ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization

Requirements

• Python == 3.7.3
• torch >= 1.8
• Cuda == 10.1
• Torchvision == 0.4.2
• timm == 0.4.12

DataSets

Please download PACS dataset from here. Make sure you use the official train/val/test split in PACS paper. Take /data/DataSets/ as the saved directory for example:

images -> /data/DataSets/PACS/kfold/art_painting/dog/pic_001.jpg, ...
splits -> /data/DataSets/PACS/pacs_label/art_painting_crossval_kfold.txt, ...

Then set the "data_root" as "/data/DataSets/" and "data" as "PACS" in "main_gfnet.py".

You can directly set the "data_root" and "data" in "ALOFT-S.sh"/"ALOFT-S.sh" for training the model.

Evaluation

To evaluate the performance of the models, you can download the models trained on PACS as below:

Methods	Acc (%)	models
Strong Baseline	87.76	download
ALOFT-S	90.88	download
ALOFT-E	91.58	download

Please set the --eval = 1 and --resume as the saved path of the downloaded models. e.g., /trained/model/path/photo/checkpoint.pth. Then you can simple run:

python main_gfnet.py --target $domain --data 'PACS' --device $device --eval 1 --resume '/trained/model/path/photo/checkpoint.pth'

Training

Firstly download the GFNet-H-Ti model pretrained on ImageNet from here and save it to /pretrained_model. To run ALOFT-E, you could run the following code. Please set the --data_root argument needs to be changed according to your folder.

bash ALOFT-E.sh

You can also train the ALOFT-S model by running the following code:

base ALOFT-S.sh

Acknowledgement

Part of our code is borrowed from the following repositories.

• GFNet: "Global Filter Networks for Image Classification", NeurIPS 2021
• DSU: "Uncertainty modeling for out-of-distribution generalization", ICLR 2022

We thank to the authors for releasing their codes. Please also consider citing their works.