension>
void UnPooling_updateGradInput(at::Tensor inputSize, at::Tensor outputSize, void UnPooling_updateGradInput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor poolSize, at::Tensor poolStride, at::Tensor &poolSize, at::Tensor &poolStride,
Metadata<Dimension> &m, Metadata<Dimension> &m,
at::Tensor d_input_features, at::Tensor &d_input_features,
at::Tensor d_output_features, at::Tensor &d_output_features,
long nFeaturesToDrop) { long nFeaturesToDrop) {
if (d_output_features.type().is_cuda()) if (d_output_features.type().is_cuda())
cuda_UnPooling_updateGradInput<float, Dimension>( cuda_UnPooling_updateGradInput<float, Dimension>(
...@@ -634,148 +638,152 @@ void UnPooling_updateGradInput(at::Tensor inputSize, at::Tensor outputSize, ...@@ -634,148 +638,152 @@ void UnPooling_updateGradInput(at::Tensor inputSize, at::Tensor outputSize,
#define FOO \ #define FOO \
template void ActivePooling_updateOutput<DIMENSION>( \ template void ActivePooling_updateOutput<DIMENSION>( \
at::Tensor inputSize, Metadata<DIMENSION> & m, \ at::Tensor & inputSize, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, bool average); \ at::Tensor & input_features, at::Tensor & output_features, \
bool average); \
template void ActivePooling_updateGradInput<DIMENSION>( \ template void ActivePooling_updateGradInput<DIMENSION>( \
at::Tensor inputSize, Metadata<DIMENSION> & m, \ at::Tensor & inputSize, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor & input_features, at::Tensor & d_input_features, \
at::Tensor d_output_features, bool average); \ at::Tensor & d_output_features, bool average); \
template void AveragePooling_updateOutput<DIMENSION>( \ template void AveragePooling_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor & inputSize, at::Tensor & outputSize, at::Tensor & poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor & poolStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, \ at::Tensor & input_features, at::Tensor & output_features, \
long nFeaturesToDrop); \ long nFeaturesToDrop); \
template void AveragePooling_updateGradInput<DIMENSION>( \ template void AveragePooling_updateGradInput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor & inputSize, at::Tensor & outputSize, at::Tensor & poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor & poolStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor & input_features, at::Tensor & d_input_features, \
at::Tensor d_output_features, long nFeaturesToDrop); \ at::Tensor & d_output_features, long nFeaturesToDrop); \
template double Convolution_updateOutput<DIMENSION>( \ template double Convolution_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor & inputSize, at::Tensor & outputSize, \
at::Tensor filterStride, Metadata<DIMENSION> & m, \ at::Tensor & filterSize, at::Tensor & filterStride, \
at::Tensor input_features, at::Tensor output_features, \ Metadata<DIMENSION> & m, at::Tensor & input_features, \
at::Tensor weight, at::Tensor bias); \ at::Tensor & output_features, at::Tensor & weight, at::Tensor & bias); \
template void Convolution_backward<DIMENSION>( \ template void Convolution_backward<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor & inputSize, at::Tensor & outputSize, \
at::Tensor filterStride, Metadata<DIMENSION> & m, \ at::Tensor & filterSize, at::Tensor & filterStride, \
at::Tensor input_features, at::Tensor d_input_features, \ Metadata<DIMENSION> & m, at::Tensor & input_features, \
at::Tensor d_output_features, at::Tensor weight, at::Tensor d_weight, \ at::Tensor & d_input_features, at::Tensor & d_output_features, \
at::Tensor d_bias); \ at::Tensor & weight, at::Tensor & d_weight, at::Tensor & d_bias); \
template double SubmanifoldConvolution_updateOutput<DIMENSION>( \ template double SubmanifoldConvolution_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor filterSize, Metadata<DIMENSION> & m, \ at::Tensor & inputSize, at::Tensor & filterSize, \
at::Tensor input_features, at::Tensor output_features, \ Metadata<DIMENSION> & m, at::Tensor & input_features, \
at::Tensor weight, at::Tensor bias); \ at::Tensor & output_features, at::Tensor & weight, at::Tensor & bias); \
template void SubmanifoldConvolution_backward<DIMENSION>( \ template void SubmanifoldConvolution_backward<DIMENSION>( \
