# 2s-AGCN

**Repository Path**: ideaoverflow/2s-AGCN

## Basic Information

- **Project Name**: 2s-AGCN
- **Description**: Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-09-29
- **Last Updated**: 2024-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 2s-AGCN
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19

# Note

PyTorch version should be 0.3! For PyTorch0.4 or higher, the codes need to be modified. 

# Data Preparation

 - Download the raw data from [NTU-RGB+D](https://github.com/shahroudy/NTURGB-D) and [Skeleton-Kinetics](https://github.com/yysijie/st-gcn). Then put them under the data directory:
 
        -data\  
          -kinetics_raw\  
            -kinetics_train\
              ...
            -kinetics_val\
              ...
            -kinetics_train_label.json
            -keintics_val_label.json
          -nturgbd_raw\  
            -nturgb+d_skeletons\
              ...
            -samples_with_missing_skeletons.txt
            

[https://github.com/shahroudy/NTURGB-D]: NTU-RGB+D
[https://github.com/yysijie/st-gcn]: Skeleton-Kinetics

 - 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`
     
# Training & Testing

Change the config file depending on what you want.


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

    `python main.py --config ./config/nturgbd-cross-view/train_bone.yaml`
To ensemble the results of joints and bones, run test firstly to generate the scores of the softmax layer. 

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

    `python main.py --config ./config/nturgbd-cross-view/test_bone.yaml`

Then combine the generated scores with: 

    `python ensemble.py` --datasets ntu/xview
     
# Citation
Please cite the following paper if you use this repository in your reseach.

    @inproceedings{2sagcn2019cvpr,  
      title     = {Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition},  
      author    = {Lei Shi and Yifan Zhang and Jian Cheng and Hanqing Lu},  
      booktitle = {CVPR},  
      year      = {2019},  
    }
    
# Contact
For any questions, feel free to contact: `lei.shi@nlpr.ia.ac.cn`