RobustNets benchmark models and code.
Code and model release for the NeurIPS 2022 paper:
Models Out of Line: A Fourier Lens on Distribution Shift Robustness
We recommend cloning this repository and running the data download script download_RobustNets.sh. Alternatively, you can manually download the RobustNets model state dicts here: https://github.com/sarafridov/RobustNets/releases.
After downloading the RobustNets dataset and code, running the program RobustNets.py will ensure you have downloaded the whole dataset and everything is working correctly. This program expects access to a GPU and takes about 10 minutes to run due to its computation of CIFAR-10 test accuracy for each model. You can skip the accuracy checks if they're too burdensome, but they do help confirm that we're working with the same data. Assuming you downloaded the RobustNets assets to the directory RobustNets and want to use the directory tempC to store torchvision's CIFAR-10 data, enter the following command:
python RobustNets.py --PATH_TO_RobustNets=RobustNets --PATH_TO_c10=tempC
RobustNets.py contains the function iterate_over_RobustNets, which shows how to iterate over the RobustNets models by creating the string identifier of each model. This program also contains check_RobustNets_c10_accuracy, which illustrates how to load these models given their identifiers. In particular, you must use the function instantiate_model, which takes the model identifier and the path to RobustNets as arguments: model = instantiate_model(model_string, PATH_TO_RobustNets).
All metrics applied in our paper to the RobustNets models are in the dictionary RobustNets/metric_and_OOD_var_dict.json, but you may want to recompute these metrics or compute them on other models. The following examples illustrate how to do this. To use a model that isn't the default model, you will have to specify that model via the args (see get_args in utilities.py). To use a model outside of the RobustNets dataset, you will have to modify the metric-computation programs to load your desired model rather than a RobustNets model. Finally, note that the interpolation programs create and save additional data at the specified PATH_TO_interp.
Compute Fourier interpolation metrics:
python fourier_interpolation.py --PATH_TO_RobustNets=RobustNets --PATH_TO_interp=tempI --PATH_TO_c10=tempC
Compute pixel interpolation metrics:
python pixel_interpolation.py --PATH_TO_RobustNets=RobustNets --PATH_TO_interp=tempI --PATH_TO_c10=tempC
Compute Jacobian norm:
python jacobian_norm.py --PATH_TO_RobustNets=RobustNets --PATH_TO_c10=tempC
@inproceedings{modelsoutofline,
title={Models Out of Line: A Fourier Lens on Distribution Shift Robustness},
author={Fridovich-Keil, Sara and Bartoldson, Brian R. and Diffenderfer, James and Kailkhura, Bhavya and Bremer, Peer-Timo},
year={2022},
booktitle={NeurIPS},
}