p.requires_grad = False
net.eval()
else:
for p in net.parameters():
p.requires_grad = False
net.eval()
for epsilon in epsilon_set:
train_dataset, val_dataset = utils.get_dataset(params)
loader_test = torch.utils.data.DataLoader(val_dataset,
batch_size=256, shuffle=False)
#test(net, loader_test)
if method == 'BayesWRM' or method == 'Bayes':
adversary = FGSMAttack(model_list, epsilon, is_train=False)
advacc = attack_over_test_data(model_list, adversary, param, loader_test)
else:
adversary = FGSMAttack(net, epsilon, is_train=False, advtraining=method)
advacc = attack_over_test_data(net, adversary, param, loader_test)
print('method',method, 'adv accuracy', advacc)
advacc_set.append(advacc)
df = pd.DataFrame(
{'epsilon': list(epsilon_set),
'advacc': advacc_set})
df.to_csv(os.path.join('trafficsignwhitebox', method+'_FGSMwhitebox.csv'))
# Data loaders
test_dataset = datasets.MNIST(root='../data/',
train=False,
download=True,
transform=transforms.ToTensor())
loader_test = torch.utils.data.DataLoader(test_dataset,
batch_size=param['test_batch_size'],
shuffle=False)
# Setup model to be attacked
net = LeNet5()
net.load_state_dict(torch.load('models/adv_trained_lenet5.pkl'))
if torch.cuda.is_available():
print('CUDA ensabled.')
net.cuda()
for p in net.parameters():
p.requires_grad = False
net.eval()
test(net, loader_test)
# Adversarial attack
adversary = FGSMAttack(net, param['epsilon'])
# adversary = LinfPGDAttack(net, random_start=False)
t0 = time()
attack_over_test_data(net, adversary, param, loader_test)
print('{}s eclipsed.'.format(time() - t0))
}
# Setup model to be attacked
net = models.resnet18(pretrained=False)
dim_in = net.fc.in_features
net.fc = nn.Linear(dim_in, 2)
num_epoch = 20
for epoch in range(num_epoch):
print('{}/{}'.format(epoch + 1, num_epoch))
print('-' * 10)
net.load_state_dict(torch.load('model_save/' + str(epoch) + '.pth'))
if torch.cuda.is_available():
print('CUDA ensabled.')
net.cuda()
for p in net.parameters():
p.requires_grad = False
net.eval()
test(net, dset_loaders['val'])
# Adversarial attack
adversary = FGSMAttack(net, param['epsilon'])
# adversary = LinfPGDAttack(net, random_start=False)
t0 = time()
attack_over_test_data(net, adversary, param, dset_loaders['val'])
print('{}s eclipsed.'.format(time() - t0))
print('Finish attacking!')
print()
