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DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action Recognition

Official PyTorch implementation of "DeGCN : Deformable Graph Convolutional Networks for Skeleton-Based Action Recognition"

Prerequisites

  • Python >= 3.6
  • PyTorch >= 1.1.0
  • PyYAML, tqdm, tensorboardX, h5py, sklearn, matplotlib, thop
  • Run pip install -e torchpack
  • Run pip install -e torchlight

Data Preparation

Download datasets.

There are 2 datasets to download:

  • NTU RGB+D 60 Skeleton
  • NTU RGB+D 120 Skeleton
  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.
# Example: training DeGCN on NTU RGB+D 120 cross subject with GPU 0
python main.py --config config/nturgbd120-cross-subject/default.yaml --work-dir work_dir/ntu120/csub/degcn --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 DeGCN on NTU RGB+D 120 cross subject under bone modality
python main.py --config config/nturgbd120-cross-subject/default.yaml --train-feeder-args bone=True --test-feeder-args bone=True --work-dir work_dir/ntu120/csub/degcn_bone --device 0
  • To train model the JBF stream, setting model arguments in the config file default.yaml or in the command line.
# Example: training DeGCN with the JBF stream on NTU RGB+D 120 cross subject
python main.py --config config/nturgbd120-cross-subject/default.yaml --model model.jbf.Model --work-dir work_dir/ntu120/csub/degcn_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/default.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 DeGCN on NTU RGB+D 120 cross subject
python ensemble.py --datasets ntu120/xsub --joint-dir work_dir/ntu120/xsub/degcn --bone-dir work_dir/ntu120/xsub/degcn_bone --joint-motion-dir work_dir/ntu120/xsub/degcn_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:.

  @inproceedings{,
    title={DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action Recognition},
    author={Woomin Myung, Nan Su, Jing-Hao Xue, Guijin Wang},
    journal={IEEE transactions on image processing (TIP)}
    year={2024}
  }