Code accompanying "Adaptive Methods for Aggregated Domain Generalization"

Overview

Adaptive Methods for Aggregated Domain Generalization (AdaClust)

Official Pytorch Implementation of Adaptive Methods for Aggregated Domain Generalization

Xavier Thomas, Dhruv Mahajan, Alex Pentland, Abhimanyu Dubey

PWC PWC PWC PWC PWC

AdaClust related hyperparameters

  • 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

Quick start

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

Available model selection criteria

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

Running unit tests

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

Citation

@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}
}

License

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

Owner
Xavier Thomas
Xavier Thomas
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