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SkeletonGCL

This repo is the official implementation for Graph Contrastive Learning for Skeleton-based Action Recognition. The paper is accepted to ICLR2023.

Architecture of SkeletonGCL

image

Prerequisites

  • Python >= 3.6

  • PyTorch >= 1.1.0

  • PyYAML, tqdm, tensorboardX

  • We provide the dependency file of our experimental environment, you can install all dependencies by creating a new anaconda virtual environment and running pip install -r requirements.txt

  • Run pip install -e torchlight

Implementation

The graph contrastive learning is implemented in this file.

Data Preparation

Download datasets.

There are 3 datasets to download:

  • NTU RGB+D 60 Skeleton
  • NTU RGB+D 120 Skeleton

NTU RGB+D 60 and 120

  1. Request dataset here: https://rose1.ntu.edu.sg/dataset/actionRecognition
  2. Download the skeleton-only datasets:
    1. nturgbd_skeletons_s001_to_s017.zip (NTU RGB+D 60)
    2. nturgbd_skeletons_s018_to_s032.zip (NTU RGB+D 120)
    3. Extract above files to ./data/nturgbd_raw

Data Processing

Directory Structure

Put downloaded data into the following directory structure:

- data/
  - ntu/
  - ntu120/
  - nturgbd_raw/
    - nturgb+d_skeletons/     # from `nturgbd_skeletons_s001_to_s017.zip`
      ...
    - nturgb+d_skeletons120/  # from `nturgbd_skeletons_s018_to_s032.zip`
      ...

Generating Data

  • Generate NTU RGB+D 60 or NTU RGB+D 120 dataset:
 cd ./data/ntu # or cd ./data/ntu120
 # Get skeleton of each performer
 python get_raw_skes_data.py
 # Remove the bad skeleton 
 python get_raw_denoised_data.py
 # Transform the skeleton to the center of the first frame
 python seq_transformation.py

Training & Testing

Training

  • Change the config file depending on what you want, and you can refer to the script folder for the more examples. Notably, please just use one GPU for training because we find that using mutiple GPUs would affect the performances.
# Example: training SkeletonGCL with CTRGCN on NTU RGB+D 120 cross-subject with joint modality on GPU 0
python main.py --config config/nturgbd120-cross-subject/ctr.yaml --work-dir work_dir/ntu120/csub/ctrgcn_joint --device 0

# Example: training SkeletonGCL with 2s-AGCN on NTU RGB+D 120 cross-subject with joint modality on GPU 0
python main.py --config config/nturgbd120-cross-subject/agcn.yaml --work-dir work_dir/ntu120/csub/agcn_joint --device 0
  • To train model on NTU RGB+D 60/120 with bone or motion modalities, setting bone or vel arguments in the config file default.yaml or in the command line.
# Example: training SkeletonGCL with CTRGCN on NTU RGB+D 120 cross subject under bone modality
python main.py --config config/nturgbd120-cross-subject/ctr.yaml --train_feeder_args bone=True --test_feeder_args bone=True --work-dir work_dir/ntu120/csub/ctrgcn_bone --device 0
  • To train your own model, put model file your_model.py under ./model and run:
# Example: training your own model on NTU RGB+D 120 cross subject
python main.py --config config/nturgbd120-cross-subject/xxx.yaml --model model.your_model.Model --work-dir work_dir/ntu120/csub/your_model --device 0

Testing

  • To test the trained models saved in <work_dir>, run the following command:
python main.py --config <work_dir>/config.yaml --work-dir <work_dir> --phase test --save-score True --weights <work_dir>/xxx.pt --device 0
  • To ensemble the results of different modalities, run
# Example: ensemble four modalities of CTRGCN on NTU RGB+D 120 cross subject
python ensemble.py --dataset ntu120/xsub \
--joint-dir work_dir/ntu120/xsub/ctrgcn_SkeletonGCL_joint \
--bone-dir work_dir/ntu120/xsub/ctrgcn_SkeletonGCL_bone \
--joint-motion-dir work_dir/ntu120/xsub/ctrgcn_SkeletonGCL_joint_motion \
--bone-motion-dir work_dir/ntu120/xsub/ctrgcn_SkeletonGCL_bone_motion

Acknowledgements

This repo is based on CTR-GCN. The data processing is borrowed from SGN and HCN.

Thanks to the original authors for their work!

Citation

Please cite this work if you find it useful:.

  @article{huang2023graph,
    title={Graph contrastive learning for skeleton-based action recognition},
    author={Huang, Xiaohu and Zhou, Hao and Feng, Bin and Wang, Xinggang and Liu, Wenyu and Wang, Jian and Feng, Haocheng and Han, Junyu and Ding, Errui and Wang, Jingdong},
    conference={The International Conference on Learning Representations (ICLR)},
    year={2023}
  }

Contact

For any questions, feel free to contact: [email protected]