def attack(model, dataset, attack_alg='fgsm', eps=0.1, device='cuda'):
accuracy = Accuracy().to(device)
model = model.to(device)
model.eval()
for x, y in dataset:
x, y = x.to(device), y.to(device)
# if model.binary:
# x[x >= 0.5] = 1
# x[x < 0.5] = 0
x_fgsm = fast_gradient_method(model,
x,
eps,
np.inf,
clip_min=0,
clip_max=1)
_, y_pred_fgsm = model(x_fgsm).max(1)
accuracy(y_pred_fgsm, y)
acc = accuracy.compute()
print(acc)
def test_step(self, batch, batch_idx):
x, y = batch
x = x.view(x.size(0), -1)
y_hat = self(x)
acc = accuracy(y_hat, y)
return {'test_loss': F.cross_entropy(y_hat, y), 'acc': acc}