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Commit aebbb122 authored by Benjamin Thomas Graham's avatar Benjamin Thomas Graham
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Merge branch 'master' of github.com:facebookresearch/SparseConvNet

parents 8c706edd e55e3bea
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Showing with 35 additions and 31 deletions
develop.sh 0 → 100644
View file @ aebbb122
#!/bin/bash
rm -rf build/ dist/ sparseconvnet.egg-info sparseconvnet_SCN*.so
python setup.py develop
python examples/hello-world.py
sparseconvnet/SCN/CUDA/ActivePooling.cu
View file @ aebbb122
...@@ -23,7 +23,7 @@ void ActivePooling_ForwardPass(T *input_features, T *output_features, ...@@ -23,7 +23,7 @@ void ActivePooling_ForwardPass(T *input_features, T *output_features,
Int batchSize, Int maxActive, Int nPlanes, Int batchSize, Int maxActive, Int nPlanes,
Int *rules, bool average) { Int *rules, bool average) {
auto rulesBuffer = at::CUDA(at_kINT).tensor({1 << 22}); auto rulesBuffer = at::empty({1<<22}, at::CUDA(at_kINT));
Int *rb = rulesBuffer.data<Int>(); Int *rb = rulesBuffer.data<Int>();
Int rowBatchSize = std::min((Int)32768, (1 << 22) / (maxActive + 1)); Int rowBatchSize = std::min((Int)32768, (1 << 22) / (maxActive + 1));
assert(rowBatchSize > 0); assert(rowBatchSize > 0);
...@@ -58,7 +58,7 @@ template <typename T> ...@@ -58,7 +58,7 @@ template <typename T>
void ActivePooling_BackwardPass(T *d_input_features, T *d_output_features, void ActivePooling_BackwardPass(T *d_input_features, T *d_output_features,
Int batchSize, Int maxActive, Int nPlanes, Int batchSize, Int maxActive, Int nPlanes,
Int *rules, bool average) { Int *rules, bool average) {
auto rulesBuffer = at::CUDA(at_kINT).tensor({1 << 22}); auto rulesBuffer = at::empty({1<<22}, at::CUDA(at_kINT));
Int *rb = rulesBuffer.data<Int>(); Int *rb = rulesBuffer.data<Int>();
Int rowBatchSize = std::min((Int)32768, (1 << 22) / (maxActive + 1)); Int rowBatchSize = std::min((Int)32768, (1 << 22) / (maxActive + 1));
assert(rowBatchSize > 0); assert(rowBatchSize > 0);
......
sparseconvnet/SCN/CUDA/IOLayers.cpp
View file @ aebbb122
...@@ -33,7 +33,7 @@ void cuda_InputLayer_updateOutput(Metadata<Dimension> &m, ...@@ -33,7 +33,7 @@ void cuda_InputLayer_updateOutput(Metadata<Dimension> &m,
} else { } else {
output_features.resize_({*m.inputNActive, nPlanes}); output_features.resize_({*m.inputNActive, nPlanes});
output_features.zero_(); output_features.zero_();
auto rulesBuffer = at::CUDA(at_kINT).tensor({(int)rules[1].size()}); auto rulesBuffer = at::empty({(int)rules[1].size()}, at::CUDA(at_kINT));
auto iF = input_features.data<T>(); auto iF = input_features.data<T>();
auto oF = output_features.data<T>(); auto oF = output_features.data<T>();
Int *rb = rulesBuffer.data<Int>(); Int *rb = rulesBuffer.data<Int>();
...@@ -58,7 +58,7 @@ void cuda_InputLayer_updateGradInput( ...@@ -58,7 +58,7 @@ void cuda_InputLayer_updateGradInput(
} else { } else {
d_input_features.resize_({rules[0][2], nPlanes}); d_input_features.resize_({rules[0][2], nPlanes});
d_input_features.zero_(); d_input_features.zero_();
auto rulesBuffer = at::CUDA(at_kINT).tensor({(int)rules[1].size()}); auto rulesBuffer = at::empty({(int)rules[1].size()}, at::CUDA(at_kINT));
auto diF = d_input_features.data<T>(); auto diF = d_input_features.data<T>();
auto doF = d_output_features.data<T>(); auto doF = d_output_features.data<T>();
Int *rb = rulesBuffer.data<Int>(); Int *rb = rulesBuffer.data<Int>();
...@@ -83,7 +83,7 @@ void cuda_OutputLayer_updateOutput(Metadata<Dimension> &m, ...@@ -83,7 +83,7 @@ void cuda_OutputLayer_updateOutput(Metadata<Dimension> &m,
} else { } else {
output_features.resize_({rules[0][2], nPlanes}); output_features.resize_({rules[0][2], nPlanes});
output_features.zero_(); output_features.zero_();
