# 根据model_name, 切换要攻击的model
elif args.model_name != '':
model = get_model_for_defense(args.model_name).to(device)
else:
model = get_custom_model(args.model, args.model_path).to(device)
# 攻击任务：Change to your attack function here
# Here is a attack baseline: PGD attack
# model = nn.DataParallel(model, device_ids=[0, 1])
model = WrappedModel(model, args.subbatch_size)
attack = get_attacker(
args.attacker, args.step_size, args.epsilon,
args.perturb_steps
)
model.eval()
if args.dataset == 'cifar10':
test_loader = get_test_cifar(args.batch_size)
elif args.dataset == 'mnist':
test_loader = get_test_mnist(args.batch_size)
elif args.dataset == 'rand_mnist':
test_loader = get_test_mnist(1)
xs = [x for (x,), _ in test_loader]
ys = numpy.load("rand_mnist.npy")
ds = list(zip(xs, ys))
test_loader = DataLoader(ds, args.batch_size)
else:
test_loader = get_test_imagenet(args.batch_size)
if args.targeted == 'untargeted':
# non-targeted attack
natural_acc, robust_acc, distance = eval_model_with_attack(
model, test_loader, attack, args.epsilon, device, args.dataset)
print(
y_cpu = y.detach().cpu().numpy()
x, y = x.to(dev), y.to(dev)
opt.zero_grad()
logit_2 = cnn(x)
loss = F.cross_entropy(logit_2, y)
loss.backward()
losses.append(loss.item())
accs.append(
(logit_2.detach().cpu().numpy().argmax(-1) == y_cpu).mean())
prog.set_description("Epoch: %d, Loss: %.4f, Acc: %.4f" %
(epoch, numpy.mean(losses), numpy.mean(accs)))
opt.step()
torch.save(cnn, "exp_randperm_cifar_wrn28.pt")
opt = torch.optim.Adam(cnn.fc.parameters(), eps=1e-2)
dl = DataLoader(ds, 100, True)
test_dl = get_test_cifar(100)
for epoch in range(100):
prog = tqdm.tqdm(dl)
accs = []
losses = []
for x, y in prog:
y_cpu = y.detach().cpu().numpy()
x, y = x.to(dev), y.to(dev)
opt.zero_grad()
logit_2 = cnn(x)
loss = F.cross_entropy(logit_2, y)
loss.backward()
losses.append(loss.item())
accs.append(
(logit_2.detach().cpu().numpy().argmax(-1) == y_cpu).mean())
prog.set_description("Epoch: %d, Loss: %.4f, Acc: %.4f" %
# import vgg
plotlib.style.use('seaborn')
plotlib.rcParams['ps.useafm'] = True
fsize = 24
tsize = 28
parameters = {
'axes.labelsize': tsize,
'axes.titlesize': tsize,
'xtick.labelsize': fsize,
'ytick.labelsize': fsize,
'legend.fontsize': fsize
}
plotlib.rcParams.update(parameters)
test_loader = get_test_cifar(1)
model = get_model_for_attack('model2')
'''model = vgg.vgg13_bn()
ensemble = torch.load('vgg13bn_regm2.dat', map_location=torch.device('cpu'))
model.load_state_dict(ensemble)'''
model.eval()
for x, y in test_loader:
def func(cx):
logits = model(cx)
# labels = torch.eye(10).to(y.device)[y]
return torch.nn.functional.cross_entropy(logits, y)
def safe_jac(cx):
jac = torch.autograd.functional.jacobian(func, cx)
jac = torch.randn_like(jac) * 1e-7 + jac