def __init__(self,
args,
model,
nb_iter,
loss_fn=nn.CrossEntropyLoss(reduction="sum")):
super(PGDAttack, self).__init__(args, model, nb_iter, loss_fn)
self.args = args
self.model = model
if args.attack_ball == 'Linf':
self.adversary = LinfPGDAttack(self.model,
loss_fn=loss_fn,
eps=args.epsilon,
nb_iter=nb_iter,
eps_iter=0.01,
rand_init=True,
clip_min=args.clip_min,
clip_max=args.clip_max,
targeted=False)
elif args.attack_ball == 'L2':
self.adversary = L2PGDAttack(self.model,
loss_fn=loss_fn,
eps=args.epsilon,
nb_iter=nb_iter,
eps_iter=0.01,
rand_init=True,
clip_min=args.clip_min,
clip_max=args.clip_max,
targeted=False)
else:
raise NotImplementedError
def whitebox_pgd(args, model):
adversary = L2PGDAttack(model,
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps=0.3,
nb_iter=40,
eps_iter=0.01,
rand_init=True,
clip_min=-1.0,
clip_max=1.0,
targeted=False)
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
trainset = torchvision.datasets.CIFAR10(root='./data',
train=True,
download=True,
transform=transform_train)
train_loader = torch.utils.data.DataLoader(trainset,
batch_size=1,
shuffle=True,
num_workers=8)
train_itr = tqdm(enumerate(train_loader), total=len(train_loader.dataset))
correct = 0
for batch_idx, (data, target) in train_itr:
x, target = data.to(args.device), target.to(args.device)
adv_image = adversary.perturb(x, target)
pred = model(adv_image)
out = pred.max(
1, keepdim=True)[1] # get the index of the max log-probability
correct += out.eq(target.unsqueeze(1).data)
acc = 100. * correct.cpu().numpy() / len(train_loader.dataset)
print("PGD attack succes rate %f" % (acc))
def create_adv_input(self, x, y, model):
# Prepare copied model
model = copy.deepcopy(model)
# Prepare input and corresponding label
data = torch.from_numpy(np.expand_dims(x, axis=0).astype(np.float32))
target = torch.from_numpy(np.array([y]).astype(np.int64))
data.requires_grad = True
from advertorch.attacks import L2PGDAttack
adversary = L2PGDAttack(model.forward,
eps=self.eps,
nb_iter=self.nb_iter,
eps_iter=self.eps_iter)
perturbed_data = adversary.perturb(data, target)
# Have to be different
output = model.forward(perturbed_data)
final_pred = output.max(
1, keepdim=True)[1] # get the index of the max log-probability
if final_pred.item() == target.item():
return perturbed_data, 0
else:
return perturbed_data, 1
def init_pgdl2(self, model):
return L2PGDAttack(model,
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps=self.args['test_epsilon'] / 255.0,
nb_iter=self.spectral_args["test_nb_iter"],
eps_iter=self.spectral_args["test_eps_iter"],
rand_init=True,
clip_min=self.spectral_args['clip_min'],
clip_max=self.spectral_args['clip_max'],
targeted=False)
def validate(val_loader, model, criterion, args):
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
top5 = AverageMeter('Acc@5', ':6.2f')
progress = ProgressMeter(
len(val_loader),
[batch_time, losses, top1, top5],
prefix='Test: ')
# switch to evaluate mode
model.eval()
with torch.no_grad():
adversary = L2PGDAttack(
model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=14.2737,
nb_iter=20, eps_iter=1.784, rand_init=True, clip_min=-2.1179, clip_max=2.6400,
targeted=False)
end = time.time()
for i, (images, target) in enumerate(val_loader):
if args.gpu is not None:
images = images.cuda(args.gpu, non_blocking=True)
if torch.cuda.is_available():
target = target.cuda(args.gpu, non_blocking=True)
with torch.enable_grad():
adv_untargeted = adversary.perturb(images, target)
# compute output
output = model(adv_untargeted)
loss = criterion(output, target)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), images.size(0))
top1.update(acc1[0], images.size(0))
top5.update(acc5[0], images.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
# TODO: this should also be done with the ProgressMeter
print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))
return top1.avg
def robust_acc(model, device, loader, epsilon=0.3, nb_iter=40, step_size=0.01):
'''
Return the robust accuracy of a given model.
