AS-CAL

Introduction

This is the official implementation of "Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition".

Requirements

• Python 3.6
• Pytorch 1.0.1

Datasets

• NTU RGB+D 60:
  Download raw data from https://github.com/shahroudy/NTURGB-D
  Use st-gcn/tools/ntu_gendata.py in https://github.com/yysijie/st-gcn to prepare data
• NTU RGB+D 120:
  Same as NTU RGB+D 60 but needs some modification for NTU RGB+D 120.
• SBU, UWA3D, N-UCLA
  Unzip the .zip file in /data and put them into the directory corresponding to the one in codes.

Usage

• pretrain and then linear evaluation:
  python pretrain_and_linEval.py

• reload pre-trained models and linear evaluation:
  python linEval.py --mode eval --model_path ./pretrained_model.pth

• supervised:
  python linEval.py --mode supervise

• reload pre-trained models and semi-supervised:
  python linEval.py --mode semi --model_path ./pretrained_model.pth

For more customized parameter settings, you can change them in parse_option() and/or parse_option_lin_eval()

License

AS-CAL is released under the MIT License.