Official Pytorch Implementation of Adaptive Methods for Aggregated Domain Generalization

Xavier Thomas, Dhruv Mahajan, Alex Pentland, Abhimanyu Dubey

• num_clusters: Number of clusters

• pca_dim: Required Feature space dimension after the SVD + Truncation step

• offset: First Principal Eigenvector in the SVD + Truncation Step

• clust_epoch: Defines the clustering schedule

  • clust_epoch = 0: cluster every 0, 1, 2, 4, 8, 16, ... epochs
  • clust_epoch = k, k>0: cluster every k epochs

Download the datasets:

python3 -m domainbed.scripts.download \
       --data_dir=./domainbed/data

Train a model:

python3 -m domainbed.scripts.train\
       --data_dir=./domainbed/data/\
       --algorithm AdaClust\
       --dataset PACS\
       --test_env 3

More details at: https://github.com/facebookresearch/DomainBed

Run SWAD:

python3 train_all.py exp_name --dataset PACS --algorithm AdaClust --data_dir /my/datasets/path

More details at: https://github.com/khanrc/swad

Launch a sweep:

python -m domainbed.scripts.sweep launch\
       --data_dir=/my/datasets/path\
       --output_dir=/my/sweep/output/path\
       --command_launcher MyLauncher

Here, MyLauncher is your cluster's command launcher, as implemented in command_launchers.py. At the time of writing, the entire sweep trains tens of thousands of models (all algorithms x all datasets x 3 independent trials x 20 random hyper-parameter choices). You can pass arguments to make the sweep smaller:

python -m domainbed.scripts.sweep launch\
       --data_dir=/my/datasets/path\
       --output_dir=/my/sweep/output/path\
       --command_launcher MyLauncher\
       --algorithms ERM AdaClust\
       --datasets PACS VLCS\
       --n_hparams 5\
       --n_trials 1

Model selection criteria differ in what data is used to choose the best hyper-parameters for a given model:

• IIDAccuracySelectionMethod: A random subset from the data of the training domains.
• LeaveOneOutSelectionMethod: A random subset from the data of a held-out (not training, not testing) domain.
• OracleSelectionMethod: A random subset from the data of the test domain.

After all jobs have either succeeded or failed, you can delete the data from failed jobs with python -m domainbed.scripts.sweep delete_incomplete and then re-launch them by running python -m domainbed.scripts.sweep launch again. Specify the same command-line arguments in all calls to sweep as you did the first time; this is how the sweep script knows which jobs were launched originally.

To view the results of your sweep:

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

DomainBed includes some unit tests and end-to-end tests. While not exhaustive, but they are a good sanity-check. To run the tests:

python -m unittest discover

By default, this only runs tests which don't depend on a dataset directory. To run those tests as well:

DATA_DIR=/my/datasets/path python -m unittest discover

@misc{thomas2021adaptive,
      title={Adaptive Methods for Aggregated Domain Generalization}, 
      author={Xavier Thomas and Dhruv Mahajan and Alex Pentland and Abhimanyu Dubey},
      year={2021},
      eprint={2112.04766},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

This source code is released under the MIT license, included here.