Forest-art/DRPT

This is the code for Disentangled and Recurrent Prompt Tunning.

Disentangled and Recurrent Prompt Tunning (DRPT)

Setup

conda create --name clip python=3.7
conda activate clip
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
pip3 install git+https://github.com/openai/CLIP.git

Alternatively, you can use pip install -r requirements.txt to install all the dependencies.

Download Dataset

We experiment with three datasets: MIT-States, UT-Zappos, and C-GQA.

sh download_data.sh

If you already have setup the datasets, you can use symlink and ensure the following paths exist: data/<dataset> where <datasets> = {'mit-states', 'ut-zappos', 'cgqa', 'clevr'}.

Training

python -u train.py --dataset <dataset>

Evaluation

python -u test.py --dataset <dataset>

You can replace --dataset with {mit-states, ut-zappos, cgqa, clevr}.

References

If you use this code, please cite

@article{lu2022decomposed,
  title={Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot Learning},
  author={Lu, Xiaocheng and Liu, Ziming and Guo, Song and Guo, Jingcai},
  journal={arXiv preprint arXiv:2211.10681},
  year={2022}
}