https://github.com/torch-points3d/torch-points-kernels
Pytorch kernels for spatial operations on point clouds
https://github.com/torch-points3d/torch-points-kernels
Last synced: over 1 year ago
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Pytorch kernels for spatial operations on point clouds
- Host: GitHub
- URL: https://github.com/torch-points3d/torch-points-kernels
- Owner: torch-points3d
- License: mit
- Created: 2020-01-02T21:37:41.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2025-02-03T23:39:29.000Z (over 1 year ago)
- Last Synced: 2025-03-29T11:11:21.852Z (over 1 year ago)
- Language: C++
- Homepage:
- Size: 316 KB
- Stars: 97
- Watchers: 9
- Forks: 27
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# 3D Point Cloud Kernels
Pytorch CPU and CUDA kernels for spatial search and interpolation for 3D point clouds.
[](https://badge.fury.io/py/torch-points-kernels) [](https://github.com/torch-points3d/torch-points-kernels/actions/workflows/deploy.yaml) [](https://github.com/torch-points3d/torch-points-kernels/actions/workflows/tests.yaml)
## Installation
**Update:** we now provide precompiled Conda packages for the latest PyTorch/CUDA combinations (PyTorch >= 1.10.0). To install with conda:
```
conda install -c torch-points3d torch-points-kernels
```
Or, you can compile the wheel yourself for any PyTorch/CUDA combination (must have a matching installation of CUDA toolkit):
```
pip install torch-points-kernels
```
To force CUDA installation (for example on Docker builds) please use the flag `FORCE_CUDA`:
```
FORCE_CUDA=1 pip install torch-points-kernels
```
## Usage
```
import torch
import torch_points_kernels.points_cuda
```
## Build and test
```
python setup.py build_ext --inplace
python -m unittest
```
## Troubleshooting
### Compilation issues
Ensure 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.:
```
$ python -c "import torch; print(torch.__version__)"
>>> 1.4.0
$ echo $PATH
>>> /usr/local/cuda/bin:...
$ echo $CPATH
>>> /usr/local/cuda/include:...
```
On the compilation, if you have this error:
```error: cannot call member function 'void std::basic_string<_CharT, _Traits, _Alloc>::_Rep::_M_set_sharable()```
it means that your nvcc version is too old. The version must be at least 10.1.168.
To check the version:
```
nvcc --version
>>> V10.1.168
```
### Windows compilation
On Windows you may have this error when compiling:
```
error: member "torch::jit::detail::ModulePolicy::all_slots" may not be initialized
error: member "torch::jit::detail::ParameterPolicy::all_slots" may not be initialized
error: member "torch::jit::detail::BufferPolicy::all_slots" may not be initialized
error: member "torch::jit::detail::AttributePolicy::all_slots" may not be initialized
```
This 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.
### CUDA kernel failed : no kernel image is available for execution on the device
This 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:
```
export TORCH_CUDA_ARCH_LIST="7.0;7.5"
```
See [this useful chart](http://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/) for more architecture compatibility.
## Projects using those kernels.
[```Pytorch Point Cloud Benchmark```](https://github.com/nicolas-chaulet/deeppointcloud-benchmarks)
## Credit
* [```Pointnet2_Tensorflow```](https://github.com/charlesq34/pointnet2) by [Charles R. Qi](https://github.com/charlesq34)
* [```Pointnet2_PyTorch```](https://github.com/erikwijmans/Pointnet2_PyTorch) by [Erik Wijmans](https://github.com/erikwijmans)
* [```GRNet```](https://github.com/hzxie/GRNet) by [Haozhe Xie](https://github.com/hzxie)