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.
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.

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 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]

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 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

Citation