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/* Copyright (c) 2020 NVIDIA CORPORATION.
* Copyright (c) 2018-2020 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 "coordinate_map_cpu.hpp"
#include "types.hpp"
#include "utils.hpp"
#include <torch/extension.h>
namespace minkowski {
using coordinate_type = int32_t;
using index_type = default_types::index_type;
using size_type = default_types::size_type;
std::pair<size_type, double>
coordinate_map_batch_insert_test(const torch::Tensor &coordinates) {
// Create TensorArgs. These record the names and positions of each tensor as a
// parameter.
torch::TensorArg arg_coordinates(coordinates, "coordinates", 0);
torch::CheckedFrom c = "coordinate_test";
torch::checkContiguous(c, arg_coordinates);
// must match coordinate_type
torch::checkScalarType(c, arg_coordinates, torch::kInt);
torch::checkBackend(c, arg_coordinates.tensor, torch::Backend::CPU);
torch::checkDim(c, arg_coordinates, 2);
auto const N = (index_type)coordinates.size(0);
auto const D = (index_type)coordinates.size(1);
coordinate_type const *ptr = coordinates.data_ptr<coordinate_type>();
CoordinateMapCPU<coordinate_type> map{N, D};
timer t;
t.tic();
map.insert(ptr, ptr + N * D);
return std::make_pair<size_type, double>(map.size(), t.toc());
}
using map_inverse_map_type =
std::pair<std::vector<int64_t>, std::vector<int64_t>>;
std::pair<map_inverse_map_type, double>
coordinate_map_inverse_test(const torch::Tensor &coordinates) {
// Create TensorArgs. These record the names and positions of each tensor as a
// parameter.
torch::TensorArg arg_coordinates(coordinates, "coordinates", 0);
torch::CheckedFrom c = "coordinate_test";
torch::checkContiguous(c, arg_coordinates);
// must match coordinate_type
torch::checkScalarType(c, arg_coordinates, torch::kInt);
torch::checkBackend(c, arg_coordinates.tensor, torch::Backend::CPU);
torch::checkDim(c, arg_coordinates, 2);
auto const N = (index_type)coordinates.size(0);
auto const D = (index_type)coordinates.size(1);
coordinate_type const *ptr = coordinates.data_ptr<coordinate_type>();
CoordinateMapCPU<coordinate_type> map{N, D};
timer t;
t.tic();
std::pair<std::vector<int64_t>, std::vector<int64_t>> unique_inverse_map =
map.insert_and_map<false>(ptr, ptr + N * D);
return std::make_pair<std::pair<std::vector<int64_t>, std::vector<int64_t>>,
double>(std::move(unique_inverse_map), t.toc());
}
std::pair<std::vector<index_type>, std::vector<index_type>>
coordinate_map_batch_find_test(const torch::Tensor &coordinates,
const torch::Tensor &queries) {
// Create TensorArgs. These record the names and positions of each tensor as a
// parameter.
torch::TensorArg arg_coordinates(coordinates, "coordinates", 0);
torch::TensorArg arg_queries(queries, "queries", 1);
torch::CheckedFrom c = "coordinate_test";
torch::checkContiguous(c, arg_coordinates);
torch::checkContiguous(c, arg_queries);
// must match coordinate_type
torch::checkScalarType(c, arg_coordinates, torch::kInt);
torch::checkScalarType(c, arg_queries, torch::kInt);
torch::checkBackend(c, arg_coordinates.tensor, torch::Backend::CPU);
torch::checkBackend(c, arg_queries.tensor, torch::Backend::CPU);
torch::checkDim(c, arg_coordinates, 2);
torch::checkDim(c, arg_queries, 2);
auto const N = (index_type)coordinates.size(0);
auto const D = (index_type)coordinates.size(1);
// auto const NQ = (index_type)queries.size(0);
auto const DQ = (index_type)queries.size(1);
ASSERT(D == DQ, "Coordinates and queries must have the same size.");
coordinate_type const *ptr = coordinates.data_ptr<coordinate_type>();
coordinate_type const *query_ptr = queries.data_ptr<coordinate_type>();
CoordinateMapCPU<coordinate_type> map{N, D};
map.insert(ptr, ptr + N * D);
auto query_coordinates = coordinate_range<coordinate_type>(N, D, query_ptr);
auto query_results =
map.find(query_coordinates.begin(), query_coordinates.end());
return query_results;
}
/******************************************************************************
* New coordinate map generation tests
******************************************************************************/
std::pair<size_type, std::vector<size_type>>
coordinate_map_stride_test(const torch::Tensor &coordinates,
const torch::Tensor &stride) {
// Create TensorArgs. These record the names and positions of each tensor as a
// parameter.
torch::TensorArg arg_coordinates(coordinates, "coordinates", 0);
torch::TensorArg arg_stride(stride, "stride", 1);
torch::CheckedFrom c = "coordinate_map_stride_test";
torch::checkContiguous(c, arg_coordinates);
// must match coordinate_type
torch::checkScalarType(c, arg_coordinates, torch::kInt);
torch::checkBackend(c, arg_coordinates.tensor, torch::Backend::CPU);
torch::checkDim(c, arg_coordinates, 2);
// must match coordinate_type
torch::checkScalarType(c, arg_stride, torch::kInt);
torch::checkBackend(c, arg_stride.tensor, torch::Backend::CPU);
torch::checkDim(c, arg_stride, 1);
auto const N = (index_type)coordinates.size(0);
auto const D = (index_type)coordinates.size(1);
auto const NS = (index_type)stride.size(0);
ASSERT(NS == D - 1, "Invalid stride size", NS);
coordinate_type const *ptr = coordinates.data_ptr<coordinate_type>();
CoordinateMapCPU<coordinate_type> map{N, D};
map.insert(ptr, ptr + N * D);
// Stride
default_types::stride_type stride_vec(NS);
int32_t const *stride_ptr = stride.data_ptr<int32_t>();
for (uint32_t i = 0; i < NS; ++i) {
stride_vec[i] = stride_ptr[i];
ASSERT(stride_ptr[i] > 0, "Invalid stride. All strides must be positive.");
}
auto stride_map = map.stride(stride_vec);
return std::make_pair(stride_map.size(), stride_map.get_tensor_stride());
}
} // namespace minkowski
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("coordinate_map_batch_insert_test",
&minkowski::coordinate_map_batch_insert_test,
"Minkowski Engine coordinate map batch insert test");
m.def("coordinate_map_inverse_test", &minkowski::coordinate_map_inverse_test,
"Minkowski Engine coordinate map batch insert test");
m.def("coordinate_map_batch_find_test",
&minkowski::coordinate_map_batch_find_test,
"Minkowski Engine coordinate map batch find test");
m.def("coordinate_map_stride_test", &minkowski::coordinate_map_stride_test,
"Minkowski Engine coordinate map stride test");
}
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