Efficient Graph Convolutional Network (EfficientGCN) (ResGCNv2.0)

1 Paper Details

Yi-Fan Song, Zhang Zhang, Caifeng Shan and Liang Wang. EfficientGCN: Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition. Submitted to IEEE T-PAMI, 2021. [Arxiv Preprint]

Previous version (ResGCN v1.0): [Github] [ACMMM 2020] [Arxiv Preprint]

The following pictures are the pipeline of EfficientGCN and the illustration of ST-JointAtt layer, respectively.

2 Prerequisites

2.1 Libraries

This code is based on Python3 (anaconda, >=3.5) and PyTorch (>=1.6.0).

Other Python libraries are presented in the 'scripts/requirements.txt', which can be installed by

pip install -r scripts/requirements.txt

2.2 Experimental Dataset

Our models are experimented on the NTU RGB+D 60 & 120 datasets, which can be downloaded from here.

There are 302 samples of NTU RGB+D 60 and 532 samples of NTU RGB+D 120 need to be ignored, which are shown in the 'src/preprocess/ignore.txt'.

2.3 Pretrained Models

Several pretrained models are provided, which include EfficientGCN-B0, EfficientGCN-B2, and EfficientGCN-B4 for the cross-subject (X-sub) and cross-view (X-view) benchmarks of the NTU RGB+D 60 dataset and the cross-subject (X-sub120) and cross-setup (X-set120) benchmarks of the NTU RGB+D 120 dataset.

These models can be downloaded from BaiduYun (Extraction code: 1c5x) or GoogleDrive.

3 Parameters

Before training and evaluating, there are some parameters should be noticed.

(1) '--config' or '-c': The config of EfficientGCN. You must use this parameter in the command line or the program will output an error. There are 12 configs given in the configs folder, which can be illustrated in the following tabel.

config	2001	2002	2003	2004
model	B0	B0	B0	B0
benchmark	X-sub	X-view	X-sub120	X-set120

config	2005	2006	2007	2008
model	B2	B2	B2	B2
benchmark	X-sub	X-view	X-sub120	X-set120

config	2009	2010	2011	2012
model	B4	B4	B4	B4
benchmark	X-sub	X-view	X-sub120	X-set120

(2) '--work_dir' or '-w': The path to workdir, for saving checkpoints and other running files. Default is './workdir'.
(3) '--pretrained_path' or '-pp': The path to the pretrained models. pretrained_path = None means using randomly initial model. Default is None.
(4) '--resume' or '-r': Resume from the recent checkpoint ('<--work_dir>/checkpoint.pth.tar').
(5) '--evaluate' or '-e': Only evaluate models. You can choose the evaluating model according to the instructions.
(6) '--extract' or '-ex': Extract features from a trained model for visualization. Using this parameter will make a data file named 'extraction_<--config>.npz' at the './visualization' folder.
(7) '--visualization' or '-v': Show the information and details of a trained model. You should extract features by using <--extract> parameter before visualizing.
(8) '--dataset' or '-d': Choose the dataset. (Choice: [ntu-xsub, ntu-xview, ntu-xsub120, ntu-xset120])
(9) '--model_type' or '-mt': Choose the model. (Format: EfficientGCN-B{coefficient}, e.g., EfficientGCN-B0, EfficientGCN-B2, EfficientGCN-B4)

Other parameters can be updated by modifying the corresponding config file in the 'configs' folder or using command line to send parameters to the model, and the parameter priority is command line > yaml config > default value.

4 Running

4.1 Modify Configs

Firstly, you should modify the 'path' parameters in all config files of the 'configs' folder.

A python file 'scripts/modify_configs.py' will help you to do this. You need only to change three parameters in this file to your path to NTU datasets.

python scripts/modify_configs.py --path <path/to/save/numpy/data> --ntu60_path <path/to/ntu60/dataset> --ntu120_path <path/to/ntu120/dataset> --pretrained_path <path/to/save/pretraiined/model> --work_dir <path/to/work/dir>

All the commands above are optional.

4.2 Generate Datasets

After modifing the path to datasets, please generate numpy datasets by using 'scripts/auto_gen_data.sh'.

bash scripts/auto_gen_data.sh

It may takes you about 2.5 hours, due to the preprocessing module for X-view benchmark (same as 2s-AGCN).

To save time, you can only generate numpy data for one benchmark by the following command (only the first time to use this benchmark).

python main.py -c <config> -gd

where <config> is the config file name in the 'configs' folder, e.g., 2001.

Note: only training the X-view benchmark requires preprocessing module.

4.3 Train

You can simply train the model by

python main.py -c <config>

If you want to restart training from the saved checkpoint last time, you can run

python main.py -c <config> -r

4.4 Evaluate

Before evaluating, you should ensure that the trained model corresponding the config is already existed in the <--pretrained_path> or '<--work_dir>' folder. Then run

python main.py -c <config> -e

4.5 Visualization

To visualize the details of the trained model, you can run

python main.py -c <config> -ex -v

where '-ex' can be removed if the data file 'extraction_<config>.npz' already exists in the './visualization' folder.

5 Results

Top-1 Accuracy for the provided models on NTU RGB+D 60 & 120 datasets.

models	FLOPs	parameters	NTU X-sub	NTU X-view	NTU X-sub120	NTU X-set120
EfficientGCN-B0	3.08G	0.32M	89.9%	94.7%	85.9%	84.3%
EfficientGCN-B2	6.12G	0.79M	90.9%	95.5%	87.9%	88.0%
EfficientGCN-B4	15.24G	2.03M	91.7%	95.7%	88.3%	89.1%

6 Citation and Contact

If you have any question, please send e-mail to yifan.song@cripac.ia.ac.cn.

Please cite our paper when you use this code in your reseach.

@article{song2021efficientgcn,
  author    = {Song, Yi-Fan and Zhang, Zhang and Shan, Caifeng and Wang, Liang},
  title     = {EfficientGCN: Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition},
  journal   = {arXiv:2106.15125},
  year      = {2021},
}

@inproceedings{song2020stronger,
  author    = {Song, Yi-Fan and Zhang, Zhang and Shan, Caifeng and Wang, Liang},
  title     = {Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-Based Action Recognition},
  booktitle = {Proceedings of the 28th ACM International Conference on Multimedia (ACMMM)},
  pages     = {1625--1633},
  year      = {2020},
  isbn      = {9781450379885},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY, USA},
  url       = {https://doi.org/10.1145/3394171.3413802},
  doi       = {10.1145/3394171.3413802},
}

zyxjtu/EfficientGCNv1