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extern.hpp
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extern.hpp
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/* Copyright (c) Chris Choy (chrischoy@ai.stanford.edu).
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
* IN THE SOFTWARE.
*
* Please cite "4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural
* Networks", CVPR'19 (https://arxiv.org/abs/1904.08755) if you use any part
* of the code.
*/
#include <pybind11/pybind11.h>
#include <torch/extension.h>
#include "src/common.hpp"
/*************************************
* Convolution
*************************************/
template <typename Dtype, typename Itype>
void ConvolutionForwardCPU(at::Tensor in_feat, at::Tensor out_feat,
at::Tensor kernel, std::vector<int> tensor_strides,
std::vector<int> strides,
std::vector<int> kernel_sizes,
std::vector<int> dilations, int region_type,
at::Tensor offsets, py::object py_in_coords_key,
py::object py_out_coords_key,
py::object py_coords_manager);
template <typename Dtype, typename Itype>
void ConvolutionBackwardCPU(
at::Tensor in_feat, at::Tensor grad_in_feat, at::Tensor grad_out_feat,
at::Tensor kernel, at::Tensor grad_kernel, std::vector<int> tensor_strides,
std::vector<int> strides, std::vector<int> kernel_sizes,
std::vector<int> dilations, int region_type, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager);
#ifndef CPU_ONLY
template <typename Dtype, typename Itype>
void ConvolutionForwardGPU(at::Tensor in_feat, at::Tensor out_feat,
at::Tensor kernel, std::vector<int> tensor_strides,
std::vector<int> strides,
std::vector<int> kernel_sizes,
std::vector<int> dilations, int region_type,
at::Tensor offsets, py::object py_in_coords_key,
py::object py_out_coords_key,
py::object py_coords_manager);
template <typename Dtype, typename Itype>
void ConvolutionBackwardGPU(
at::Tensor in_feat, at::Tensor grad_in_feat, at::Tensor grad_out_feat,
at::Tensor kernel, at::Tensor grad_kernel, std::vector<int> tensor_strides,
std::vector<int> strides, std::vector<int> kernel_sizes,
std::vector<int> dilations, int region_type, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager);
#endif
/*************************************
* Convolution Transpose
*************************************/
template <typename Dtype, typename Itype>
void ConvolutionTransposeForwardCPU(
at::Tensor in_feat, at::Tensor out_feat, at::Tensor kernel,
std::vector<int> tensor_strides, std::vector<int> strides,
std::vector<int> kernel_sizes, std::vector<int> dilations, int region_type,
at::Tensor offsets, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager,
bool generate_new_coords);
template <typename Dtype, typename Itype>
void ConvolutionTransposeBackwardCPU(
at::Tensor in_feat, at::Tensor grad_in_feat, at::Tensor grad_out_feat,
at::Tensor kernel, at::Tensor grad_kernel, std::vector<int> tensor_strides,
std::vector<int> strides, std::vector<int> kernel_sizes,
std::vector<int> dilations, int region_type, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager);
#ifndef CPU_ONLY
template <typename Dtype, typename Itype>
void ConvolutionTransposeForwardGPU(
at::Tensor in_feat, at::Tensor out_feat, at::Tensor kernel,
std::vector<int> tensor_strides, std::vector<int> strides,
std::vector<int> kernel_sizes, std::vector<int> dilations, int region_type,
at::Tensor offsets, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager,
bool generate_new_coords);
template <typename Dtype, typename Itype>
void ConvolutionTransposeBackwardGPU(
at::Tensor in_feat, at::Tensor grad_in_feat, at::Tensor grad_out_feat,
at::Tensor kernel, at::Tensor grad_kernel, std::vector<int> tensor_strides,
std::vector<int> strides, std::vector<int> kernel_sizes,
std::vector<int> dilations, int region_type, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager);
#endif
/*************************************
* AvgPooling
*************************************/
template <typename Dtype, typename Itype>
void AvgPoolingForwardCPU(
at::Tensor in_feat, at::Tensor out_feat, at::Tensor num_nonzero,
std::vector<int> tensor_strides, std::vector<int> strides,
std::vector<int> kernel_sizes, std::vector<int> dilations, int region_type,
at::Tensor offsets, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager, bool use_avg);
template <typename Dtype, typename Itype>
void AvgPoolingBackwardCPU(
at::Tensor in_feat, at::Tensor grad_in_feat, at::Tensor grad_out_feat,