'''
correct = 0
total = 0
model.to(device).eval()
adversary = L2PGDAttack(model, loss_fn = nn.CrossEntropyLoss(reduction = "sum"), eps = epsilon, \
nb_iter = nb_iter, eps_iter = step_size, rand_init = True, clip_min = 0.0, clip_max = 1.0, \
targeted = False)
for cln_data, label in loader:
cln_data, label = cln_data.to(device), label.to(device)
adv_untargeted = adversary.perturb(cln_data, label)
with torch.no_grad():
output = model(adv_untargeted)
total += label.shape[0]
correct += (torch.argmax(output, dim=1) == label).sum().item()
return correct / total
def generate(datasetname, batch_size):
save_dir_path = "{}/data_adv_defense/guided_denoiser".format(PY_ROOT)
os.makedirs(save_dir_path, exist_ok=True)
set_log_file(save_dir_path + "/generate_{}.log".format(datasetname))
data_loader = DataLoaderMaker.get_img_label_data_loader(datasetname, batch_size, is_train=True)
attackers = []
for model_name in MODELS_TRAIN_STANDARD[datasetname] + MODELS_TEST_STANDARD[datasetname]:
model = StandardModel(datasetname, model_name, no_grad=False)
model = model.cuda().eval()
linf_PGD_attack =LinfPGDAttack(model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=0.031372, nb_iter=30,
eps_iter=0.01, rand_init=True, clip_min=0.0, clip_max=1.0, targeted=False)
l2_PGD_attack = L2PGDAttack(model, loss_fn=nn.CrossEntropyLoss(reduction="sum"),eps=4.6,
nb_iter=30,clip_min=0.0, clip_max=1.0, targeted=False)
FGSM_attack = FGSM(model, loss_fn=nn.CrossEntropyLoss(reduction="sum"))
momentum_attack = MomentumIterativeAttack(model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=0.031372, nb_iter=30,
eps_iter=0.01, clip_min=0.0, clip_max=1.0, targeted=False)
attackers.append(linf_PGD_attack)
attackers.append(l2_PGD_attack)
attackers.append(FGSM_attack)
attackers.append(momentum_attack)
log.info("Create model {} done!".format(model_name))
generate_and_save_adv_examples(datasetname, data_loader, attackers, save_dir_path)
num_workers=4)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=100,
shuffle=False,
num_workers=4)
net = ResNet18()
net = net.to(device)
net = torch.nn.DataParallel(net)
cudnn.benchmark = True
adversary = L2PGDAttack(net,
loss_fn=nn.CrossEntropyLoss(),
eps=0.25,
nb_iter=100,
eps_iter=0.01,
rand_init=True,
clip_min=0.0,
clip_max=1.0,
targeted=False)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),
lr=learning_rate,
momentum=0.9,
weight_decay=0.0002)
def train(epoch):
print('\n[ Train epoch: %d ]' % epoch)
net.train()
train_loss = 0
# load model
model = MNISTM_Model()
# setup optimizer
optimizer = optim.Adam(model.parameters(), lr=lr)
loss_func = torch.nn.CrossEntropyLoss()
model = model.cuda()
loss_func = loss_func.cuda()
adversary_train = L2PGDAttack(model,
loss_fn=nn.CrossEntropyLoss().cuda(),
eps=80 / 255,
nb_iter=40,
eps_iter=0.01,
rand_init=True,
clip_min=0,
clip_max=1.0,
targeted=False)
adversary_test = L2PGDAttack(model,
loss_fn=nn.CrossEntropyLoss().cuda(),
eps=80 / 255,
nb_iter=100,
eps_iter=0.01,
rand_init=True,
clip_min=0,
clip_max=1.0,
targeted=False)
def train(model, device, trainloader, testloader, loss_fn, optimizer, epochs = 1, verbose = 0, ckpt_folder = None, \
adv = False, epsilon = 0.3, nb_iter = 40, step_size = 0.01, \
regularizer = False, mu = 1, tau = 1, beta = 1):
'''
Train a model, returning the model in train mode on the device.
