Adversarial Defenses for PyTorch
Project description
Adversarial-Defenses-PyTorch (Under Reconstruction)
[Torchdefenses] is a PyTorch library that provides adversarial defenses to obtain robust model against adversarial attacks. It contains PyTorch Lightning-like interface and functions that make it easier for PyTorch users to implement adversarial defenses.
How to use?
import torchdefenses as td
rmodel = td.RobModel(model, n_classes=10,
normalize={'mean':[0.4914, 0.4822, 0.4465], 'std':[0.2023, 0.1994, 0.2010]})
Easy training
import torchdefenses.trainer as tr
trainer = tr.Standard(rmodel)
trainer.record_rob(train_loader, val_loader, eps=0.3, alpha=0.1, steps=5, std=0.1)
trainer.fit(train_loader=train_loader, max_epoch=10, optimizer="SGD(lr=0.01)",
scheduler="Step([100, 105], 0.1)", scheduler_type="Epoch",
record_type="Epoch", save_type="Epoch",
save_path="./_temp/"+"sample", save_overwrite=True)
Recording, Saving and Visualizing
trainer.save_all("./_temp/"+"sample", overwrite=True)
trainer.rm.plot(title="A", xlabel="Epoch", ylabel="Accuracy",
figsize=(6, 4),
x_key='Epoch',
y_keys=['Clean(Tr)', 'FGSM(Tr)', 'PGD(Tr)', 'GN(Tr)',
'Clean(Val)', 'FGSM(Val)', 'PGD(Val)', 'GN(Val)'],
ylim=(-10, 110),
colors=['k', '#D81B60', '#1E88E5', '#004D40']*2,
labels=['Clean', 'FGSM', 'PGD', 'GN', '', '', '', ''],
linestyles=['-', '-', '-', '-', '--', '--', '--', '--'],
)
Easy evaluation
rmodel.eval_accuracy(test_loader)
rmodel.eval_rob_accuracy_pgd(test_loader, eps=1, alpha=0.1,
steps=10, random_start=True, restart_num=1)
Useful functions
from torchdefenses.utils import fix_randomness, fix_gpu
fix_randomness(0)
fix_gpu(0)
How to customize?
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