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Point Transformer V2

This repo is not actively maintained. Please checkout our new codebase Pointcept.

This is a lightweight and easy-to-use codebase for point cloud recognition research supporting indoor & outdoor point cloud datasets and several backbones, namely PointCloudRecog (PCR). The next release version of PCR will further support instance segmentation, object detection, and pretraining. This is an official implementation of the following paper:

Point Transformer V2: Grouped Vector Attention and Partition-based Pooling
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao
Neural Information Processing Systems (NeurIPS) 2022
[ Arxiv ] [ Bib ]

News

Dec, 2022: Initial release our PCR codebase and PTv2 official implementation.
Sep, 2022: PTv2 accepted by NeurIPS 2022.

Installation

Requirements

Ubuntu: 18.04 or higher
CUDA: 10.2 or higher
PyTorch: 1.10.0 ~ 1.11.0
Hardware: 4 x 24G memory GPUs or better

Conda Environment

conda create -n pcr python=3.8 -y
conda activate pcr
conda install ninja -y
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge -y
conda install -c anaconda h5py pyyaml -y
conda install -c conda-forge sharedarray tensorboardx yapf addict einops scipy plyfile termcolor timm -y
conda install -c pyg pytorch-cluster pytorch-scatter pytorch-sparse -y
pip install torch-geometric

# spconv (SparseUNet)
# refer https://github.com/traveller59/spconv
pip install spconv-cu113

Optional Installation

# Open3D (Visualization)
pip install open3d

# PTv1 & PTv2
cd libs/pointops
python setup.py install
cd ../..

# stratified transformer
pip install torch-points3d

# fix dependence, caused by install torch-points3d 
pip uninstall SharedArray
pip install SharedArray==3.2.1

cd libs/pointops2
python setup.py install
cd ../..

# MinkowskiEngine (SparseUNet)
# refer https://github.com/NVIDIA/MinkowskiEngine

# torchsparse (SPVCNN)
# refer https://github.com/mit-han-lab/torchsparse
# install method without sudo apt install
conda install google-sparsehash -c bioconda
export C_INCLUDE_PATH=${CONDA_PREFIX}/include:$C_INCLUDE_PATH
export CPLUS_INCLUDE_PATH=${CONDA_PREFIX}/include:CPLUS_INCLUDE_PATH
pip install --upgrade git+https://github.com/mit-han-lab/torchsparse.git

Data Preparation

ScanNet v2

The preprocessing support semantic and instance segmentation for both ScanNet20 and ScanNet200.

Download the ScanNet v2 dataset.
Run preprocessing code for raw ScanNet as follows:

# RAW_SCANNET_DIR: the directory of downloaded ScanNet v2 raw dataset.
# PROCESSED_SCANNET_DIR: the directory of processed ScanNet dataset (output dir).
python pcr/datasets/preprocessing/scannet/preprocess_scannet.py --dataset_root ${RAW_SCANNET_DIR} --output_root ${PROCESSED_SCANNET_DIR}

Link processed dataset to codebase:

# PROCESSED_SCANNET_DIR: the directory of processed ScanNet dataset.
mkdir data
ln -s ${RAW_SCANNET_DIR} ${CODEBASE_DIR}/data/scannet

S3DIS

Download S3DIS data by filling this Google form. Download the Stanford3dDataset_v1.2_Aligned_Version.zip file and unzip it.
The original S3DIS data contains some bugs data need manually fix it. xxx^@xxx
Run preprocessing code for S3DIS as follows:

# RAW_S3DIS_DIR: the directory of downloaded Stanford3dDataset_v1.2_Aligned_Version dataset.
# PROCESSED_S3DIS_DIR: the directory of processed S3DIS dataset (output dir).
python pcr/datasets/preprocessing/s3dis/preprocess_s3dis.py --dataset_root ${RAW_S3DIS_DIR} --output_root ${PROCESSED_S3DIS_DIR}

Link processed dataset to codebase.

