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/*
* Copyright (c) 2020 NVIDIA Corporation.
*
* 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 "coordinate_map.hpp"
#include "coordinate_map_cpu.hpp"
#include "coordinate_map_key.hpp"
#include "coordinate_map_manager.hpp"
#include "errors.hpp"
#include "types.hpp"
#include "utils.hpp"
#include "interpolation_kernel.hpp"
#include <pybind11/pybind11.h>
#include <torch/extension.h>
namespace minkowski {
template <typename coordinate_type>
std::vector<at::Tensor>
InterpolationForwardCPU(at::Tensor const &in_feat, //
at::Tensor const &tfield, //
CoordinateMapKey *p_in_map_key, //
cpu_manager_type<coordinate_type> *p_map_manager) {
ASSERT(in_feat.is_contiguous(), "in_feat must be contiguous");
ASSERT(!in_feat.is_cuda(), "in_feat must be CPU");
ASSERT(in_feat.dim() == 2, "in_feat.dim():", in_feat.dim());
ASSERT(tfield.is_contiguous(), "tfield must be contiguous");
ASSERT(!tfield.is_cuda(), "tfield must be CPU");
ASSERT(tfield.dim() == 2, "tfield.dim():", tfield.dim());
ASSERT(tfield.dtype() == in_feat.dtype(),
"tfield and in_feat must have the same dtype");
coordinate_map_key_type in_key = p_in_map_key->get_key();
ASSERT(p_map_manager->exists(in_key), ERROR_MAP_NOT_FOUND);
ASSERT(in_feat.size(0) == p_map_manager->size(in_key), "Invalid in_feat size",
in_feat.size(0), "!=", p_map_manager->size(in_key));
auto map_weight =
p_map_manager->interpolation_map_weight(tfield, p_in_map_key);
LOG_DEBUG("out_feat with size", tfield.size(0), in_feat.size(1));
auto out_feat =
torch::zeros({tfield.size(0), in_feat.size(1)}, tfield.options());
AT_DISPATCH_FLOATING_TYPES(
in_feat.scalar_type(), "interpolation_forward_cpu", [&] {
LOG_DEBUG("InterpolationForwardKernelCPU");
InterpolationForwardKernelCPU<scalar_t, scalar_t, int>(
in_feat.template data_ptr<scalar_t>(),
out_feat.template data_ptr<scalar_t>(), in_feat.size(1),
map_weight[0].template data_ptr<int>(), // in
map_weight[1].template data_ptr<int>(), // out
map_weight[2].template data_ptr<scalar_t>(), // weight
map_weight[0].numel());
});
// to out_feats
map_weight.insert(map_weight.begin(), out_feat);
return map_weight;
}
template <typename coordinate_type>
at::Tensor
InterpolationBackwardCPU(at::Tensor &grad_out_feat, //
at::Tensor const &in_map, //
at::Tensor const &out_map, //
at::Tensor const &weight, //
CoordinateMapKey *p_in_map_key, //
cpu_manager_type<coordinate_type> *p_map_manager) {
if (!grad_out_feat.is_contiguous())
grad_out_feat = grad_out_feat.contiguous();
ASSERT(!grad_out_feat.is_cuda(), "grad_out_feat must be CPU");
ASSERT(grad_out_feat.dim() == 2, "grad_out_feat.dim():", grad_out_feat.dim());
coordinate_map_key_type in_key = p_in_map_key->get_key();
ASSERT(p_map_manager->exists(in_key), ERROR_MAP_NOT_FOUND);
uint32_t const in_nrows = p_map_manager->size(in_key);
uint32_t const nchannel = grad_out_feat.size(1);
LOG_DEBUG("grad_in_feat with size", in_nrows, nchannel);
auto grad_in_feat =
torch::zeros({in_nrows, nchannel}, grad_out_feat.options());
AT_DISPATCH_FLOATING_TYPES(
grad_out_feat.scalar_type(), "interpolation_backward_cpu", [&] {
LOG_DEBUG("InterpolationBackwardKernelCPU");
InterpolationBackwardKernelCPU<scalar_t, scalar_t, int>(
grad_in_feat.template data_ptr<scalar_t>(), in_nrows, nchannel,
grad_out_feat.template data_ptr<scalar_t>(),
in_map.template data_ptr<int>(), // in
out_map.template data_ptr<int>(), // out
weight.template data_ptr<scalar_t>(), // weight
in_map.numel());
});
// to out_feats
return grad_in_feat;
}
template std::vector<at::Tensor>
InterpolationForwardCPU<int32_t>(at::Tensor const &in_feat, //
at::Tensor const &tfield, //
CoordinateMapKey *p_in_map_key, //
cpu_manager_type<int32_t> *p_map_manager);
template at::Tensor
InterpolationBackwardCPU<int32_t>(at::Tensor &grad_out_feat, //
at::Tensor const &in_map, //
at::Tensor const &out_map, //
at::Tensor const &weight, //
CoordinateMapKey *p_in_map_key, //
cpu_manager_type<int32_t> *p_map_manager);
} // end namespace minkowski
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