sample conda env: /private/home/s9xie/.conda/envs/s9xie_env
load modules
- ::
source module_load.sh
Building _ext module
- ::
python setup.py build_ext --inplace
- 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 with2.7
,3.5
, and3.6
Install dependencies
pip install -r requirments.txt
Building _ext module
python setup.py build_ext --inplace
Optionally, you can also install this repo as a package
pip install -e .
Two training examples are provided by pointnet2/train/train_sem_seg.py
and pointnet2/train/train_cls.py
.
The datasets for both will be downloaded automatically by default.
They can be run via
python -m pointnet2.train.train_cls python -m pointnet2.train.train_sem_seg
Both scripts will print training progress after every epoch to the command line. Use the --visdom
flag to
enable logging to visdom and more detailed logging of training progress.
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} }