This repository holds the codebase for the paper:
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition - Shujian Liao, Terry Lyons, Weixin Yang, Kevin Schlegel, and Hao Ni, BMVC 2021
We provide configureations for two datasets:
-Chalearn 2013 skeleton -NTU RGB+D 120 skeleton
- numpy
- signatory
- torch
- tqdm
Put downloaded data into the following directory structure:
- data/
- chalearn/
- 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
- NTU RGB+D 120
cd data_genpython3 ntu120_gendata.py
- To train a new GCN-LogsigRNN model run:
python3 main.py
--config <config file>
--work-dir <place to keep things (weights, checkpoints, logs)>
--device <GPU IDs to use>
- To test a trained model:
python3 main.py
--config <config file>
--work-dir <place to keep things>
--device <GPU IDs to use>
--weights <path to model weights>
-
Examples
- Train on Chalearn 2013
python3 main.py --config ./config/chalearn/train_joint.yaml
- Train on NTU 120 XSub Joint on device 0
python3 main.py --config ./config/ntu_sub/train_joint.yaml --device 0
- The model used is in
model/gcn_logsigRNN.py
- Train on Chalearn 2013
-
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>
We want to thank the authors of the following papers and repositories, their work formed the basis for this repository
Please cite this work if you find it useful.
@InProceedings{2021LogsigRNN,
title={Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition},
author={Liao, Shujian and Lyons, Terry and Yang, Weixin and Schlegel, Kevin and Ni, Hao},
booktitle={British Machine Vision Conference},
year={2021}
}