FRET: Feature Redundancy Elimination for Test Time Adaptation

__This repo is officical PyTorch implement of 'Feature Redundancy Elimination for Test Time Adaptation' __
This codebase is mainly based on TSD and AETTA.

Dependence

We use python==3.8.13, other packages including:

torch==1.12.0+cu113
torchvision==0.13.0+cu113
numpy==1.24.4
pandas==2.0.3
tqdm==4.66.2
timm==0.9.16
scikit-learn==1.3.2 
pillow==10.3.0

We also share our python environment that contains all required python packages. Please refer to the ./FRET.yml file.
You can import our environment using conda:

conda env create -f FRET.yml -n FRET

Dataset

Download PACS and OfficeHome datasets used in our paper from:
PACS
OfficeHome
Download them from the above links, and organize them as follows.

|-your_data_dir
  |-PACS
    |-art_painting
    |-cartoon
    |-photo
    |-sketch
  |-OfficeHome
    |-Art
    |-Clipart
    |-Product
    |-RealWorld

To download the CIFAR10/CIFAR10-C and CIFAR100/CIFAR100-C datasets ,run the following commands:

$. download_cifar10c.sh        #download CIFAR10/CIFAR10-C datasets
$. download_cifar100c.sh       #download CIFAR100/CIFAR100-C datasets

Train source model

Please use train.py to train the source model. For example:

cd code/
python train.py --dataset PACS \
                --data_dir your_data_dir \
                --opt_type Adam \
                --lr 5e-5 \
                --max_epoch 50 \
                --net resnet18 \
                --test_envs 0  \

Change --dataset PACS for other datasets, such as office-home, CIFAR-10, CIFAR-100.
Set --net to use different backbones, such as resnet50, ViT-B16.
Set --test_envs 0 to change the target domain.
For CIFAR-10 and CIFAR-100, there is no need to set the --data_dir and --test_envs .

Test time adaptation

For domain datasets such as PACS and OfficeHome, run the following code:

python unsupervise_adapt.py --dataset PACS \
                            --data_dir your_data_dir \
                            --adapt_alg FRET \ 
                            --pretrain_dir your_pretrain_model_dir \
                            --lr 1e-4 \
                            --net resnet18 \
                            --test_envs 0

For corrupted datasets such as CIFAR10-C and CIFAR100-C, run the following code:

python unsupervise_adapt_corrupted.py --dataset CIFAR-10 \
                                      --data_dir your_data_dir \
                                      --adapt_alg FRET \ 
                                      --pretrain_dir your_pretrain_model_dir \
                                      --lr 1e-4 \
                                      --net resnet18

Change --adapt_alg FRET to use different methods of test time adaptation, e.g. TSD, BN, Tent.
--pretrain_dir denotes the path of source model, e.g. ./train_outputs/model.pkl.
For FRET, we have set default parameters in our code. For better results, you might consider adjusting the parameters --lam_FRET1, --lam_FRET2, --lam_FRET3, --filter_K, and --FRET_K. For guidance on selecting hyperparameters, please refer to our paper.

Tested Environment

We tested our code in the environment described below.

OS: Ubuntu 18.04.6 LTS
GPU: NVIDIA GeForce RTX 4090
GPU Driver Version: 535.129.03
CUDA Version: 12.2

youlj109 / fret