auto rulesBuffer = at::CUDA(at_kINT).tensor({(int)rules[1].size()}); auto rulesBuffer = at::empty({(int)rules[1].size()}, at::CUDA(at_kINT));
auto iF = input_features.data<T>(); auto iF = input_features.data<T>();
auto oF = output_features.data<T>(); auto oF = output_features.data<T>();
Int *rb = rulesBuffer.data<Int>(); Int *rb = rulesBuffer.data<Int>();
...@@ -107,7 +107,7 @@ void cuda_OutputLayer_updateGradInput( ...@@ -107,7 +107,7 @@ void cuda_OutputLayer_updateGradInput(
} else { } else {
d_input_features.resize_({nRows, nPlanes}); d_input_features.resize_({nRows, nPlanes});
d_input_features.zero_(); d_input_features.zero_();
auto rulesBuffer = at::CUDA(at_kINT).tensor({(int)rules[1].size()}); auto rulesBuffer = at::empty({(int)rules[1].size()}, at::CUDA(at_kINT));
auto diF = d_input_features.data<T>(); auto diF = d_input_features.data<T>();
auto doF = d_output_features.data<T>(); auto doF = d_output_features.data<T>();
Int *rb = rulesBuffer.data<Int>(); Int *rb = rulesBuffer.data<Int>();
...@@ -137,7 +137,7 @@ void cuda_BLInputLayer_updateOutput(Metadata<Dimension> &m, ...@@ -137,7 +137,7 @@ void cuda_BLInputLayer_updateOutput(Metadata<Dimension> &m,
output_features.copy_(input_features); output_features.copy_(input_features);
output_features.resize_({*m.inputNActive, nPlanes}); output_features.resize_({*m.inputNActive, nPlanes});
} else { } else {
auto rulesBuffer = at::CUDA(at_kINT).tensor({(int)rules[1].size()}); auto rulesBuffer = at::empty({(int)rules[1].size()}, at::CUDA(at_kINT));
auto iF = input_features.data<T>(); auto iF = input_features.data<T>();
auto oF = output_features.data<T>(); auto oF = output_features.data<T>();
Int *rb = rulesBuffer.data<Int>(); Int *rb = rulesBuffer.data<Int>();
...@@ -164,7 +164,7 @@ void cuda_BLInputLayer_updateGradInput( ...@@ -164,7 +164,7 @@ void cuda_BLInputLayer_updateGradInput(
} else { } else {
d_input_features.resize_({rules[0][2], rules[0][3], nPlanes}); d_input_features.resize_({rules[0][2], rules[0][3], nPlanes});
d_input_features.zero_(); d_input_features.zero_();
auto rulesBuffer = at::CUDA(at_kINT).tensor({(int)rules[1].size()}); auto rulesBuffer = at::empty({(int)rules[1].size()}, at::CUDA(at_kINT));
auto diF = d_input_features.data<T>(); auto diF = d_input_features.data<T>();
auto doF = d_output_features.data<T>(); auto doF = d_output_features.data<T>();
Int *rb = rulesBuffer.data<Int>(); Int *rb = rulesBuffer.data<Int>();
...@@ -191,7 +191,7 @@ void cuda_BLOutputLayer_updateOutput( ...@@ -191,7 +191,7 @@ void cuda_BLOutputLayer_updateOutput(
} else { } else {
output_features.resize_({rules[0][2], rules[0][3], nPlanes}); output_features.resize_({rules[0][2], rules[0][3], nPlanes});
output_features.zero_(); output_features.zero_();
auto rulesBuffer = at::CUDA(at_kINT).tensor({(int)rules[1].size()}); auto rulesBuffer = at::empty({(int)rules[1].size()}, at::CUDA(at_kINT));
auto iF = input_features.data<T>(); auto iF = input_features.data<T>();
auto oF = output_features.data<T>(); auto oF = output_features.data<T>();
Int *rb = rulesBuffer.data<Int>(); Int *rb = rulesBuffer.data<Int>();
...@@ -216,7 +216,7 @@ void cuda_BLOutputLayer_updateGradInput( ...@@ -216,7 +216,7 @@ void cuda_BLOutputLayer_updateGradInput(
} else { } else {
d_input_features.resize_({nRows, nPlanes}); d_input_features.resize_({nRows, nPlanes});
d_input_features.zero_(); d_input_features.zero_();
auto rulesBuffer = at::CUDA(at_kINT).tensor({(int)rules[1].size()}); auto rulesBuffer = at::empty({(int)rules[1].size()}, at::CUDA(at_kINT));
auto diF = d_input_features.data<T>(); auto diF = d_input_features.data<T>();
auto doF = d_output_features.data<T>(); auto doF = d_output_features.data<T>();
Int *rb = rulesBuffer.data<Int>(); Int *rb = rulesBuffer.data<Int>();
......
