Comments (3)
Tianlong-Chen
commented on July 20, 2024
the command in sh file is:
python main_free.py XXXX
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riceric22
commented on July 20, 2024
Hi @TianlongChenTAMU,
The files that you are looking for are in the utils folder. You can either move them out of the utils folder or add the utils folder to your path. Note that the entire structure of the ImageNet repository is a near direct clone of the free adversarial training repository, with minimal changes to perform FGSM training.
As for main_free.py, this is a result of me renaming the file to main_fast.py to differentiate it from the free training script in case people were working with both repositories, but I forgot to update the sh files. Sorry about that, thanks for bringing it up and I'll update the shell scripts!
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riceric22
commented on July 20, 2024
Closed with f0cb637
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Related Issues (20)
- About PGD evaluation HOT 1
- Why do we need to do clamp(delta, lower_limit - X, upper_limit - X)？ HOT 2
- Reproduce the results of Free adversarial training.
- invalid key "/xff" when loading model.
- When computing the perturbation, do we need to set model.eval()? HOT 1
- Parameter settings on CIFAR-100 HOT 1
- adversarial attack
- Reproduce the result of CIFAR-10 from the default setting HOT 2
- facing "nan" values during training the model HOT 1
- reproduce problem of imagenet on default set HOT 1
- Can't reproduce MNIST results using current codes HOT 2
- Inconsistent clamping behaviour between CIFAR and MNIST fgsm implementaitions HOT 1
- Parameters of training HOT 1
- Why not using clean samples during training? HOT 1
- Include python/pytorch version for MNIST reproducibility HOT 1
- Probable gradient accumulation bug in mnist_train.py HOT 1
- torch.where API in MNIST and CIFAR10, ImageNet configuration files HOT 1
- Overwrite of variable i in nested for loop
- Reproduce results HOT 1
- indices HOT 1
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