{"id":19836492,"url":"https://github.com/torch-points3d/torch-points-kernels","last_synced_at":"2025-04-05T12:08:30.058Z","repository":{"id":40618602,"uuid":"231463550","full_name":"torch-points3d/torch-points-kernels","owner":"torch-points3d","description":"Pytorch kernels for spatial operations on point clouds","archived":false,"fork":false,"pushed_at":"2025-02-03T23:39:29.000Z","size":324,"stargazers_count":97,"open_issues_count":13,"forks_count":27,"subscribers_count":9,"default_branch":"master","last_synced_at":"2025-03-29T11:11:21.852Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/torch-points3d.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-01-02T21:37:41.000Z","updated_at":"2024-12-24T01:59:40.000Z","dependencies_parsed_at":"2023-02-19T14:55:16.962Z","dependency_job_id":"b0918020-994e-4dca-aadb-a308c94f18dc","html_url":"https://github.com/torch-points3d/torch-points-kernels","commit_stats":null,"previous_names":[],"tags_count":26,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/torch-points3d%2Ftorch-points-kernels","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/torch-points3d%2Ftorch-points-kernels/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/torch-points3d%2Ftorch-points-kernels/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/torch-points3d%2Ftorch-points-kernels/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/torch-points3d","download_url":"https://codeload.github.com/torch-points3d/torch-points-kernels/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247332612,"owners_count":20921853,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-12T12:11:30.837Z","updated_at":"2025-04-05T12:08:30.037Z","avatar_url":"https://github.com/torch-points3d.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 3D Point Cloud Kernels\nPytorch CPU and CUDA kernels for spatial search and interpolation for 3D point clouds.\n\n[![PyPI version](https://badge.fury.io/py/torch-points-kernels.svg)](https://badge.fury.io/py/torch-points-kernels) [![Deploy](https://github.com/torch-points3d/torch-points-kernels/actions/workflows/deploy.yaml/badge.svg)](https://github.com/torch-points3d/torch-points-kernels/actions/workflows/deploy.yaml) [![Unittests](https://github.com/torch-points3d/torch-points-kernels/actions/workflows/tests.yaml/badge.svg)](https://github.com/torch-points3d/torch-points-kernels/actions/workflows/tests.yaml)\n\n## Installation\n**Update:** we now provide precompiled Conda packages for the latest PyTorch/CUDA combinations (PyTorch \u003e= 1.10.0). To install with conda:\n```\nconda install -c torch-points3d torch-points-kernels\n```\n\nOr, you can compile the wheel yourself for any PyTorch/CUDA combination (must have a matching installation of CUDA toolkit):\n```\npip install torch-points-kernels\n```\n\nTo force CUDA installation (for example on Docker builds) please use the flag `FORCE_CUDA`:\n```\nFORCE_CUDA=1 pip install torch-points-kernels\n```\n\n## Usage\n```\nimport torch\nimport torch_points_kernels.points_cuda\n```\n\n## Build and test\n```\npython setup.py build_ext --inplace\npython -m unittest\n```\n\n## Troubleshooting\n\n### Compilation issues\nEnsure that at least PyTorch 1.4.0 is installed and verify that `cuda/bin` and `cuda/include` are in your `$PATH` and `$CPATH` respectively, e.g.:\n```\n$ python -c \"import torch; print(torch.__version__)\"\n\u003e\u003e\u003e 1.4.0\n\n$ echo $PATH\n\u003e\u003e\u003e /usr/local/cuda/bin:...\n\n$ echo $CPATH\n\u003e\u003e\u003e /usr/local/cuda/include:...\n```\n\nOn the compilation, if you have this error:\n```error: cannot call member function 'void std::basic_string\u003c_CharT, _Traits, _Alloc\u003e::_Rep::_M_set_sharable()```\nit means that your nvcc version is too old. The version must be at least 10.1.168.\nTo check the version:\n```\nnvcc --version\n\u003e\u003e\u003e V10.1.168\n```\n\n### Windows compilation\nOn Windows you may have this error when compiling:\n```\nerror: member \"torch::jit::detail::ModulePolicy::all_slots\" may not be initialized\nerror: member \"torch::jit::detail::ParameterPolicy::all_slots\" may not be initialized\nerror: member \"torch::jit::detail::BufferPolicy::all_slots\" may not be initialized\nerror: member \"torch::jit::detail::AttributePolicy::all_slots\" may not be initialized\n```\nThis requires you to edit some of your pytorch header files, use [this script](https://github.com/rusty1s/pytorch_scatter/blob/master/script/torch.sh) as a guide.\n\n### CUDA kernel failed : no kernel image is available for execution on the device\n\nThis can happen when trying to run the code on a different GPU than the one used to compile the `torch-points-kernels` library. Uninstall `torch-points-kernels`, clear cache, and reinstall after setting the `TORCH_CUDA_ARCH_LIST` environment variable. For example, for compiling with a Tesla T4 (Turing 7.5) and running the code on a Tesla V100 (Volta 7.0) use:\n```\nexport TORCH_CUDA_ARCH_LIST=\"7.0;7.5\"\n```\nSee [this useful chart](http://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/) for more architecture compatibility.\n\n\n## Projects using those kernels.\n\n[```Pytorch Point Cloud Benchmark```](https://github.com/nicolas-chaulet/deeppointcloud-benchmarks)\n\n## Credit\n\n* [```Pointnet2_Tensorflow```](https://github.com/charlesq34/pointnet2) by [Charles R. Qi](https://github.com/charlesq34)\n\n* [```Pointnet2_PyTorch```](https://github.com/erikwijmans/Pointnet2_PyTorch) by [Erik Wijmans](https://github.com/erikwijmans)\n\n* [```GRNet```](https://github.com/hzxie/GRNet) by [Haozhe Xie](https://github.com/hzxie)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftorch-points3d%2Ftorch-points-kernels","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftorch-points3d%2Ftorch-points-kernels","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftorch-points3d%2Ftorch-points-kernels/lists"}