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https://github.com/yfzhang114/adanpc
This is an official PyTorch implementation of the ICML 2023 paper AdaNPC and SIGKDD paper DRM.
https://github.com/yfzhang114/adanpc
domain-generalization machine-learning robustness test-time-adaptation
Last synced: about 1 month ago
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This is an official PyTorch implementation of the ICML 2023 paper AdaNPC and SIGKDD paper DRM.
- Host: GitHub
- URL: https://github.com/yfzhang114/adanpc
- Owner: yfzhang114
- License: mit
- Created: 2023-04-26T01:12:07.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-04-16T08:31:54.000Z (8 months ago)
- Last Synced: 2024-04-16T11:34:00.502Z (8 months ago)
- Topics: domain-generalization, machine-learning, robustness, test-time-adaptation
- Language: Python
- Homepage:
- Size: 463 KB
- Stars: 67
- Watchers: 2
- Forks: 6
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation
This codebase is the official implementation of [`AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation`](https://arxiv.org/abs/2304.12566) (**ICML, 2023**), and [`Domain-Specific Risk Minimization for Out-of-Distribution Generalization
`](https://arxiv.org/abs/2208.08661) (**SIGKDD, 2023**)
This codebase is mainly based on [DomainBed](https://github.com/facebookresearch/DomainBed), and [T3A](https://github.com/matsuolab/T3A).
## Installation
## Quick start
#### (1) Downlload the datasets
```sh
python download.py --data_dir=/my/datasets/path
```
Note: set the target datasets in the download.py
#### (2) Train a model on source domains
```sh
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
```sh
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 (`AdaNPC`, `AdaNPCBN`, or `TentClf`).
#### (4) Evaluate model with fine-tuning classifier
```sh
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 for AdaNPC
#### 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 commands.
Specifically, for baselines based on ResNet-50 (PLClf, PLFull, SHOT, SHOTIM, T3A)
```
sh scripts/launch.sh pretrain resnet50 10 3 local ERM
sh scripts/launch.sh sup resnet50 10 3 local ERM
sh scripts/launch.sh unsup resnet50 10 3 local ERM
```
for baselines based on ResNet-50-BN (TentClf, TentNorm, TentFull)
```
sh scripts/launch.sh pretrain resnet50-BN 10 3 local ERM
sh scripts/launch.sh sup resnet50-BN 10 3 local ERM
sh scripts/launch.sh unsup resnet50-BN 10 3 local ERM
```
for baselines based on KNN training algorithm
```
sh scripts/launch.sh pretrain resnet50 10 3 local KNN
sh scripts/launch.sh sup resnet50 10 3 local AdaNPC
sh scripts/launch.sh unsup resnet50 10 3 local AdaNPC
```
## Reproducing results for DRM
```
sh scripts/launch.sh pretrain resnet50 10 3 local DRM
sh scripts/launch.sh sup resnet50 10 3 local DRM
sh scripts/launch.sh unsup resnet50 10 3 local DRM
```
## License
This source code is released under the MIT license, included [here](LICENSE).
### Citation
If you find this repo useful, please consider citing:
```
@inproceedings{zhang2023adanpc,
title={Adanpc: Exploring non-parametric classifier for test-time adaptation},
author={Zhang, Yifan and Wang, Xue and Jin, Kexin and Yuan, Kun and Zhang, Zhang and Wang, Liang and Jin, Rong and Tan, Tieniu},
booktitle={International Conference on Machine Learning},
pages={41647--41676},
year={2023},
organization={PMLR}
}
@misc{zhang2022domainspecific,
title={Domain-Specific Risk Minimization for Out-of-Distribution Generalization},
author={Yi-Fan Zhang and Jindong Wang and Jian Liang and Zhang Zhang and Baosheng Yu and Liang Wang and Dacheng Tao and Xing Xie},
year={2023},
archivePrefix={KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
primaryClass={cs.LG}
}
```