Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization

This codebase is the official implementation of Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization (NeurIPS2021, Spotlight, url. This codebase is mainly based on DomainBed, with following modifications:

enable to use various backbone networks including Big Transfer (BiT), Vision Transformers (ViT, DeiT, HViT), and MLP-Mixer.
enable to test test-time adaptation method (T3A and Tent).

Installation

CUDA/Python

git clone [email protected]:matsuolab/Domainbed_contrib.git
cd Domainbed_contrib/docker
docker build -t {image_name} .
docker run -it -h `hostname` --runtime=nvidia -v /path/to/Domainbed_contrib /path/to/anyware --shm-size=40gb --name {container_name} {image_name}

Python libralies

We use pipenv for package management.

cd /path/to/Domainbed_contrib
pip install pipenv
pipenv install
pipenv shell
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cu102.html

Quick start

(1) Downlload the datasets

python -m domainbed.scripts.download --data_dir=/my/datasets/path --dataset pacs

Note: change --dataset pacs for downloading other datasets (e.g., vlcs, office_home, terra_incognita).

(2) Train a model on source domains

python -m domainbed.scripts.train\
       --data_dir /my/datasets/path\
       --output_dir /my/pretrain/path\
       --algorithm ERM\
       --dataset PACS\
       --hparams "{\"backbone\": \"resnet50\"}"

This scripts will produce new directory /my/pretrain/path, which include the full training log.

Note: change --dataset PACS for training on other datasets (e.g., VLCS, OfficeHome, TerraIncognita).

Note: change --hparams "{\"backbone\": \"resnet50\"}" for using other backbones (e.g., resnet18, ViT-B16, HViT).

(3) Evaluate model with test time adaptation (Table 1, Table 2, Figure 2)

python -m domainbed.scripts.unsupervised_adaptation\
       --input_dir=/my/pretrain/path\
       --adapt_algorithm=T3A

This scripts will produce a new file in /my/pretrain/path, whose name is results_{adapt_algorithm}.jsonl.

Note: change --adapt_algorithm=T3A for using other test time adaptation methods (T3A, Tent, or TentClf).

(4) Evaluate model with fine-tuning classifier(Figure 1)

python -m domainbed.scripts.supervised_adaptation\
       --input_dir=/my/pretrain/path\
       --ft_mode=clf

This scripts will produce a new file in /my/pretrain/path, whose name is results_{ft_mode}.jsonl.

Available backbones

resnet18
resnet50
BiT-M-R50x3
BiT-M-R101x3
BiT-M-R152x2
ViT-B16
ViT-L16
DeiT
Hybrid ViT (HViT)
MLP-Mixer (Mixer-L16)

Reproducing results

Table 1 and Figure 2 (Tuned ERM and CORAL)

You can use scripts/hparam_search.sh. Specifically, for each dataset and base algorithm, you can just type a following command.

sh scripts/hparam_search.sh resnet50 PACS ERM

Note that, it automatically starts 240 jobs, and take many times to finish.

Table 2 and Figure 1 (ERM with various backbone)

You can use scripts/launch.sh. Specifically, for each backbone, you can just type following three commands.

sh scripts/launch.sh pretrain resnet50 10 3 local
sh scripts/launch.sh sup resnet50 10 3 local
sh scripts/launch.sh unsup resnet50 10 3 local

Other results

For table 1, we used scores reported by In Search of Lost Domain Generalization. Full results for the reported scores in LaTeX format available here. Note: We only used scores for VLCS, PACS, OfficeHome, and TerraIncognita. We used the resutls with IIDAccuracySelectionMethod.

License

This source code is released under the MIT license, included here.

eadstry / t3a Goto Github PK

t3a's Introduction