PyTorch kernels for spatial operations on point clouds
Project description
3D Point Cloud Kernels
Pytorch CPU and CUDA kernels for spatial search and interpolation for 3D point clouds.
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
To force CUDA installation (for example on Docker builds) please use the flag FORCE_CUDA
like
pip install torch-points-kernels FORCE_CUDA=1
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 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 for more architecture compatibility.
Projects using those kernels.
Credit
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