https://github.com/BIT-DA/CIRL
[CVPR 2022 Oral] Code release for "Causality Inspired Representation Learning for Domain Generalization"
https://github.com/BIT-DA/CIRL
Last synced: 15 days ago
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[CVPR 2022 Oral] Code release for "Causality Inspired Representation Learning for Domain Generalization"
- Host: GitHub
- URL: https://github.com/BIT-DA/CIRL
- Owner: BIT-DA
- Created: 2022-03-27T07:20:47.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-02-06T20:48:04.000Z (over 2 years ago)
- Last Synced: 2024-11-15T06:32:03.561Z (6 months ago)
- Language: Python
- Homepage:
- Size: 124 KB
- Stars: 128
- Watchers: 0
- Forks: 12
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
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README
## 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](https://github.com/MediaBrain-SJTU/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.