BIT-DA/CIRL

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 [email protected] or describe your problem in Issues.