- Implemention of Pointnet2/Pointnet++ written in PyTorch.
- Supports Multi-GPU via nn.DataParallel.
- Supports PyTorch version >= 1.0.0. Use v1.0 for support of older versions of PyTorch.
See the official code release for the paper (in tensorflow), charlesq34/pointnet2, for official model definitions and hyper-parameters.
The custom ops used by Pointnet++ are currently ONLY supported on the GPU using CUDA.
Install
python
-- This repo is tested with{3.6, 3.7}
Install
pytorch
with CUDA -- This repo is tested with{1.4, 1.5}
. It may work with versions newer than1.5
, but this is not guaranteed.Install dependencies
pip install -r requirements.txt
Install with: pip install -e .
There example training script can be found in pointnet2/train.py
. The training examples are built
using PyTorch Lightning and Hydra.
A classifion pointnet can be trained as
python pointnet2/train.py task=cls # Or with model=msg for multi-scale grouping python pointnet2/train.py task=cls model=msg
Similarly, semantic segmentation can be trained by changing the task to semseg
python pointnet2/train.py task=semseg
Multi-GPU training can be enabled by passing a list of GPU ids to use, for instance
python pointnet2/train.py task=cls gpus=[0,1,2,3]
pip install pointnet2_ops_lib/. # Or if you would like to install them directly (this can also be used in a requirements.txt) pip install "git+git://github.com/erikwijmans/Pointnet2_PyTorch.git#egg=pointnet2_ops&subdirectory=pointnet2_ops_lib"
This repository uses black for linting and style enforcement on python code. For c++/cuda code, clang-format is used for style. The simplest way to comply with style is via pre-commit
pip install pre-commit pre-commit install
@article{pytorchpointnet++, Author = {Erik Wijmans}, Title = {Pointnet++ Pytorch}, Journal = {https://github.com/erikwijmans/Pointnet2_PyTorch}, Year = {2018} } @inproceedings{qi2017pointnet++, title={Pointnet++: Deep hierarchical feature learning on point sets in a metric space}, author={Qi, Charles Ruizhongtai and Yi, Li and Su, Hao and Guibas, Leonidas J}, booktitle={Advances in Neural Information Processing Systems}, pages={5099--5108}, year={2017} }