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README.md

GCN-NAS

PyTorch Source code for "Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching", AAAI2020

Requirements

  • python packages
    • pytorch = 0.4.1
    • torchvision>=0.2.1

Data Preparation

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

  • Preprocess the data with

    python data_gen/ntu_gendata.py

    python data_gen/kinetics-gendata.py.

  • Generate the bone data with:

    python data_gen/gen_bone_data.py

Model Training

  • Here, you can train the model searched by our method.

  • Configure the config file for different settings. For example, training model under the corss-view protocal:

    python main.py --config ./config/nturgbd-cross-view/train_joint.yaml

Model Evaluation

  • Change the config file for corresponding dateset with its protocal.

    python main.py --config ./config/nturgbd-cross-view/test_joint.yaml

Model searching

  • Devide the training data into trainging set and searching parts, as it is for a general NAS.
  • Run the gcn_search.py with corresponding configuration.
  • Here, we search on the NTU RGB+D dataset under the cross-view evaluation.
python gcn_search.py --config ./config/nturgbd-cross-view/search_joint.yaml

Acknowledgement

BibTeX

@article{peng2020learning,
  title={Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching},
  author={Peng, Wei and Hong, Xiaopeng and Chen, Haoyu and Zhao, Guoying},
  journal={The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI},
  year={2020}
}

License

All materials in this repository are released under the Apache License 2.0.

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Source code for "Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching", AAAI2020

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