at::Tensor num_nonzero, std::vector<int> tensor_strides,
std::vector<int> strides, std::vector<int> kernel_sizes,
std::vector<int> dilations, int region_type, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager, bool use_avg);
#ifndef CPU_ONLY
template <typename Dtype, typename Itype>
void AvgPoolingForwardGPU(
at::Tensor in_feat, at::Tensor out_feat, at::Tensor num_nonzero,
std::vector<int> tensor_strides, std::vector<int> strides,
std::vector<int> kernel_sizes, std::vector<int> dilations, int region_type,
at::Tensor offsets, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager, bool use_avg);
template <typename Dtype, typename Itype>
void AvgPoolingBackwardGPU(
at::Tensor in_feat, at::Tensor grad_in_feat, at::Tensor grad_out_feat,
at::Tensor num_nonzero, std::vector<int> tensor_strides,
std::vector<int> strides, std::vector<int> kernel_sizes,
std::vector<int> dilations, int region_type, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager, bool use_avg);
#endif
/*************************************
* MaxPooling
*************************************/
template <typename Dtype, typename Itype>
void MaxPoolingForwardCPU(
at::Tensor in_feat, at::Tensor out_feat, at::Tensor num_nonzero,
std::vector<int> tensor_strides, std::vector<int> strides,
std::vector<int> kernel_sizes, std::vector<int> dilations, int region_type,
at::Tensor offsets, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager);
template <typename Dtype, typename Itype>
void MaxPoolingBackwardCPU(
at::Tensor in_feat, at::Tensor grad_in_feat, at::Tensor grad_out_feat,
at::Tensor num_nonzero, std::vector<int> tensor_strides,
std::vector<int> strides, std::vector<int> kernel_sizes,
std::vector<int> dilations, int region_type, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager);
#ifndef CPU_ONLY
template <typename Dtype, typename Itype>
void MaxPoolingForwardGPU(
at::Tensor in_feat, at::Tensor out_feat, at::Tensor num_nonzero,
std::vector<int> tensor_strides, std::vector<int> strides,
std::vector<int> kernel_sizes, std::vector<int> dilations, int region_type,
at::Tensor offsets, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager);
template <typename Dtype, typename Itype>
void MaxPoolingBackwardGPU(
at::Tensor in_feat, at::Tensor grad_in_feat, at::Tensor grad_out_feat,
at::Tensor num_nonzero, std::vector<int> tensor_strides,
std::vector<int> strides, std::vector<int> kernel_sizes,
std::vector<int> dilations, int region_type, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager);
#endif
/*************************************
* PoolingTranspose
*************************************/
template <typename Dtype, typename Itype>
void PoolingTransposeForwardCPU(
at::Tensor in_feat, at::Tensor out_feat, at::Tensor num_nonzero,
std::vector<int> tensor_strides, std::vector<int> strides,
std::vector<int> kernel_sizes, std::vector<int> dilations, int region_type,
at::Tensor offsets, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager);
template <typename Dtype, typename Itype>
void PoolingTransposeBackwardCPU(
at::Tensor in_feat, at::Tensor grad_in_feat, at::Tensor grad_out_feat,
at::Tensor num_nonzero, std::vector<int> tensor_strides,
std::vector<int> strides, std::vector<int> kernel_sizes,
std::vector<int> dilations, int region_type, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager);
#ifndef CPU_ONLY
template <typename Dtype, typename Itype>
void PoolingTransposeForwardGPU(
at::Tensor in_feat, at::Tensor out_feat, at::Tensor num_nonzero,
std::vector<int> tensor_strides, std::vector<int> strides,
std::vector<int> kernel_sizes, std::vector<int> dilations, int region_type,
at::Tensor offsets, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager);
template <typename Dtype, typename Itype>
void PoolingTransposeBackwardGPU(
at::Tensor in_feat, at::Tensor grad_in_feat, at::Tensor grad_out_feat,
at::Tensor num_nonzero, std::vector<int> tensor_strides,
std::vector<int> strides, std::vector<int> kernel_sizes,
std::vector<int> dilations, int region_type, py::object py_in_coords_key,
py::object py_out_coords_key, py::object py_coords_manager);
#endif
/*************************************
* GlobalPooling
*************************************/
template <typename Dtype, typename Itype>
void GlobalPoolingForwardCPU(at::Tensor in_feat, at::Tensor out_feat,
at::Tensor num_nonzero,
py::object py_in_coords_key,