# PROCESSED_S3DIS_DIR: the directory of processed S3DIS dataset.
mkdir data
ln -s ${RAW_S3DIS_DIR} ${CODEBASE_DIR}/data/s3dis

Semantic KITTI

Download Semantic KITTI dataset.
Link dataset to codebase.

# SEMANTIC_KITTI_DIR: the directory of Semantic KITTI dataset.
mkdir data
ln -s ${SEMANTIC_KITTI_DIR} ${CODEBASE_DIR}/data/semantic_kitti

Quick Start

Training

Train from scratch. The training processing is based on configs in configs folder. The training script will generate an experiment folder in exp folder and backup essential code in the experiment folder. Training config, log, tensorboard and checkpoints will also be saved into the experiment folder during the training process.

export CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES}
# Script (Recommended)
sh scripts/train.sh -p ${INTERPRETER_PATH} -g ${NUM_GPU} -d ${DATASET_NAME} -c ${CONFIG_NAME} -n ${EXP_NAME}
# Direct
export PYTHONPATH=./
python tools/train.py --config-file ${CONFIG_PATH} --num-gpus ${NUM_GPU} --options save_path=${SAVE_PATH}

For example:

# By script (Recommended)
# -p is default set as python and can be ignored
sh scripts/train.sh -p python -d scannet -c semseg-ptv2m2-0-base -n semseg-ptv2m2-0-base
# Direct
export PYTHONPATH=./
python tools/train.py --config-file configs/scannet/semseg-ptv2m2-0-base.py --options save_path=exp/scannet/semseg-ptv2m2-0-base

Resume training from checkpoint. If the training process is interrupted by accident, the following script can resume training from a given checkpoint.

export CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES}
# Script (Recommended)
# simply add "-r true"
sh scripts/train.sh -p ${INTERPRETER_PATH} -g ${NUM_GPU} -d ${DATASET_NAME} -c ${CONFIG_NAME} -n ${EXP_NAME} -r true
# Direct
export PYTHONPATH=./
python tools/train.py --config-file ${CONFIG_PATH} --num-gpus ${NUM_GPU} --options save_path=${SAVE_PATH} resume=True weight=${CHECKPOINT_PATH}

Testing

The validation during training only evaluate model on point clouds after grid sampling (voxelization) and testing is need to achieve a precise evaluate result. Our testing code support TTA (test time augmentation) testing. (Currently only support testing on a single GPU, I might add support to multi-gpus testing in the future version.)

# By script (Based on experiment folder created by training script)
sh scripts/test.sh -p ${INTERPRETER_PATH} -d ${DATASET_NAME} -n ${EXP_NAME} -w ${CHECKPOINT_NAME}
# Direct
export PYTHONPATH=./
python tools/test.py --config-file ${CONFIG_PATH} --options save_path=${SAVE_PATH} weight=${CHECKPOINT_PATH}

For example:

# By script (Based on experiment folder created by training script)
# -p is default set as python and can be ignored
# -w is default set as model_best and can be ignored
sh scripts/test.sh -p python -d scannet -n semseg-ptv2m2-0-base -w model_best
# Direct
export PYTHONPATH=./
python tools/test.py --config-file configs/scannet/semseg-ptv2m2-0-base.py --options save_path=exp/scannet/semseg-ptv2m2-0-base weight=exp/scannet/semseg-ptv2m2-0-base/models/model_best.pth

Offset

Offset is the separator of point clouds in batch data, and it is similar to the concept of Batch in PyG. A visual illustration of batch and offset is as follows:

Model Zoo

The PCR codebase supports most combinations of supporting datasets and models, and I haven't tested and tuned all the combinations. Consequently, I list configs for some of them. It would be helpful to reach me if you find a better setting.

Currently, PCR only focuses on semantic segmentation, but in the next version, I will introduce the hook mechanism to support pretraining, instance segmentation, and object detection. The released version contains some code for classification, but I did not maintain it since object-level recognition is quite different from scene-level recognition. I will focus more on the scene-level point clouds. Nevertheless, the code framework support object classification. If you want to run classification tasks, you can modify the codebase from these unmaintained codes for object classification.