at::Tensor inputSize, at::Tensor filterSize, Metadata<DIMENSION> & m, \ at::Tensor & inputSize, at::Tensor & filterSize, \
at::Tensor input_features, at::Tensor d_input_features, \ Metadata<DIMENSION> & m, at::Tensor & input_features, \
at::Tensor d_output_features, at::Tensor weight, at::Tensor d_weight, \ at::Tensor & d_input_features, at::Tensor & d_output_features, \
at::Tensor d_bias); \ at::Tensor & weight, at::Tensor & d_weight, at::Tensor & d_bias); \
template double PermutohedralSubmanifoldConvolution_updateOutput<DIMENSION>( \ template double PermutohedralSubmanifoldConvolution_updateOutput<DIMENSION>( \
at::Tensor inputSize, Metadata<DIMENSION> & m, \ at::Tensor & inputSize, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, \ at::Tensor & input_features, at::Tensor & output_features, \
at::Tensor weight, at::Tensor bias); \ at::Tensor & weight, at::Tensor & bias); \
template void PermutohedralSubmanifoldConvolution_backward<DIMENSION>( \ template void PermutohedralSubmanifoldConvolution_backward<DIMENSION>( \
at::Tensor inputSize, Metadata<DIMENSION> & m, \ at::Tensor & inputSize, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor & input_features, at::Tensor & d_input_features, \
at::Tensor d_output_features, at::Tensor weight, at::Tensor d_weight, \ at::Tensor & d_output_features, at::Tensor & weight, \
at::Tensor d_bias); \ at::Tensor & d_weight, at::Tensor & d_bias); \
template double FullConvolution_updateOutput<DIMENSION>( \ template double FullConvolution_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor & inputSize, at::Tensor & outputSize, \
at::Tensor filterStride, Metadata<DIMENSION> & mIn, \ at::Tensor & filterSize, at::Tensor & filterStride, \
Metadata<DIMENSION> & mOut, at::Tensor input_features, \ Metadata<DIMENSION> & mIn, Metadata<DIMENSION> & mOut, \
at::Tensor output_features, at::Tensor weight, at::Tensor bias); \ at::Tensor & input_features, at::Tensor & output_features, \
at::Tensor & weight, at::Tensor & bias); \
template void FullConvolution_backward<DIMENSION>( \ template void FullConvolution_backward<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor & inputSize, at::Tensor & outputSize, \
at::Tensor filterStride, Metadata<DIMENSION> & mIn, \ at::Tensor & filterSize, at::Tensor & filterStride, \
Metadata<DIMENSION> & mOut, at::Tensor input_features, \ Metadata<DIMENSION> & mIn, Metadata<DIMENSION> & mOut, \
at::Tensor d_input_features, at::Tensor d_output_features, \ at::Tensor & input_features, at::Tensor & d_input_features, \
at::Tensor weight, at::Tensor d_weight, at::Tensor d_bias); \ at::Tensor & d_output_features, at::Tensor & weight, \
at::Tensor & d_weight, at::Tensor & d_bias); \
template double RandomizedStrideConvolution_updateOutput<DIMENSION>( \ template double RandomizedStrideConvolution_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor & inputSize, at::Tensor & outputSize, \
at::Tensor filterStride, Metadata<DIMENSION> & m, \ at::Tensor & filterSize, at::Tensor & filterStride, \
at::Tensor input_features, at::Tensor output_features, \ Metadata<DIMENSION> & m, at::Tensor & input_features, \
at::Tensor weight, at::Tensor bias); \ at::Tensor & output_features, at::Tensor & weight, at::Tensor & bias); \
template void RandomizedStrideConvolution_backward<DIMENSION>( \ template void RandomizedStrideConvolution_backward<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor & inputSize, at::Tensor & outputSize, \
at::Tensor filterStride, Metadata<DIMENSION> & m, \ at::Tensor & filterSize, at::Tensor & filterStride, \
at::Tensor input_features, at::Tensor d_input_features, \ Metadata<DIMENSION> & m, at::Tensor & input_features, \
at::Tensor d_output_features, at::Tensor weight, at::Tensor d_weight, \ at::Tensor & d_input_features, at::Tensor & d_output_features, \
at::Tensor d_bias); \ at::Tensor & weight, at::Tensor & d_weight, at::Tensor & d_bias); \
template double Deconvolution_updateOutput<DIMENSION>( \ template double Deconvolution_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor & inputSize, at::Tensor & outputSize, \
at::Tensor filterStride, Metadata<DIMENSION> & m, \ at::Tensor & filterSize, at::Tensor & filterStride, \
