TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation, WACV 2023

updates (03/10/2022)

Add the environment requirements to reproduce the results.
Add the attention visualization code. An example is as follows where att_visual.txt contains image pathes:

python3 visualize.py --dataset office --name dw --num_classes 31 --image_path att_visual.txt --img_size 256

More details can be found in Attention Map Visualization

updates (03/08/2022)

Add the source-only code. An example on Office-31 dataset is as follows, where dslr is the source domain, webcam is the target domain:

python3 train.py --train_batch_size 64 --dataset office --name dw_source_only --train_list data/office/dslr_list.txt --test_list data/office/webcam_list.txt --num_classes 31 --model_type ViT-B_16 --pretrained_dir checkpoint/ViT-B_16.npz --num_steps 5000 --img_size 256

Environment (Python 3.8.12)

# Install Anaconda (https://docs.anaconda.com/anaconda/install/linux/)
wget https://repo.anaconda.com/archive/Anaconda3-2021.11-Linux-x86_64.sh
bash Anaconda3-2021.11-Linux-x86_64.sh

# Install required packages
conda install pytorch==1.8.1 torchvision==0.9.1 torchaudio==0.8.1 -c pytorch
pip install tqdm==4.50.2
pip install tensorboard==2.8.0
# apex 0.1
conda install -c conda-forge nvidia-apex
pip install scipy==1.5.2
pip install ml-collections==0.1.0
pip install scikit-learn==0.23.2

Pretrained ViT

Download the following models and put them in checkpoint/

ViT-B_16 (ImageNet-21K)
ViT-B_16 (ImageNet)

TVT with ViT-B_16 (ImageNet-21K) performs a little bit better than TVT with ViT-B_16 (ImageNet):

Datasets:

Download data and replace the current data/
Download images from Office-31, Office-Home, VisDA-2017 and put them under data/. For example, images of Office-31 should be located at data/office/domain_adaptation_images/

Training:

All commands can be found in script.txt. An example:

python3 main.py --train_batch_size 64 --dataset office --name wa \
--source_list data/office/webcam_list.txt --target_list data/office/amazon_list.txt \
--test_list data/office/amazon_list.txt --num_classes 31 --model_type ViT-B_16 \
--pretrained_dir checkpoint/ViT-B_16.npz --num_steps 5000 --img_size 256 \
--beta 0.1 --gamma 0.01 --use_im --theta 0.1

Attention Map Visualization:

python3 visualize.py --dataset office --name wa --num_classes 31 --image_path att_visual.txt --img_size 256

The code will automatically use the best model in wa to visualize the attention maps of images in att_visual.txt. att_visual.txt contains image pathes you want to visualize, for example:

/data/office/domain_adaptation_images/dslr/images/calculator/frame_0001.jpg 5
/data/office/domain_adaptation_images/dslr/images/calculator/frame_0002.jpg 5
/data/office/domain_adaptation_images/dslr/images/calculator/frame_0003.jpg 5
/data/office/domain_adaptation_images/dslr/images/calculator/frame_0004.jpg 5
/data/office/domain_adaptation_images/dslr/images/calculator/frame_0005.jpg 5

Citation:

@article{yang2021tvt,
  title={TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation},
  author={Yang, Jinyu and Liu, Jingjing and Xu, Ning and Huang, Junzhou},
  journal={arXiv preprint arXiv:2108.05988},
  year={2021}
}

Our code is largely borrowed from CDAN and ViT-pytorch

uta-smile/TVT