[NeurIPS-23] Pytorch Implementation of Revisiting Visual Model Robustness: A Frequency Long-Tailed Distribution View
-
If you have any questions about the paper or code, please contact us
- pytorch
- numpy
- autoattack
- The
Visualize.ipynbfile contains the model's under-fitting Behavior visualization and sensitivity analysis experimental code for high-frequency components. - We provide the data files required for visualization in the link, including input gradients under standard training and sampling results of the model loss space.
We provide two methods of using BaSS in the data preprocessing stage:
- for CIFAR10 or CIFAR100 dataset
train_set = torchvision.datasets.CIFAR10(root='/data/cifar', train=True, download=True)
test_set = torchvision.datasets.CIFAR10(root='/data/cifar', train=False, download=True)
print("CIFAR-B dataset :)")
train_set.data = get_BaSS_dataset(train_set.data.copy())
test_set.data = get_BaSS_dataset(test_set.data.copy())
We provide the algorithm details of the get_BaSS_dataset() function in the BaSS_cifar.py file
- for ImageNet dataset
TRAIN_TRANSFORMS_IMAGENET = transforms.Compose([
transforms.Resize((128,128)),
## applying BaSS
FreqLog(),
##
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(
brightness=0.1,
contrast=0.1,
saturation=0.1
),
transforms.ToTensor(),
Lighting(0.05, IMAGENET_PCA['eigval'],
IMAGENET_PCA['eigvec'])
])
TEST_TRANSFORMS_IMAGENET = transforms.Compose([
transforms.Resize((128,128)),
## applying BaSS
FreqLog(),
##
transforms.ToTensor(),
])
We provide the algorithm details of the FreqLog() function in the BaSS_imagegnet.py file
Our adversarial training framework is based on Bag of Tricks for Adversarial Training (ICLR 2021, code) and MART(ICLR 2020, code).
We only modified the data processing part based on the original code framework. For detailed code description, please refer to the readme file in the original repo. In order to train quickly, we edited the easy_train.py file to include the training script.
cd Bag-of-Tricks-for-AT
python easy_train.py
cd MART
python easy_train.py
We set the --f-train switch in the training script whether to use BaSS, for example
## standard training
script1 = 'CUDA_VISIBLE_DEVICES=0 python train.py --model resnet --dataset cifar10'
## training with BaSS
script2 = 'CUDA_VISIBLE_DEVICES=1 python train.py --model resnet --dataset cifar10 --f-train'
Our evaluation results are based on Clean acc, PGD(CE loss,CW loss),Autoattack.
cd Bag-of-Tricks-for-AT
python eval_cifar.py --out-dir 'path_to_the_model' --ATmethods 'TRADES' --f-test
cd MART
python test.py --dataset cifar10 --model resnet --f-test
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent