Metadata-Version: 2.1
Name: torch-points-kernels
Version: 0.6.6
Summary: PyTorch kernels for spatial operations on point clouds
Home-page: https://github.com/nicolas-chaulet/torch-points-kernels
Author: Nicolas Chaulet
License: UNKNOWN
Download-URL: https://github.com/nicolas-chaulet/torch-points-kernels/archive/0.6.6.tar.gz
Description: # 3D Point Cloud Kernels
        Pytorch CPU and CUDA kernels for spatial search and interpolation for 3D point clouds.
        
        [![PyPI version](https://badge.fury.io/py/torch-points-kernels.svg)](https://badge.fury.io/py/torch-points-kernels) ![Deploy](https://github.com/nicolas-chaulet/torch-points-kernels/workflows/Deploy/badge.svg) ![Unittests](https://github.com/nicolas-chaulet/torch-points-kernels/workflows/Unittests/badge.svg)
        
        ## Installation
        **Requires torch** version 1.0 or higher to be installed before proceeding. Once this is done, simply run
        ```
        pip install torch-points-kernels
        ```
        or with poetry:
        ```
        poetry add 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
        
        $ python -c "import torch; print(torch.__version__)"
        >>> 1.1.0
        
        $ echo $PATH
        >>> /usr/local/cuda/bin:...
        
        $ echo $CPATH
        >>> /usr/local/cuda/include:...
        ```
        
        
        ### 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)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
