zhengzangw / DoPrompt

Official implementation of PCS in essay "Prompt Vision Transformer for Domain Generalization"

Prompt Vision Transformer for Domain Generalization (DoPrompt)

Pytorch implementation of DoPrompt (Prompt Vision Transformer for Domain Generalization)

Overview

Architecture of Network:

Training

Refer to DomainBed Readme for more details on commands running jobs. The training setting sweeps across multiple hyperparameters. Here we select some hyperparameters that can reach a good result. (Update 17/11/22: as many queries about the ERM baseline hyper-parameter, we present them below.)

# OfficeHome ERM
python -m domainbed.scripts.train --data_dir=./domainbed/data/ --steps 5001 --dataset OfficeHome --test_env 0/1/2/3 --algorithm ERM --output_dir results/exp \
     --hparams '{"lr": 1e-5, "lr_classifier": 1e-4}'
# OfficeHome
python -m domainbed.scripts.train --data_dir=./domainbed/data/ --steps 5001 --dataset OfficeHome --test_env 0/1/2/3 --algorithm DoPrompt --output_dir results/exp \
     --hparams '{"lr": 1e-5, "lr_classifier": 1e-3}'
# PACS ERM
python -m domainbed.scripts.train --data_dir=./domainbed/data/ --steps 5001 --dataset PACS --test_env 0/2/3 --algorithm ERM --output_dir results/exp \
     --hparams '{"lr": 5e-6, "lr_classifier": 5e-5}'
# PACS
python -m domainbed.scripts.train --data_dir=./domainbed/data/ --steps 5001 --dataset PACS --test_env 0/2/3 --algorithm DoPrompt --output_dir results/exp \
     --hparams '{"lr": 5e-6, "lr_classifier": 5e-5, "wd_classifier": 1e-5}'
# VLCS ERM
python -m domainbed.scripts.train --data_dir=./domainbed/data/ --steps 5001 --dataset VLCS --test_env 0/1/2/3 --algorithm ERM --output_dir results/exp \
     --hparams '{"lr": 5e-6, "lr_classifier": 5e-5}'
# VLCS
python -m domainbed.scripts.train --data_dir=./domainbed/data/ --steps 5001 --dataset VLCS --test_env 0/1/2/3 --algorithm DoPrompt --output_dir results/exp \
     --hparams '{"lr": 5e-6, "lr_classifier": 5e-6}'

Collect Results

python -m domainbed.scripts.collect_results --input_dir=results

Requirements

pip install -r domainbed/requirements.txt

Citation

@article{zheng2022prompt,
  title={Prompt Vision Transformer for Domain Generalization},
  author={Zheng, Zangwei and Yue, Xiangyu and Wang, Kai and You, Yang},
  journal={arXiv preprint arXiv:2208.08914},
  year={2022}
}

Acknowlegdement

This code is built on DomainBed. We thank the authors for sharing their codes.