Pytorch CPU and CUDA kernels for spatial search and interpolation for 3D point clouds.
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
import torch
import torch_points_kernels.points_cuda
python setup.py build_ext --inplace
python -m unittest
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
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 as a guide.
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 for more architecture compatibility.
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