sparseconvnet/SCN/CUDA/RuleBookIterator.h
View file @ aebbb122
...@@ -17,7 +17,7 @@ ...@@ -17,7 +17,7 @@
Int rbMaxSize = 0; \ Int rbMaxSize = 0; \
for (auto &r : _rules) \ for (auto &r : _rules) \
rbMaxSize = std::max(rbMaxSize, (Int)r.size()); \ rbMaxSize = std::max(rbMaxSize, (Int)r.size()); \
at::Tensor rulesBuffer = at::CUDA(at_kINT).tensor({rbMaxSize}); \ at::Tensor rulesBuffer = at::empty({rbMaxSize}, at::CUDA(at_kINT)); \
Int *rbB = rulesBuffer.data<Int>(); \ Int *rbB = rulesBuffer.data<Int>(); \
for (int k = 0; k < _rules.size(); ++k) { \ for (int k = 0; k < _rules.size(); ++k) { \
auto &r = _rules[k]; \ auto &r = _rules[k]; \
......
sparseconvnet/SCN/Metadata/Metadata.cpp
View file @ aebbb122
...@@ -588,13 +588,13 @@ Metadata<dimension>::compareSparseHelper(Metadata<dimension> &mR, ...@@ -588,13 +588,13 @@ Metadata<dimension>::compareSparseHelper(Metadata<dimension> &mR,
} }
} }
} }
at::Tensor cL_ = torch::CPU(at::kLong).tensor({(long)cL.size()}); at::Tensor cL_ = at::empty({(long)cL.size()}, at::CPU(at::kLong));
std::memcpy(cL_.data<long>(), &cL[0], cL.size() * sizeof(long)); std::memcpy(cL_.data<long>(), &cL[0], cL.size() * sizeof(long));
at::Tensor cR_ = torch::CPU(at::kLong).tensor({(long)cR.size()}); at::Tensor cR_ = at::empty({(long)cR.size()}, at::CPU(at::kLong));
std::memcpy(cR_.data<long>(), &cR[0], cR.size() * sizeof(long)); std::memcpy(cR_.data<long>(), &cR[0], cR.size() * sizeof(long));
at::Tensor L_ = torch::CPU(at::kLong).tensor({(long)L.size()}); at::Tensor L_ = at::empty({(long)L.size()}, at::CPU(at::kLong));
std::memcpy(L_.data<long>(), &L[0], L.size() * sizeof(long)); std::memcpy(L_.data<long>(), &L[0], L.size() * sizeof(long));
at::Tensor R_ = torch::CPU(at::kLong).tensor({(long)R.size()}); at::Tensor R_ = at::empty({(long)R.size()}, at::CPU(at::kLong));
std::memcpy(R_.data<long>(), &R[0], R.size() * sizeof(long)); std::memcpy(R_.data<long>(), &R[0], R.size() * sizeof(long));
return {cL_, cR_, L_, R_}; return {cL_, cR_, L_, R_};
} }
......
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