Value of verbose:
0 -- Only print training loss
1 -- Print training loss and training acc
2 -- Print training loss, training error and test acc
If ckpt_path != None, then assume verbosr = 2
'''
print('Train %s on %s' % (model.__class__.__name__, device))
model.to(device).train()
for i in range(epochs):
total_loss = 0
for img, label in trainloader:
# label is a tensor, one number for each image
img, label = img.to(device), label.to(device)
if adv == True:
model.eval()
adversary = L2PGDAttack(model, loss_fn = nn.CrossEntropyLoss(reduction = "sum"), eps = epsilon, \
nb_iter = nb_iter, eps_iter = step_size, rand_init = True, clip_min = 0.0, clip_max = 1.0, \
targeted = False)
img = adversary.perturb(img, label)
model.train()
model.zero_grad()
output = model(img)
loss = loss_fn(output, label)
if regularizer == True:
n_samples = output.shape[0]
index = torch.arange(n_samples, device=device)
output = output[index, label].reshape((n_samples, 1)) - output
output[index, label] = torch.tensor(1e10, device=device)
alpha, _ = torch.min(output, dim=1)
# regularizer is H_tau(\alpha) - H_0(\alpha)
# if model.__class__.__name__ != 'CIFARCNN':
# loss += mu * torch.mean(torch.max(torch.tensor(0, dtype = torch.float, device = device), tau - alpha))
# else:
loss += mu * torch.mean(torch.clamp(-alpha, min=0, max=tau))
if beta > 0:
loss += orthogonal_constraint(model, device=device, beta=beta)
loss.backward()
optimizer.step()
total_loss += loss.item()
total_loss /= len(trainloader)
# Start to evaluate model
model.eval()
if ckpt_folder != None:
verbose = 2
if verbose == 0:
print('Epoch : %d, Loss : %.10f' % (i + 1, total_loss))
elif verbose == 1:
train_acc = acc(model, device, trainloader)
print('Epoch : %d, Loss : %.10f, Training Acc : %f' %
(i + 1, total_loss, train_acc))
elif verbose == 2:
train_acc = acc(model, device, trainloader)
test_acc = acc(model, device, testloader)
print(
'Epoch : %d, Loss : %.10f, Training Acc : %f, Test Acc : %f' %
(i + 1, total_loss, train_acc, test_acc))
else:
assert (0)
if ckpt_folder != None:
checkpoint = {'epochs' : i + 1, \
'loss' : total_loss, \
'train_acc' : train_acc, \
'test_acc' : test_acc, \
'model_state_dict' : model.state_dict(), \
'optmizer_state_dict' : optimizer.state_dict(), \
}
torch.save(
checkpoint, ckpt_folder + '/' + model.__class__.__name__ +
'_' + ('' if regularizer == False and beta < 1e-15 else
('reg_' if regularizer == 1 else 'constraint_')) +
str(i + 1).zfill(5) + '.tar')
# Set model back to train mode for the next epoch
model.train()
return model
def validate(val_loader, model, criterion, args):
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
top5 = AverageMeter('Acc@5', ':6.2f')
progress = ProgressMeter(len(val_loader), [batch_time, losses, top1, top5],
prefix='Test: ')
# switch to evaluate mode
model.eval()
de_transform = denormalize_transform()
with torch.no_grad():
adversary = L2PGDAttack(model,
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps=14.2737,
nb_iter=20,
eps_iter=1.784,
rand_init=True,
clip_min=-2.1179,
clip_max=2.6400,
targeted=False)
end = time.time()
for i, (images, target) in enumerate(val_loader):
# save_image(images[0], 'img1.png')
# de_transform(images)
save_image(images[2], 'img1.png')
print(images.shape)
grid_img = torchvision.utils.make_grid(images, nrow=2)
save_image(grid_img, 'grid_img.png')
