AIDGN

PyTorch Implementation for "Domain Generalization Through the Lens of Angular Invariance" (Our code is mainly based on DomainBed, Ishaan and David, 2021 library.)

Usage

1. Obtain dataset The datasets used in our experiments can be downloaded from the following links:

• PACS: https://drive.google.com/uc?id=1JFr8f805nMUelQWWmfnJR3y4_SYoN5Pd
• VLCS: https://drive.google.com/uc?id=1skwblH1_okBwxWxmRsp9_qi15hyPpxg8
• OfficeHome: https://drive.google.com/uc?id=1uY0pj7oFsjMxRwaD3Sxy0jgel0fsYXLC
• TerraIncognita: https://lilablobssc.blob.core.windows.net/caltechcameratraps/eccv_18_all_images_sm.tar.gz https://lilablobssc.blob.core.windows.net/caltechcameratraps/labels/caltech_camera_traps.json.zip

2. Train the model

python3 -m domainbed.scripts.train\
       --data_dir=./domainbed/data/OfficeHome/\
       --algorithm AIDGN\
       --dataset OfficeHome\
       --test_env 2

or launch a sweep

python -m domainbed.scripts.sweep launch\
       --data_dir=/my/datasets/path\
       --output_dir=/my/sweep/output/path\
       --command_launcher MyLauncher\
       --algorithms AIDGN\
       --datasets OfficeHome\
       --n_hparams 20\
       --n_trials 3 

3. To view the results of your sweep:

python -m domainbed.scripts.collect_results\
       --input_dir=/my/sweep/output/path

Requirements

• Python 3.7.9
• PyTorch 1.7.1
• torchvision 0.8.2
• Numpy 1.19.4