Skip to content

Latest commit

 

History

History
76 lines (63 loc) · 3.38 KB

README.md

File metadata and controls

76 lines (63 loc) · 3.38 KB
Raw

Test-time Adaptation via Conjugate Pseudo-Labels

This is a PyTorch implementation of Conjugate Pseudo-Labels, proposed in our NeurIPS 2022 paper:

Test-time Adaptation via Conjugate Pseudo-Labels.
Sachin Goyal*, Mingjie Sun*, Aditi Raghunanthan, J. Zico Kolter

For more details, please check out our paper.

We provide the source training code (PolyLoss) in cifar_source_train.py and imagenet_source_train.py. For ImageNet training, we follow the setup of pytorch ImageNet training. Put the source model in the saved_models/pretrained directory. Here we provide our PolyLoss with eps 6 model on CIFAR-10 as an example.

For test-time adaptation, specify the path in MODEL.CKPT_PATH argument in the yaml config file, for polyloss model, an extra parameter MODEL.EPS should be added. To use our conjugate pseudo-label loss, specify the optimizer parameter OPTIM.ADAPT as conjugate in the config file.

For dataloading, be sure to update the PATH to ImageNet-C/R in the get_imagenetc_loader and get_imagenetr_loader functions.

Meta Learning the Loss Function

For meta train a meta loss for test-time adaptation on CIFAR-10-C corruption:

python cifar10_meta_train.py --cfg cfgs/cifar.yaml

Evaluation on CIFAR-10/100-C datasets

Train a source classifier on the cifar dataset using the following command. Specify the source loss function, dataset and save path in cfgs/source_train.yaml file.

python cifar_source_train.py --cfg cfgs/source_train.yaml

Evaluation code for test-time adaptation on CIFAR-10/100-C. Specify the path of the pre-trained source classifier (MODEL.CKPT_PATH) and test-time-adaptation loss (OPTIM.ADAPT) in cfgs/cifar.yaml file.

python cifar_tta_test.py --cfg cfgs/cifar.yaml

For MEMO baseline:

python memo_cifar.py --cfg cfgs/cifar.yaml

Evaluation on ImageNet-C/R dataset

Train a source classifier on the ImageNet dataset with PolyLoss using the following command.

python imagenet_source_train.py --eps 6.0

Evaluation code for test-time adaptation on ImageNet-C/R.

python imagenet_tta_test.py --cfg cfgs/imagenetc.yaml

For MEMO baseline:

python memo_imagenet.py --cfg cfgs/imagenetc.yaml

Evaluation on SVHN --> MNIST Benchmark

Train a source classifier on the SVHN dataset using the following command. Specify the source loss function (for ex. polyloss) and save path in cfgs/source_train_svhn.yaml file.

python svhn_source_train.py --cfg cfgs/source_train_svhn.yaml

Evaluation code for test-time adaptation on SVHN --> MNIST.

python svhn_mnist_tta_test.py --cfg cfgs/digit.yaml

Credits

This code has been built upon the code accompanying the paper "Tent: Fully Test-time Adaptation by Entropy Minimization" at https://github.com/DequanWang/tent. We are grateful to authors for releasing their code.

License

This project is released under the MIT license. Please see the LICENSE file for more information.

Citation

If you find this repository helpful, please consider citing:

@Article{goyal2022conjugate,
  author  = {Goyal, Sachin and Sun, Mingjie and Raghunanthan, Aditi and Kolter, Zico},
  title   = {Test-time adaptation via conjugate pseudo-labels},
  journal = {Advances in Neural Information Processing Systems},
  year    = {2022},
}