67 lines
2.7 KiB
Plaintext
67 lines
2.7 KiB
Plaintext
/*
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* SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <torch/extension.h>
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#include <c10/cuda/CUDAException.h>
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#include "cuda_kdtree.cuh"
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#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")
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#define CHECK_IS_FLOAT(x) TORCH_CHECK(x.scalar_type() == at::ScalarType::Float, #x " must be a float tensor")
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std::vector<torch::Tensor> nearestNeighbours(torch::Tensor query, torch::Tensor tree, int knn) {
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CHECK_CUDA(query);
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CHECK_IS_FLOAT(query);
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CHECK_CUDA(tree);
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CHECK_IS_FLOAT(tree);
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TORCH_CHECK(tree.size(0) >= knn, "knn is too small compared to the size of point cloud!");
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torch::Tensor strided_tree = tree;
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torch::Tensor strided_query = query;
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torch::Device device = tree.device();
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long n_queries = query.size(0);
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long n_tree = tree.size(0);
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if (strided_tree.stride(0) != 4) {
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strided_tree = torch::zeros({n_tree, 4}, torch::dtype(torch::kFloat32).device(device));
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strided_tree.index_put_(
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{torch::indexing::Ellipsis, torch::indexing::Slice(torch::indexing::None, tree.size(1))}, tree);
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}
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if (strided_query.stride(0) != 3) {
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strided_query = torch::zeros({n_queries, 3}, torch::dtype(torch::kFloat32).device(device));
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strided_query.index_put_(
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{torch::indexing::Ellipsis, torch::indexing::Slice(torch::indexing::None, query.size(1))}, query);
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}
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auto *knn_index = new tinyflann::KDTreeCuda3dIndex<tinyflann::CudaL2>(strided_tree.data_ptr<float>(), n_tree);
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knn_index->buildIndex();
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C10_CUDA_KERNEL_LAUNCH_CHECK();
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torch::Tensor dist = torch::empty(n_queries * knn, torch::dtype(torch::kFloat32).device(device));
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torch::Tensor indices = torch::empty(n_queries * knn, torch::dtype(torch::kInt32).device(device));
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knn_index->knnSearch(strided_query.data_ptr<float>(), n_queries, 3, indices.data_ptr<int>(), dist.data_ptr<float>(),
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knn);
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C10_CUDA_KERNEL_LAUNCH_CHECK();
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delete knn_index;
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return {dist.reshape({n_queries, knn}), indices.reshape({n_queries, knn})};
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}
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