at::Tensor input_features, at::Tensor output_features, \ Metadata<DIMENSION> & m, at::Tensor & input_features, \
at::Tensor weight, at::Tensor bias); \ at::Tensor & output_features, at::Tensor & weight, at::Tensor & bias); \
template void Deconvolution_backward<DIMENSION>( \ template void Deconvolution_backward<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor filterSize, \ at::Tensor & inputSize, at::Tensor & outputSize, \
at::Tensor filterStride, Metadata<DIMENSION> & m, \ at::Tensor & filterSize, at::Tensor & filterStride, \
at::Tensor input_features, at::Tensor d_input_features, \ Metadata<DIMENSION> & m, at::Tensor & input_features, \
at::Tensor d_output_features, at::Tensor weight, at::Tensor d_weight, \ at::Tensor & d_input_features, at::Tensor & d_output_features, \
at::Tensor d_bias); \ at::Tensor & weight, at::Tensor & d_weight, at::Tensor & d_bias); \
template void InputLayer_updateOutput<DIMENSION>( \ template void InputLayer_updateOutput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor spatialSize, \ Metadata<DIMENSION> & m, at::Tensor & spatialSize, \
at::Tensor input_coords, at::Tensor input_features, \ at::Tensor & input_coords, at::Tensor & input_features, \
at::Tensor output_features, long batchSize, long mode); \ at::Tensor & output_features, long batchSize, long mode); \
template void InputLayer_updateGradInput<DIMENSION>( \ template void InputLayer_updateGradInput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor d_input_features, \ Metadata<DIMENSION> & m, at::Tensor & d_input_features, \
at::Tensor d_output_features); \ at::Tensor & d_output_features); \
template void OutputLayer_updateOutput<DIMENSION>( \ template void OutputLayer_updateOutput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor input_features, \ Metadata<DIMENSION> & m, at::Tensor & input_features, \
at::Tensor output_features); \ at::Tensor & output_features); \
template void OutputLayer_updateGradInput<DIMENSION>( \ template void OutputLayer_updateGradInput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor d_input_features, \ Metadata<DIMENSION> & m, at::Tensor & d_input_features, \
at::Tensor d_output_features); \ at::Tensor & d_output_features); \
template void BLInputLayer_updateOutput<DIMENSION>( \ template void BLInputLayer_updateOutput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor spatialSize, \ Metadata<DIMENSION> & m, at::Tensor & spatialSize, \
at::Tensor input_coords, at::Tensor input_features, \ at::Tensor & input_coords, at::Tensor & input_features, \
at::Tensor output_features, long mode); \ at::Tensor & output_features, long mode); \
template void BLInputLayer_updateGradInput<DIMENSION>( \ template void BLInputLayer_updateGradInput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor d_input_features, \ Metadata<DIMENSION> & m, at::Tensor & d_input_features, \
at::Tensor d_output_features); \ at::Tensor & d_output_features); \
template void BLOutputLayer_updateOutput<DIMENSION>( \ template void BLOutputLayer_updateOutput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor input_features, \ Metadata<DIMENSION> & m, at::Tensor & input_features, \
at::Tensor output_features); \ at::Tensor & output_features); \
template void BLOutputLayer_updateGradInput<DIMENSION>( \ template void BLOutputLayer_updateGradInput<DIMENSION>( \
Metadata<DIMENSION> & m, at::Tensor d_input_features, \ Metadata<DIMENSION> & m, at::Tensor & d_input_features, \
at::Tensor d_output_features); \ at::Tensor & d_output_features); \
template void MaxPooling_updateOutput<DIMENSION>( \ template void MaxPooling_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor & inputSize, at::Tensor & outputSize, at::Tensor & poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor & poolStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, \ at::Tensor & input_features, at::Tensor & output_features, \
long nFeaturesToDrop); \ long nFeaturesToDrop); \
template void MaxPooling_updateGradInput<DIMENSION>( \ template void MaxPooling_updateGradInput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor & inputSize, at::Tensor & outputSize, at::Tensor & poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor & poolStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor & input_features, at::Tensor & d_input_features, \
