Awesome

Unsupervised Prompt Learning for Vision-Language Models

Introduction

This repo is the official implementation of Unsupervised Prompt Learning for Vision-Language Models. UPL is the first framework to introduce unsupervised learning into prompt learning of vision-language models to avoid time-consuming prompt engineering and better adapt vision-language models for the downstream image recognition task.

Fig.1 Overview of Unsupervised Prompt Learning (UPL) Framework.

</center>

Install

The code is built on the CoOp and Dassl with commit ac6e44194b2f90e325f477aadd6d9bc3a92ce255, so you need to install the dassl environment first. You can follow the instructions to install dassl as well as PyTorch. After that, run pip install -r requirements.txt under UPL/ to install a few more packages required by CLIP.

We also prepare all installation commands for you:


############ install Dassl ############

# Clone this repo
git clone https://github.com/KaiyangZhou/Dassl.pytorch.git
cd Dassl.pytorch/
git reset --hard ac6e44194b2f90e325f477aadd6d9bc3a92ce255

# Create a conda environment
conda create -n dassl python=3.7

# Activate the environment
conda activate dassl

# Install dependencies
pip install -r requirements.txt

# Install torch (version >= 1.7.1) and torchvision
conda install pytorch==1.8.1 torchvision==0.9.1 cudatoolkit=10.1 -c pytorch

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

############ install UPL ############

# Enter the directory at the same level as Dassl
cd ..

# Clone this repo
git clone https://github.com/tonyhuang2022/UPL.git
cd UPL/

# Install CLIP dependencies
pip install -r requirements.txt

######## attention ########
# We have two soft links, and you can redirect them!
# The `data` is linked to the datasets, and the `temp_analyze_results_miltiple` is linked to the `info`.
# We strongly recommend that you create these two paths on the disk which has enough space, and then use

rm data temp_analyze_results_miltiple # remove the existing file 
ln -s ${your_data_path} ./data
ln -s ${your_temp_analyze_results_miltiple_path} ./temp_analyze_results_miltiple

# Finished

Datasets

After that, you can follow the CoOp Datasets Instructions to prepare the datasets.

Then, you can run the code ~

Training

Note that we need to obtain the pseudo labels of the train sets, so you need to run the get info step under UPL/scripts before training to obtain the needed files (e.g., logits).

get info of RN50

CUDA_VISIBLE_DEVICES=0 bash get_info.sh sscaltech101 anay_rn50 end 16 -1 False

You can change the config file to get the info from other models (e.g., anay_rn101, anay_rn50x4). We also prepare a script for getting info from all CLIP released models.

CUDA_VISIBLE_DEVICES=0 bash get_all_info.sh sscaltech101

UPL train

After get info step, we can train the prompt (default run 16 seeds, you can change it in UPL/configs/UPLTrainer/rn50_ep50.yaml):

CUDA_VISIBLE_DEVICES=0 bash upl_train.sh sscaltech101 rn50_ep50 end 16 16 False True rn50_random_init

If you want to use UPL*, please change the PSEUDO_LABEL_MODELS in UPL/configs/UPLTrainer/rn50_ep50.yaml. Please ensure that you have obatined info from all released models. Then, you can run

CUDA_VISIBLE_DEVICES=0 bash upl_train.sh sscaltech101 rn50_ep50 end 16 16 False True multiple_models_random_init

Testing

Test with existing files after UPL training

bash upl_test_existing_logits.sh sscaltech101 rn50_ep50 end 16 16 False True

Thanks

We use code from CoOp and Dassl, which are great repositories and we encourage you to check them out and cite them in your work.