This repository holds the codebase for the paper:

Motif-GCNs with Local and Non-Local Temporal Blocks for Skeleton-Based Action Recognition Yu-Hui Wen, Lin Gao, Hongbo Fu, Fang-Lue Zhang, Shihong Xia, Yong-Jin Liu, TPAMI 2022. [Early Access]

And this branch is the jittor version of the project

• Python3 (>3.5)
• jittor(just run pip install jittor)
• installed by pip install -r requirements.txt

git clone -b jittor https://github.com/wenyh1616/SAMotif-GCN.git 
cd SAMotif-GCN
cd torchlight; python setup.py install; cd ..

• Download the raw data from NTU-RGB+D and Skeleton-Kinetics.

• Preprocess the data with

  python tools/ntu_gendata.py

  python tools/kinetics-gendata.py.

• Generate the bone data with:

  python tools/gen_bone_data.py

To train a new model, run

python main.py recognition -c config/st_gcn/<dataset>/train.yaml

where the <dataset> can be ntu-xsub, ntu-xview or kinetics, depending on the dataset you want to use. The training results, including model weights, configurations and logging files, will be saved under the ./work_dir by default or <work folder> if you appoint it.

You can modify the training parameters such as work_dir, batch_size, step, base_lr and device in the command line or configuration files. The order of priority is: command line > config file > default parameter.

if you want to train it on multiple gpus,run

CUDA_VISIBLE_DEVICES="0,1" mpirun -np 2 python main_determine_sparse_intri.py recognition -c config/st_gcn/<dataset>/train.yaml

You can mofify the gpus in the CUDA_VISIBLE_DEVICES parameter. The "-np" parameter refers to the number of the gpus you want to train on.

Finally, custom model evaluation can be achieved by this command as we mentioned above:

python main.py recognition -c config/st_gcn/<dataset>/test.yaml --weights <path to model weights>

You can also run

CUDA_VISIBLE_DEVICES="0,1" mpirun -np 2 python main_determine_sparse_intri.py recognition -c config/st_gcn/<dataset>/test.yaml --weights <path to model weights>

to test model on multiple gpus.

Please cite the following paper if you use this repository in your reseach.

@article{wen2022motif,
  title={Motif-GCNs with Local and Non-Local Temporal Blocks for Skeleton-Based Action Recognition},
  author={Wen, Yu-Hui and Gao, Lin and Fu, Hongbo and Zhang, Fang-Lue and Xia, Shihong and Liu, Yong-Jin},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2022},
  publisher={IEEE}
}

For any question, feel free to contact

Yu-Hui Wen: [email protected]

The project is translated from pytorch version to jittor version by ChangSong Lei. If you have any quesion about the implementation in jittor version,feel free to contact

[email protected]