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https://github.com/ExplainableML/ACVC
Official PyTorch implementation of CVPRW 2022 paper "Attention Consistency on Visual Corruptions for Single-Source Domain Generalization"
https://github.com/ExplainableML/ACVC
computer-vision cvpr cvpr2022 cvprw cvprw2022 data-augmentation deep-learning domain-generalization l3d-ivu machine-learning pytorch single-source-domain-generalization
Last synced: 3 months ago
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Official PyTorch implementation of CVPRW 2022 paper "Attention Consistency on Visual Corruptions for Single-Source Domain Generalization"
- Host: GitHub
- URL: https://github.com/ExplainableML/ACVC
- Owner: ExplainableML
- License: mit
- Created: 2022-04-08T09:29:31.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-02-22T19:22:46.000Z (over 1 year ago)
- Last Synced: 2024-04-17T00:19:01.294Z (7 months ago)
- Topics: computer-vision, cvpr, cvpr2022, cvprw, cvprw2022, data-augmentation, deep-learning, domain-generalization, l3d-ivu, machine-learning, pytorch, single-source-domain-generalization
- Language: Python
- Homepage:
- Size: 2.24 MB
- Stars: 27
- Watchers: 7
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# ACVC
> [**Attention Consistency on Visual Corruptions for Single-Source Domain Generalization**](https://openaccess.thecvf.com/content/CVPR2022W/L3D-IVU/html/Cugu_Attention_Consistency_on_Visual_Corruptions_for_Single-Source_Domain_Generalization_CVPRW_2022_paper.html)
> [Ilke Cugu](https://cuguilke.github.io/),
> [Massimiliano Mancini](https://www.eml-unitue.de/people/massimiliano-mancini),
> [Yanbei Chen](https://www.eml-unitue.de/people/yanbei-chen),
> [Zeynep Akata](https://www.eml-unitue.de/people/zeynep-akata)
> *IEEE Computer Vision and Pattern Recognition Workshops (CVPRW), 2022*
The official PyTorch implementation of the **CVPR 2022, L3D-IVU Workshop** paper titled "Attention Consistency on Visual Corruptions for Single-Source Domain Generalization". This repository contains: (1) our single-source domain generalization benchmark that aims at generalizing from natural images to other domains such as paintings, cliparts and skethces, (2) our adaptation/version of well-known advanced data augmentation techniques in the literaure, and (3) our final model ACVC which fuses visual corruptions with an attention consistency loss.
## Dependencies
```
torch~=1.5.1+cu101
numpy~=1.19.5
torchvision~=0.6.1+cu101
Pillow~=8.3.1
matplotlib~=3.1.1
sklearn~=0.0
scikit-learn~=0.24.1
scipy~=1.6.1
imagecorruptions~=1.1.2
tqdm~=4.58.0
pycocotools~=2.0.0
```
- We also include a YAML script `acvc-pytorch.yml` that is prepared for an easy Anaconda environment setup.
- One can also use the `requirements.txt` if one knows one's craft.
## Training
Training is done via `run.py`. To get the up-to-date list of commands:
```shell
python run.py --help
```
We include a sample script `run_experiments.sh` for a quick start.
## Analysis
The benchmark results are prepared by `analysis/GeneralizationExpProcessor.py`, which outputs LaTeX tables of the cumulative results in a .tex file.
- For example:
```shell
python GeneralizationExpProcessor.py --path generalization.json --to_dir ./results --image_format pdf
```
- You can also run distributed experiments, and merge the results later on:
```shell
python GeneralizationExpProcessor.py --merge_logs generalization_gpu0.json generalization_gpu1.json
```
## Case Study: COCO benchmark
COCO benchmark is especially useful for further studies on ACVC since it includes segmentation masks per image.
Here are the steps to make it work:
1. For this benchmark you only need 10 classes:
```
airplane
bicycle
bus
car
horse
knife
motorcycle
skateboard
train
truck
```
2. Download COCO 2017 [trainset](http://images.cocodataset.org/zips/train2017.zip), [valset](images.cocodataset.org/zips/val2017.zip), and [annotations](http://images.cocodataset.org/annotations/annotations_trainval2017.zip)
3. Extract the annotations zip file into a folder named `COCO` inside your choice of `data_dir` (For example: `datasets/COCO`)
4. Extract train and val set zip files into a subfolder named `downloads` (For example: `datasets/COCO/downloads`)
5. Download [DomainNet (clean version)](https://ai.bu.edu/M3SDA/)
6. Create a new `DomainNet` folder next to your `COCO` folder
7. Extract each domain's zip file under its respective subfolder (For example: `datasets/DomainNet/clipart`)
8. Back to the project, use `--first_run` argument once while running the training script:
```shell
python run.py --loss CrossEntropy --epochs 1 --corruption_mode None --data_dir datasets --first_run --train_dataset COCO --test_datasets DomainNet:Real --print_config
```
9. If everything works fine, you will see `train2017` and `val2017` folders under `COCO`
10. Both folders must contain 10 subfolders that belong to shared classes between COCO and DomainNet
11. Now, try running ACVC as well:
```shell
python run.py --loss CrossEntropy AttentionConsistency --epochs 1 --corruption_mode acvc --data_dir datasets --train_dataset COCO --test_datasets DomainNet:Real --print_config
```
12. All good? Then, you are good to go with the COCO section of `run_experiments.sh` to run multiple experiments
13. That's it!
## Citation
If you use these codes in your research, please cite:
```bibtex
@InProceedings{Cugu_2022_CVPR,
author = {Cugu, Ilke and Mancini, Massimiliano and Chen, Yanbei and Akata, Zeynep},
title = {Attention Consistency on Visual Corruptions for Single-Source Domain Generalization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2022},
pages = {4165-4174}
}
```
## References
We indicate if a function or script is borrowed externally inside each file.
Specifically for visual corruption implementations we benefit from:
- The imagecorruptions library of [Autonomous Driving when Winter is Coming](https://github.com/bethgelab/imagecorruptions).
Consider citing this work as well if you use it in your project.