Recommendations:

PTv2m2 (good performance)
SparseUNet-SpConv (fast, lightweight and easy to install)

(Please email me your recommendations, I might add support in a future version)

Point Transformer V1 & V2

PTv2 mode2 (recommend)

The original PTv2 was trained on 4 * RTX a6000 (48G memory). Even enabling AMP, the memory cost of the original PTv2 is slightly larger than 24G. Considering GPUs with 24G memory are much more accessible, I tuned the PTv2 on the latest PCR codebase and made it runnable on 4 * RTX 3090 machines.

PTv2 Mode2 enables AMP and disables Position Encoding Multiplier & Grouped Linear. During our further research, we found that precise coordinates are not necessary for point cloud understanding (Replacing precise coordinates with grid coordinates doesn't influence the performance. Also, SparseUNet is an example). As for Grouped Linear, my implementation of Grouped Linear seems to cost more memory than the Linear layer provided by PyTorch. Benefiting from the codebase and better parameter tuning, we also relieve the overfitting problem. The reproducing performance is even better than the results reported in our paper.

Example running script is as follows:

# ptv2m2: PTv2 mode2, disable PEM & Grouped Linear, GPU memory cost < 24G (recommend)
# ScanNet
sh scripts/train.sh -g 4 -d scannet -c semseg-ptv2m2-0-base -n semseg-ptv2m2-0-base
# ScanNet test benchmark (train on train set and val set)
sh scripts/train.sh -g 4 -d scannet -c semseg-ptv2m2-1-benchmark-submit -n semseg-ptv2m2-1-benchmark-submit
# ScanNet200
sh scripts/train.sh -g 4 -d scannet200 -c semseg-ptv2m2-0-base -n semseg-ptv2m2-0-base
# S3DIS
sh scripts/train.sh -g 4 -d s3dis -c semseg-ptv2m2-0-base -n semseg-ptv2m2-0-base

Example training and testing records are as follows:

Dataset	mIoU	mAcc	allAcc	Config	Train	Test	Tensorboard
ScanNet v2 20	75.5	82.9	91.2	config	log	log	tensorboard
ScanNet v2 200	31.9	39.2	82.7	config	log	log	tensorboard
S3DIS Area 5	72.6	78.0	91.6	config	log	log	tensorboard

*Dataset represents reported results from an older version of the PCR codebase.

PTv2 mode1

PTv2 mode1 is the original PTv2 we reported in our paper, example running script is as follows:

# ptv2m1: PTv2 mode1, Original PTv2, GPU memory cost > 24G
# ScanNet
sh scripts/train.sh -g 4 -d scannet -c semseg-ptv2m1-0-base -n semseg-ptv2m1-0-base
# ScanNet200
sh scripts/train.sh -g 4 -d scannet200 -c semseg-ptv2m1-0-base -n semseg-ptv2m1-0-base
# S3DIS
sh scripts/train.sh -g 4 -d s3dis -c semseg-ptv2m1-0-base -n semseg-ptv2m1-0-base

PTv1

The original PTv1 is also available in our PCR codebase. I haven't run PTv1 for a long time, but I have ensured that the example running script works well.

# ScanNet
sh scripts/train.sh -g 4 -d scannet -c semseg-ptv1-0-base -n semseg-ptv1-0-base
# ScanNet200
sh scripts/train.sh -g 4 -d scannet200 -c semseg-ptv1-0-base -n semseg-ptv1-0-base
# S3DIS
sh scripts/train.sh -g 4 -d s3dis -c semseg-ptv1-0-base -n semseg-ptv1-0-base

Stratified Transformer

Uncomment # from .stratified_transformer import * in pcr/models/__init__.py.
Refer Optional Installation to install dependence.
Training with the following example running scripts:

# stv1m1: Stratified Transformer mode1, Modified from the original Stratified Transformer code.
# ptv2m2: Stratified Transformer mode2, My rewrite version (recommend).