py::object py_out_coords_key,
py::object py_coords_manager, bool use_avg);
template <typename Dtype, typename Itype>
void GlobalPoolingBackwardCPU(at::Tensor in_feat, at::Tensor grad_in_feat,
at::Tensor grad_out_feat, at::Tensor num_nonzero,
py::object py_in_coords_key,
py::object py_out_coords_key,
py::object py_coords_manager, bool use_avg);
#ifndef CPU_ONLY
template <typename Dtype, typename Itype>
void GlobalPoolingForwardGPU(at::Tensor in_feat, at::Tensor out_feat,
at::Tensor num_nonzero,
py::object py_in_coords_key,
py::object py_out_coords_key,
py::object py_coords_manager, bool use_avg);
template <typename Dtype, typename Itype>
void GlobalPoolingBackwardGPU(at::Tensor in_feat, at::Tensor grad_in_feat,
at::Tensor grad_out_feat, at::Tensor num_nonzero,
py::object py_in_coords_key,
py::object py_out_coords_key,
py::object py_coords_manager, bool use_avg);
#endif
/*************************************
* GlobalMaxPooling
*************************************/
template <typename Dtype, typename Itype>
void GlobalMaxPoolingForwardCPU(
at::Tensor in_feat, at::Tensor out_feat, at::Tensor num_nonzero,
py::object py_in_coords_key, py::object py_out_coords_key,
py::object py_coords_manager);
template <typename Dtype, typename Itype>
void GlobalMaxPoolingBackwardCPU(
at::Tensor in_feat, at::Tensor grad_in_feat,
at::Tensor grad_out_feat, at::Tensor num_nonzero,
py::object py_in_coords_key, py::object py_out_coords_key,
py::object py_coords_manager);
#ifndef CPU_ONLY
template <typename Dtype, typename Itype>
void GlobalMaxPoolingForwardGPU(
at::Tensor in_feat, at::Tensor out_feat, at::Tensor num_nonzero,
py::object py_in_coords_key, py::object py_out_coords_key,
py::object py_coords_manager);
template <typename Dtype, typename Itype>
void GlobalMaxPoolingBackwardGPU(
at::Tensor in_feat, at::Tensor grad_in_feat,
at::Tensor grad_out_feat, at::Tensor num_nonzero,
py::object py_in_coords_key, py::object py_out_coords_key,
py::object py_coords_manager);
#endif
/*************************************
* Broadcast
*************************************/
template <typename Dtype, typename Itype>
void BroadcastForwardCPU(at::Tensor in_feat, at::Tensor in_feat_glob,
at::Tensor out_feat, int op,
py::object py_in_coords_key,
py::object py_out_coords_key,
py::object py_coords_manager);
template <typename Dtype, typename Itype>
void BroadcastBackwardCPU(at::Tensor in_feat, at::Tensor grad_in_feat,
at::Tensor in_feat_glob, at::Tensor grad_in_feat_glob,
at::Tensor grad_out_feat, int op,
py::object py_in_coords_key,
py::object py_out_coords_key,
py::object py_coords_manager);
#ifndef CPU_ONLY
template <typename Dtype, typename Itype>
void BroadcastForwardGPU(at::Tensor in_feat, at::Tensor in_feat_glob,
at::Tensor out_feat, int op,
py::object py_in_coords_key,
py::object py_out_coords_key,
py::object py_coords_manager);
template <typename Dtype, typename Itype>
void BroadcastBackwardGPU(at::Tensor in_feat, at::Tensor grad_in_feat,
at::Tensor in_feat_glob, at::Tensor grad_in_feat_glob,
at::Tensor grad_out_feat, int op,
py::object py_in_coords_key,
py::object py_out_coords_key,
py::object py_coords_manager);
#endif
/*************************************
* Pruning
*************************************/
template <typename Dtype, typename Itype>
void PruningForwardCPU(at::Tensor in_feat, at::Tensor out_feat,
at::Tensor use_feat, py::object py_in_coords_key,
py::object py_out_coords_key,
py::object py_coords_manager);
template <typename Dtype, typename Itype>
void PruningBackwardCPU(at::Tensor grad_in_feat, at::Tensor grad_out_feat,
py::object py_in_coords_key,
py::object py_out_coords_key,
py::object py_coords_manager);
#ifndef CPU_ONLY
template <typename Dtype, typename Itype>
void PruningForwardGPU(at::Tensor in_feat, at::Tensor out_feat,
at::Tensor use_feat, py::object py_in_coords_key,
py::object py_out_coords_key,
py::object py_coords_manager);
template <typename Dtype, typename Itype>
void PruningBackwardGPU(at::Tensor grad_in_feat, at::Tensor grad_out_feat,
py::object py_in_coords_key,
py::object py_out_coords_key,
py::object py_coords_manager);
#endif
/*************************************
* Voxelization
*************************************/
#ifndef CPU_ONLY
#include <pybind11/numpy.h>
std::vector<py::array_t<int>>
SparseVoxelization(py::array_t<uint64_t, py::array::c_style> keys,
py::array_t<int, py::array::c_style> labels,
int ignore_label, bool has_label);
#endif