This is a community implementation for the paper EcoTTA.

Reference code:
pytorch-cifar100
EATA
TENT
Folder Robustbench cloned from Robustbench
Folder autoattack cloned from Auto-attack

Dataset: CIFAR10, CIFAR-100
Model: WideResNet28, WideResNet40

Usage

For experiment on CIFAR-100 and WideResNet40

Warmup Phase:

python main.py --dataset cifar100 --net wideresnet40 --b 64 --mode pretrain \
--warm 1 --lr 5e-2 --warmup_epoch 10 \
--checkpoint_path [path to save checkpoints] --log_dir_pretrain [path to save tensorboard logs]

The checkpoint and txt log file will be saved in ./[checkpoint_path]/*. The tensorboard log will be saved in ./[log_dir_pretrain]/*.

The required robust model will be automatically downloaded at robust_models folder.

Adaptation Phase:

The corruption config is defined in conf.py. By default you don't have to change it.
To experiment on specific corruptions or severity, you can change the configures in conf.py accordingly.

python main.py --dataset cifar100 --net wideresnet40 --b 64 --mode tta \
--warmup_checkpoint [your warmup checkpoint path] \
--lr_tta 5e-3 --e_margin 0.4 --lambda_reg 0.25 --log_dir_tta [log directory for tensorboard logs]

The txt log for adaptation phase will be saved at the same folder of your warmup checkpoint.

Experiment Result

CIFAR-100 WideResNet40
mean error for adaptation is 0.36481.

Other results to be updated.

Citation:

@inproceedings{song2023ecotta,
  title={EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization},
  author={Junha Song and Jungsoo Lee and In So Kweon and Sungha Choi},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2023}
}

Thank the authors for their marvelous job!
I hope that I've faithfully reproduced their work. Other results (ResNet50 ImageNet, combination with adaptBN, etc.) will be updated when I'm not so busy. If there is any problem, welcome to raise issues.