# ScanNet
sh scripts/train.sh -g 4 -d scannet -c semseg-stv1m2-0-refined -n semseg-stv1m2-0-refined
sh scripts/train.sh -g 4 -d scannet -c semseg-stv1m1-0-origin -n semseg-stv1m1-0-origin
# ScanNet200
sh scripts/train.sh -g 4 -d scannet200 -c semseg-stv1m2-0-refined -n semseg-stv1m2-0-refined
# S3DIS
sh scripts/train.sh -g 4 -d s3dis -c semseg-stv1m2-0-refined -n semseg-stv1m2-0-refined

I did not tune the parameters for Stratified Transformer and just ensured it could run.

SparseUNet

The PCR codebase provides SparseUNet implemented by SpConv and MinkowskiEngine. The SpConv version is recommended since SpConv is easy to install and faster than MinkowskiEngine. Meanwhile, the PCR codebase will add more support to instance segmentation and object detection in the future version. SpConv is also widely applied in these areas.

SpConv (recommend)

The SpConv version SparseUNet in the codebase was fully rewrite from Li Jiang's code, example running script is as follows:

# Uncomment "# from .sparse_unet import *" in "pcr/models/__init__.py"
# Uncomment "# from .spconv_unet import *" in "pcr/models/sparse_unet/__init__.py"
# ScanNet val
sh scripts/train.sh -g 4 -d scannet -c semseg-spunet34c-0-base -n semseg-spunet34c-0-base
# ScanNet200
sh scripts/train.sh -g 4 -d scannet200 -c semseg-spunet34c-0-base -n semseg-spunet34c-0-base
# S3DIS
sh scripts/train.sh -g 4 -d s3dis -c semseg-spunet34c-0-base -n semseg-spunet34c-0-base
# Semantic-KITTI
sh scripts/train.sh -g 2 -d semantic-kitti -c semseg-spunet34c-0-base -n semseg-spunet34c-0-base

Example training and testing records are as follows:

Dataset	mIoU	mAcc	allAcc	Config	Train	Test	Tensorboard
ScanNet v2 20	73.6	82.2	90.4	config	log	log	tensorboard
ScanNet v2 200	28.8	36.1	81.1	config	log	log	tensorboard
S3DIS Area 5	67.7	73.1	90.1	config	log	log	tensorboard

*Dataset represents reported results from an older version of the PCR codebase.

MinkowskiEngine

The MinkowskiEngine version SparseUNet in the codebase was modified from original MinkowskiEngine repo, and example running script is as follows:

# Uncomment "# from .sparse_unet import *" in "pcr/models/__init__.py"
# Uncomment "# from .mink_unet import *" in "pcr/models/sparse_unet/__init__.py"
# ScanNet
sh scripts/train.sh -g 4 -d scannet -c semseg-minkunet34c-0-base -n semseg-minkunet34c-0-base
# ScanNet200
sh scripts/train.sh -g 4 -d scannet200 -c semseg-minkunet34c-0-base -n semseg-minkunet34c-0-base
# S3DIS
sh scripts/train.sh -g 4 -d s3dis -c semseg-minkunet34c-0-base -n semseg-minkunet34c-0-base
# Semantic-KITTI
sh scripts/train.sh -g 2 -d semantic-kitti -c semseg-minkunet34c-0-base -n semseg-minkunet34c-0-base

SPVCNN

SPVCNN is baseline model of SPVNAS, it is also a practical baseline for outdoor dataset.

# Semantic-KITTI
sh scripts/train.sh -g 2 -d semantic-kitti -c semseg-spvcnn34c-0-base -n semseg-spvcnn34c-0-base

Citation

If you find this work useful to your research, please cite our work:

@inproceedings{wu2022point,
  title     = {Point transformer V2: Grouped Vector Attention and Partition-based Pooling},
  author    = {Wu, Xiaoyang and Lao, Yixing and Jiang, Li and Liu, Xihui and Zhao, Hengshuang},
  booktitle = {NeurIPS},
  year      = {2022}
}

Acknowledgement

The repo is derived from Point Transformer code and inspirited by several repos, e.g., MinkowskiEngine, pointnet2, mmcv, and Detectron2.