DualHead-Network

PyTorch implementation of "Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition" in ACM Multimedia 2021.

Dependencies

• Python >= 3.6
• PyTorch >= 1.2.0
• PyYAML, tqdm, tensorboardX

Data Preparation

Disk usage warning: after preprocessing, the total sizes of datasets are around 38GB, 77GB, 63GB for NTU RGB+D 60, NTU RGB+D 120, and Kinetics 400, respectively. The raw/intermediate sizes may be larger.

Download Datasets

There are 3 datasets to download:

• NTU RGB+D 60 Skeleton
• NTU RGB+D 120 Skeleton
• Kinetics 400 Skeleton

NTU RGB+D 60 and 120

1. Request dataset here: http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp

2. Download the skeleton-only datasets:

   • nturgbd_skeletons_s001_to_s017.zip (NTU RGB+D 60)
   • nturgbd_skeletons_s018_to_s032.zip (NTU RGB+D 120, on top of NTU RGB+D 60)
   • Total size should be 5.8GB + 4.5GB.

3. Download missing skeletons lookup files from the authors' GitHub repo:

   • NTU RGB+D 60 Missing Skeletons: wget https://raw.githubusercontent.com/shahroudy/NTURGB-D/master/Matlab/NTU_RGBD_samples_with_missing_skeletons.txt

   • NTU RGB+D 120 Missing Skeletons: wget https://raw.githubusercontent.com/shahroudy/NTURGB-D/master/Matlab/NTU_RGBD120_samples_with_missing_skeletons.txt

   • Remember to remove the first few lines of text in these files!

Kinetics Skeleton 400

1. Download dataset from ST-GCN repo: https://github.com/yysijie/st-gcn/blob/master/OLD_README.md#kinetics-skeleton
2. This might be useful if you want to wget the dataset from Google Drive

Data Preprocessing

Directory Structure

Put downloaded data into the following directory structure:

- data/
  - kinetics_raw/
    - kinetics_train/
      ...
    - kinetics_val/
      ...
    - kinetics_train_label.json
    - keintics_val_label.json
  - nturgbd_raw/
    - nturgb+d_skeletons/     # from `nturgbd_skeletons_s001_to_s017.zip`
      ...
    - nturgb+d_skeletons120/  # from `nturgbd_skeletons_s018_to_s032.zip`
      ...
    - NTU_RGBD_samples_with_missing_skeletons.txt
    - NTU_RGBD120_samples_with_missing_skeletons.txt

Generating Data

1. NTU RGB+D
   • cd data_gen
   • python3 ntu_gendata.py
   • python3 ntu120_gendata.py

2. Kinetics
   • python3 kinetics_gendata.py

3. Generate the bone data with:
   • python gen_bone_data.py --dataset ntu
   • python gen_bone_data.py --dataset ntu120
   • python gen_bone_data.py --dataset kinetics

4. Generate the motion data with:
   • python gen_motion_data.py --dataset ntu
   • python gen_motion_data.py --dataset ntu120
   • python gen_motion_data.py --dataset kinetics

Pretrained Models

To be released soon (so many files)

Training & Testing

• The general training template command:

CUDA_VISIBLE_DEVICES=0,1,2,3 python main_dualhead.py --config config/ntu-xsub/train_joint.yaml \
    --work-dir work_dir/ntu-xsub/train_joint \
    --base-lr 0.05 --device 0 1 2 3 \
    --step 40 60 80 \
    --batch-size 64 --forward-batch-size 64 --test-batch-size 64 \
    --num-epoch 300 \
    --eval-interval 1 --save-interval 1
    

The model is evaluated every --eval-interval iteration and saved every --save-interval iteration.

• Template for multi-stream fusion:

python ensemble.py
  --dataset <dataset to ensemble, e.g. ntu120/xsub>
  --joint-dir <work_dir of your test command for joint model>
  --bone-dir <work_dir of your test command for bone model>

Details are to be released.

• Use the corresponding config files from ./config to train/test different datasets

• Resume training from checkpoint

python3 main.py
  ...  # Same params as before
  --start-epoch <0 indexed epoch>
  --weights <weights in work_dir>
  --checkpoint <checkpoint in work_dir>

Notes

• Default hyper-parameters are stored in the config files; you can tune them & add extra training techniques to boost performance
• ...

Acknowledgements

This repo is based on

• 2s-AGCN
• ST-GCN
• MS-G3D

Thanks to the original authors for their work!

Citation

Please cite this work if you find it useful:

@inproceedings{chen2021dualhead,
title = {Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-Based Action Recognition},
author = {Chen, Tailin and Zhou, Desen and Wang, Jian and Wang, Shidong and Guan, Yu and He, Xuming and Ding, Errui},
booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
pages = {4334–4342},
year = {2021},
}