Pointnet2/Pointnet++ PyTorch

Project Status: Unmaintained. Due to finite time, I have no plans to update this code and I will not be responding to issues.

• Implemention of Pointnet2/Pointnet++ written in PyTorch <http://pytorch.org>_.

• Supports Multi-GPU via nn.DataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.DataParallel>_.

• Supports PyTorch version >= 1.0.0. Use v1.0 <https://github.com/erikwijmans/Pointnet2_PyTorch/releases/tag/v1.0>_ for support of older versions of PyTorch.

See the official code release for the paper (in tensorflow), charlesq34/pointnet2 <https://github.com/charlesq34/pointnet2>_, for official model definitions and hyper-parameters.

The custom ops used by Pointnet++ are currently ONLY supported on the GPU using CUDA.

Setup

• 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 than 1.5, but this is not guaranteed.

• Install dependencies

  ::

  pip install -r requirements.txt

Example training

Install with: pip install -e .

There example training script can be found in pointnet2/train.py. The training examples are built using PyTorch Lightning <https://github.com/williamFalcon/pytorch-lightning> and Hydra <https://hydra.cc/>.

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]

Building only the CUDA kernels

::

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"

Contributing

This repository uses black <https://github.com/ambv/black> for linting and style enforcement on python code. For c++/cuda code, clang-format <https://clang.llvm.org/docs/ClangFormat.html> is used for style. The simplest way to comply with style is via pre-commit <https://pre-commit.com/>_

::

pip install pre-commit pre-commit install

Citation

::

@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} }