Initial commit

Author: Bai-YT

.gitignore

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+**/.DS_Store
+*/__pycache__/
+results/*
+base_models/*
+test.sh

LICENSE.txt

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+MIT License
+
+Copyright (c) 2024 Yatong Bai
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers
+
+This is the official code implementation of the preprint paper \
+*MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers* \
+by [Yatong Bai](https://bai-yt.github.io), [Mo Zhou](https://cdluminate.github.io), [Vishal M. Patel](https://engineering.jhu.edu/faculty/vishal-patel), and [Somayeh Sojoudi](https://www2.eecs.berkeley.edu/Faculty/Homepages/sojoudi.html).
+
+**TL;DR:** MixedNUTS balances clean data classification accuracy and adversarial robustness without additional training 
+via a mixed classifier with nonlinear base model logit transformations.
+
+
+<img src="main_figure.jpg" alt="MixedNUTS Results" title="Results" width="800"/>
+
+
+#### Citing our work (BibTeX)
+
+```bibtex
+@article{MixedNUTS,
+  title={MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers},
+  author={Bai, Yatong and Zhou, Mo and Patel, Vishal M. and Sojoudi, Somayeh},
+  year={2024}
+}
+```
+
+
+## Getting Started
+
+### Model Checkpoints
+
+All robust base classifiers are available on [RobustBench](https://robustbench.github.io).
+
+The ImageNet accurate base classifier is from 
+the [ConvNeXt-V2](https://github.com/facebookresearch/ConvNeXt-V2) repository and can be downloaded
+[here](https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt).
+
+The CIFAR-10 and -100 accurate base classifiers are fine-tuned from 
+[BiT](https://github.com/google-research/big_transfer) checkpoints and will be released soon.
+
+Create a `base_models` directory and organize as follows:
+```
+base_models
+│
+└───cifar10
+│   └───cifar10_std_rn152.pt
+│   
+└───cifar100
+    └───cifar100_std_rn152.pt
+│   
+└───imagenet
+    └───imagenet_std_convnext_v2-l_224.pt
+```
+
+### Environment
+
+Run the following to install the environment:
+```
+conda env create -f environment.yml
+```
+
+There are two additional pip packages that are not present in pypi and need to be manually installed via the following:
+
+```
+conda activate nlmc
+pip install git+https://github.com/fra31/auto-attack
+pip install git+https://github.com/RobustBench/robustbench.git
+```
+
+### Replicating Our Results
+
+CIFAR-10:
+```
+python run_robustbench.py --root_dir base_models --dataset_name cifar10 \
+    --rob_model_name Peng2023Robust --std_model_arch rn152 --map_type best \
+    --adaptive --n_examples 10000 --batch_size_per_gpu 40  --disable_nonlin_for_grad
+```
+
+CIFAR-100:
+```
+python run_robustbench.py --root_dir base_models --dataset_name cifar100 \
+    --rob_model_name Wang2023Better_WRN-70-16 --std_model_arch rn152 --map_type best \
+    --adaptive --n_examples 10000 --batch_size_per_gpu 40 --disable_nonlin_for_grad
+```
+
+ImageNet:
+```
+python run_robustbench.py --root_dir base_models --dataset_name imagenet \
+    --rob_model_name Liu2023Comprehensive_Swin-L --std_model_arch convnext_v2-l_224 --map_type best \
+    --adaptive --n_examples 5000 --batch_size_per_gpu 20 --disable_nonlin_for_grad
+```
+
+### Building MixedNUTS with Your Base Classifiers
+
+Please refer to `scripts.sh` for the workflow of constructing MixedNUTS.
+
+
+## Third-party Code
+
+`adaptive_autoattack.py`, `autopgd_base.py`, and `fab_pt.py` in `adaptive_autoattack` are modified based on [AutoAttack](https://github.com/fra31/auto-attack).
+
+`robust_bench.py` is modified based on [RobustBench](https://github.com/RobustBench/robustbench).

adaptive_autoattack/__init__.py

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+from .adaptive_autoattack import AdaptiveAutoAttack