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Source-free Domain Generalization

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# PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization

[![arXiv](https://img.shields.io/badge/arXiv-2409.14163-b31b1b.svg)](https://arxiv.org/abs/2409.14163)

Authors: Haoran Zhang, [Shuanghao Bai](https://baishuanghao.github.io/), [Wanqi Zhou](https://scholar.google.com/citations?user=3Q_3PR8AAAAJ&hl=zh-CN), [Jingwen Fu](https://scholar.google.com/citations?hl=zh-CN&user=2Cu9uMUAAAAJ), [Badong Chen](https://scholar.google.com/citations?user=mq6tPX4AAAAJ&hl=zh-CN&oi=ao).

This repo contains implementation of several soure-free domain generalization methods: zero-shot CLIP, [PromptStyler](https://arxiv.org/abs/2307.15199), [PromptTA (Ours)](https://arxiv.org/abs/2409.14163)

## Highlights

![main figure](https://raw.githubusercontent.com/zhanghr2001/PromptTA/main/model.jpg)
> **<p align="justify"> Abstract:** *Source-free domain generalization (SFDG) tackles the challenge of adapting models to unseen target domains without access to source domain data. 
To deal with this challenging task, recent advances in SFDG have primarily focused on leveraging the text modality of vision-language models such as CLIP. 
These methods involve developing a transferable linear classifier based on diverse style features extracted from the text and learned prompts or deriving domain-unified text representations from domain banks. 
However, both style features and domain banks have limitations in capturing comprehensive domain knowledge.
In this work, we propose Prompt-Driven Text Adapter (PromptTA) method, which is designed to better capture the distribution of style features and employ resampling to ensure thorough coverage of domain knowledge. 
To further leverage this rich domain information, we introduce a text adapter that learns from these style features for efficient domain information storage.
Extensive experiments conducted on four benchmark datasets demonstrate that PromptTA achieves state-of-the-art performance.* </p>

<details>
  
<summary>Main Contributions</summary>

1) We propose PromptTA, a novel adapter-based framework for SFDG that incorporates a text adapter to effectively leverage rich domain information.
2) We introduce style feature resampling that ensures comprehensive coverage of textual domain knowledge.
3) Extensive experiments demonstrate that our PromptTA achieves the state of the art on DG benchmarks.
   
</details>

## Installation 
For installation and other package requirements, please follow the instructions as follows. 
This codebase is tested on Ubuntu 20.04 LTS with python 3.7. Follow the below steps to create environment and install dependencies.

* Setup conda environment.
```bash
# Create a conda environment
conda create -y -n prompt_ta python=3.7

# Activate the environment
conda activate prompt_ta

# Install torch (requires version >= 1.8.1) and torchvision
# Please refer to https://pytorch.org/get-started/previous-versions/ if your cuda version is different
conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch
```

* Install dassl library.
```bash
# Instructions borrowed from https://github.com/KaiyangZhou/Dassl.pytorch#installation

# Clone this repo
git clone https://github.com/KaiyangZhou/Dassl.pytorch.git
cd Dassl.pytorch

# Install dependencies
pip install -r requirements.txt

# Install dassl library (no need to re-build if the source code is modified)
python setup.py develop
cd ..
```

* Install clip library.
```bash
# Dependencies, may have been installed in former steps
pip install ftfy regex tqdm

# Install clip library from git
pip install git+https://github.com/openai/CLIP.git
```

* Clone PromptTA code repository and install requirements.
```bash
# Clone PromptTA code base
git clone https://github.com/zhanghr2001/PromptTA.git
cd PromptTA

# Install requirements
pip install -r requirements.txt
```

## Data Preparation
Download datasets:
- [PACS](https://drive.google.com/file/d/1m4X4fROCCXMO0lRLrr6Zz9Vb3974NWhE/view)
- [VLCS](https://drive.google.com/file/d/1r0WL5DDqKfSPp9E3tRENwHaXNs1olLZd/view)
- [OfficeHome](https://drive.google.com/file/d/1gkbf_KaxoBws-GWT3XIPZ7BnkqbAxIFa/view)
- [DomainNet (VisDA 2019)](http://ai.bu.edu/DomainNet/)

For PACS, VLCS and OfficeHome, unzip the files with the original folder names (pacs, VLCS, office_home_dg). For DomainNet, put the extracted image folders and train/test splits in the following structure or you can modify the configuration in [datasets](datasets/) to fit your own file structure.
```
your_directory
├─domainnet
│  ├─images
│  │  ├─clipart
│  │  └─infograph
│  └─splits
│     ├─clipart_train.txt
│     ├─clipart_test.txt
│     ├─infograph_train.txt
│     └─infograph_test.txt
├─office_home_dg
├─pacs
└─VLCS
```

## Training and Evaluation
Scripts for training and evaluation are in [scripts folder](scripts/). Modify *DATA* to your dataset directory before running.

```bash
# Example: train and evaluate on PACS dataset, with backbone ResNet-50 and GPU 0
bash scripts/prompt_ta/main_ta_all.sh pacs b128_ep50_pacs RN50 0
```

## Citation
If our code is helpful to your research or projects, please consider citing:
```bibtex
@misc{zhang2024prompttapromptdriventextadapter,
      title={PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization}, 
      author={Haoran Zhang and Shuanghao Bai and Wanqi Zhou and Jingwen Fu and Badong Chen},
      year={2024},
      eprint={2409.14163},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2409.14163}, 
}
```

## Acknowledgements

Our style of readme refers to [PDA](https://github.com/BaiShuanghao/Prompt-based-Distribution-Alignment). 
And our code is based on [CoOp](https://github.com/KaiyangZhou/CoOp) and [PromptStyler](https://arxiv.org/abs/2307.15199). We thank the authors for their great work.

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