https://github.com/mueller-mp/SAM-ON

https://github.com/mueller-mp/SAM-ON

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• Host: GitHub
• URL: https://github.com/mueller-mp/SAM-ON
• Owner: mueller-mp
• Created: 2022-11-18T15:35:19.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-01-25T12:32:43.000Z (10 months ago)
• Last Synced: 2024-08-02T15:26:53.096Z (3 months ago)
• Language: Python
• Size: 1.13 MB
• Stars: 30
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# [Normalization Layers Are All That Sharpness Aware Minimization Needs](https://arxiv.org/abs/2306.04226)

**Maximilian Müller\*, Tiffany Vlaar\*, David Rolnick\*, Matthias Hein**

**NeurIPS 2023**

Paper: [https://arxiv.org/abs/2306.04226](https://arxiv.org/abs/2306.04226)  

![teaser.png](teaser.png)

# SAM-ON

This repository contains the SAM-ON optimizer for CIFAR data. SAM-ON performs the (A)SAM perturbation _only_ for the normalization parameters and achieves superior performance compared to its counterparts using all layers. The optimizer is adapted from the original ASAM optimizer (https://github.com/SamsungLabs/ASAM/)

### Dependencies

You can install the required packages via conda:

```

conda env create -f samon_env.yml

conda activate samon-env

```

In case this doesn't work for you, the required packages can also be found in `requirements.txt`.

### How to run the code

The code is set up to train a WRN28-10 on CIFAR100, and the desired options can be passed with flags:

```

  python train.py --dataset=CIFAR100 --data_path=/path/to/CIFAR100/ --minimizer=ASAM_ON --p=2 --elementwise --normalize_bias --autoaugment --rho=10. --only_norm

 ```

 Currently, SAM_ON, ASAM_ON, AdamW and SGD can be selected as optimizers. If ASAM_ON or SAM_ON is chosen and neither the only_norm nor the no_norm flag are set, the conventional (A)SAM optimizer is used.

### New Models

The available models are wrn28_10, resnet56, resnext vit_t, vit_s. Those can be chosen via the `--model` flag. If you would like to try SAM-ON on other models, you need to add them in `models.py`. Since for now the optimizer is selecting the normalization-layers by name, you need to make sure that _all_ normalization parameters contain the string 'norm' or 'bn' in their name, otherwise the optimizer does not recognize them. You can check this by calling model.named_parameters().

### Citations

```

@inproceedings{mueller2023normalization,

      title={Normalization Layers Are All That Sharpness-Aware Minimization Needs}, 

      author={Maximilian Mueller and Tiffany Vlaar and David Rolnick and Matthias Hein},

      year={2023},

      booktitle = {Advances in Neural Information Processing Systems},

}

```

### License