openpack_challenge

Pytorch implementation of paper A Spatial-Temporal Graph Convolutional Networks-based Approach for the OpenPack Challenge 2022. We won the 3rd place in the OpenPack challenge 2022.

Get started

Please follow the instructions in openpack-toolkit and openpack-torch

Data Preparation

Please download data from OpenPack dataset.

Training & Testing

Training and Testing

For skeleton data: Replace the path with your dataset path in 'config/ctr-gcn/configs/ctr-gcn.yaml'.

python main.py mode=train debug=false
python main.py mode=test debug=false

For sensors data: Replace the path with your dataset path in 'config/TCN/configs/TCN.yaml'.

python main_acc_boundary.py mode=train debug=false
python main_acc_boundary.py mode=test debug=false

(Please remove ['U0202', 'S0300'] in openpack_toolkit/configs/datasets/splits.py from test set if have mismatch error.)

Making a prediction file

python main.py mode=submission debug=false
python main_acc_boundary.py mode=submission debug=false

This will generate a '.pkl' file in 'v0.3.1/log/openpack-2d-kpt/your_issue_name/modality/save_scores'

• To ensemble the results of different modalities, replace the path with your dataset path in align.py and run:

python align.py

Final file will generate in results folder.

Pretrained Models

• Pretrained Models are avaliable at https://drive.google.com/file/d/1v2bpcJtRofwi1ovl82DHrQlrD08WohO-/view?usp=share_link.

Acknowledgements

This repo is based on openpack-toolkit and openpack-torch.

Thanks original authors for their work!