at::Tensor output_features, at::Tensor d_output_features, \ at::Tensor & output_features, at::Tensor & d_output_features, \
long nFeaturesToDrop); \ long nFeaturesToDrop); \
template void RandomizedStrideMaxPooling_updateOutput<DIMENSION>( \ template void RandomizedStrideMaxPooling_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor & inputSize, at::Tensor & outputSize, at::Tensor & poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor & poolStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, \ at::Tensor & input_features, at::Tensor & output_features, \
long nFeaturesToDrop); \ long nFeaturesToDrop); \
template void RandomizedStrideMaxPooling_updateGradInput<DIMENSION>( \ template void RandomizedStrideMaxPooling_updateGradInput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor & inputSize, at::Tensor & outputSize, at::Tensor & poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor & poolStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor & input_features, at::Tensor & d_input_features, \
at::Tensor output_features, at::Tensor d_output_features, \ at::Tensor & output_features, at::Tensor & d_output_features, \
long nFeaturesToDrop); \ long nFeaturesToDrop); \
template void SparseToDense_updateOutput<DIMENSION>( \ template void SparseToDense_updateOutput<DIMENSION>( \
at::Tensor inputSize, Metadata<DIMENSION> & m, \ at::Tensor & inputSize, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, long nPlanes); \ at::Tensor & input_features, at::Tensor & output_features, \
long nPlanes); \
template void SparseToDense_updateGradInput<DIMENSION>( \ template void SparseToDense_updateGradInput<DIMENSION>( \
at::Tensor inputSize, Metadata<DIMENSION> & m, \ at::Tensor & inputSize, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor d_input_features, \ at::Tensor & input_features, at::Tensor & d_input_features, \
at::Tensor d_output_features); \ at::Tensor & d_output_features); \
template void UnPooling_updateOutput<DIMENSION>( \ template void UnPooling_updateOutput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor & inputSize, at::Tensor & outputSize, at::Tensor & poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor & poolStride, Metadata<DIMENSION> & m, \
at::Tensor input_features, at::Tensor output_features, \ at::Tensor & input_features, at::Tensor & output_features, \
long nFeaturesToDrop); \ long nFeaturesToDrop); \
template void UnPooling_updateGradInput<DIMENSION>( \ template void UnPooling_updateGradInput<DIMENSION>( \
at::Tensor inputSize, at::Tensor outputSize, at::Tensor poolSize, \ at::Tensor & inputSize, at::Tensor & outputSize, at::Tensor & poolSize, \
at::Tensor poolStride, Metadata<DIMENSION> & m, \ at::Tensor & poolStride, Metadata<DIMENSION> & m, \
at::Tensor d_input_features, at::Tensor d_output_features, \ at::Tensor & d_input_features, at::Tensor & d_output_features, \
long nFeaturesToDrop); long nFeaturesToDrop);
#define DIMENSION 1 #define DIMENSION 1
...@@ -797,15 +805,15 @@ FOO; ...@@ -797,15 +805,15 @@ FOO;
FOO; FOO;
#undef DIMENSION #undef DIMENSION
void CopyFeaturesHelper_updateOutput(at::Tensor rules, at::Tensor context, void CopyFeaturesHelper_updateOutput(at::Tensor &rules, at::Tensor &context,
at::Tensor Context) { at::Tensor &Context) {
if (context.is_cuda()) if (context.is_cuda())
cuda_CopyFeaturesHelper_updateOutput<float>(rules, context, Context); cuda_CopyFeaturesHelper_updateOutput<float>(rules, context, Context);
else else
cpu_CopyFeaturesHelper_updateOutput<float>(rules, context, Context); cpu_CopyFeaturesHelper_updateOutput<float>(rules, context, Context);
} }
void CopyFeaturesHelper_updateGradInput(at::Tensor rules, at::Tensor dcontext, void CopyFeaturesHelper_updateGradInput(at::Tensor &rules, at::Tensor &dcontext,
at::Tensor dContext) { at::Tensor &dContext) {
if (dContext.is_cuda()) if (dContext.is_cuda())
cuda_CopyFeaturesHelper_updateGradInput<float>(rules, dcontext, dContext); cuda_CopyFeaturesHelper_updateGradInput<float>(rules, dcontext, dContext);
else else
......
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