Pytorch Lightning Implementation of xREgoPose
How to setup:
- Setup Virtualenv on your home directory.
cd ~/
module load python/3.9
virtualenv --no-download torch
source torch/bin/activate
pip install --no-index --upgrade pip
- Install required dependencies for the repo
# First cd into the xREgoPose directory
# For example, I will have to " cd ~/projects/def-pfieguth/j97park/xREgoPose/
pip install -r requirements.txt
- train.py
- Main wrapper for training
- Options for training can be found inside the script
- Testing can be done after training is finished. Check the arguments.
- eval.py
- Given a trained checkpoint, run the evaluation only.
- net/
- Folder that contains all the different Pytorch Lightning classes of different methods.
- visualizations/
- Folder that contains the visualizations included in the main paper.
- results/
- Folder that contains the csv files of results.
- Legacy/
- Folder that contains the codes before migrating to pytorch lightning.
- dataset/
- Folder that contains the codes for processing data.
- data/
- Folder that contains information about mo2cap2 (in progress) and a config file for xREgoPose.
- base/
- Folder that contains base class for dataset, eval and transform.
- utils/
- Folder that contains codes for utility. (Logging, metrics, configuration of joints etc.)
- sbash_scripts/
- Example scripts to run on compute canada.