Awesome

CIRL

This repo provides a demo for the CVPR 2022 paper "Causality Inspired Representation Learning for Domain Generalization" on the PACS dataset.

Requirements

• Python 3.6
• Pytorch 1.1.0

Training from scratch

Please first download the PACS dataset from http://www.eecs.qmul.ac.uk/~dl307/project_iccv2017 or from https://pan.baidu.com/s/1KxMA6SiQX1jdRxwkeKMqOw (password：pacs). Then update the files with suffix _train.txt and _val.txt in data/datalists for each domain, following styles below:

/home/user/data/images/PACS/kfold/art_painting/dog/pic_001.jpg 0
/home/user/data/images/PACS/kfold/art_painting/dog/pic_002.jpg 0
/home/user/data/images/PACS/kfold/art_painting/dog/pic_003.jpg 0
...

Please make sure you are using the official train-val-split. Once the data is prepared, then remember to update the path of train&val files and output logs in shell_train.py:

input_dir = 'path/to/train/files'
output_dir = 'path/to/output/logs'

Then running the code:

python shell_train.py -d=art_painting

Use the argument -d to specify the held-out target domain.

Evaluation

After training the model, firstly create directory ckpt/ and drag your model under it. For running the evaluation code, please update the files with suffix _test.txt in data/datalists for each domain, following the same styles as the train/val files above.

Then update the path of test files and output logs in shell_test.py:

input_dir = 'path/to/test/files'
output_dir = 'path/to/output/logs'

then simply run:

 python shell_test.py -d=art_painting

You can use the argument -d to specify the held-out target domain.

Acknowledgements

Some codes are adapted from FACT. We thank them for their excellent projects.

Contact

If you have any problem about our code, feel free to contact fangruilv@bit.edu.cn or describe your problem in Issues.