p2p_test/lib/Pointnet2_PyTorch

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}
  }