# image_numpy=images.numpy()
# temp=image_numpy.transpose(0,2,3,1)
# img = Image.fromarray(temp[0].squeeze(),'RGB')
# img.save('my.png')
# img.show()
# image = np.asarray(bytearray(image_numpy), dtype="uint8")
# image = cv2.imdecode(image, cv2.IMREAD_COLOR)
# cv2.imshow('image',image)
# cv2.waitKey(0)
# print(temp.shape)
# print(type(images))
if args.gpu is not None:
images = images.cuda(args.gpu, non_blocking=True)
if torch.cuda.is_available():
target = target.cuda(args.gpu, non_blocking=True)
with torch.enable_grad():
adv_untargeted = adversary.perturb(images, target)
adv_grid_img = torchvision.utils.make_grid(adv_untargeted, nrow=2)
save_image(adv_grid_img, 'adv_grid_img.png')
diff = adv_untargeted - images
diff_norm = torch.norm(diff, p=2, dim=(1, 2, 3))
print(diff_norm)
return
# compute output
output = model(adv_untargeted)
loss = criterion(output, target)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), images.size(0))
top1.update(acc1[0], images.size(0))
top5.update(acc5[0], images.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
plt.show()
# TODO: this should also be done with the ProgressMeter
print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'.format(top1=top1,
top5=top5))
return top1.avg
return (test_loss / batch_idx, acc)
train_loss, train_acc = test(trainloader)
test_loss, test_acc = test(testloader)
test_loss, test_acc = test(testloader, do_awgn=True)
adversary_linf = LinfPGDAttack(net,
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps=0.03,
nb_iter=20,
eps_iter=0.003,
rand_init=False,
clip_min=-2.0,
clip_max=2.0,
targeted=False)
adver_lss, adver_acc = test_adver(testloader, adversary_linf)
print('Linf acc', adver_acc)
adversary_l2 = L2PGDAttack(net,
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps=np.sqrt(3 * 32 * 32) * 0.03,
nb_iter=20,
eps_iter=np.sqrt(3 * 32 * 32) * 0.003,
rand_init=False,
clip_min=-2.0,
clip_max=2.0,
targeted=False)
adver_lss, adver_acc = test_adver(testloader, adversary_l2)
print('L2 acc', adver_acc)
print(args)
device = "cuda"
set_seed(args.seed)
testset, normalize, unnormalize = str2dataset(args.dataset)
testloader = torch.utils.data.DataLoader(testset, batch_size=args.batch_size, shuffle=False, num_workers=0)
net = str2model(args.checkpoint, dataset=args.dataset, pretrained=True).eval().to(device)
if args.norm is None:
adversary = Clean()
elif args.norm == "2":
adversary = L2PGDAttack(predict=lambda x: net(normalize(x)), loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=args.eps,
nb_iter=args.nb_iter, eps_iter=args.lr, rand_init=True, clip_min=0.0, clip_max=1.0, targeted=False)
elif args.norm == "inf":
adversary = LinfPGDAttack(predict=lambda x: net(normalize(x)), loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=args.eps,
nb_iter=args.nb_iter, eps_iter=args.lr, rand_init=True, clip_min=0.0, clip_max=1.0, targeted=False)
acc = test(lambda x: net(normalize(x)),
testloader,
device=device,
attacker=adversary,
num_batch=args.num_batch,
save_img_loc=args.save_img_loc)
print(acc)
def validate(adversary_settings, model, criterion, val_loader, device):
"""
Runs a single epoch of validation using Madry's adversarial method
"""
# The model should not accumulate gradients
# However, adversary will require gradients
# Don't wrap this function with torch.no_grad()
model.eval()
for param in model.parameters():
param.requires_grad = False
val_loss = 0.0
val_correct = 0.0
val_total = 0.0
adv_val_loss = 0.0
adv_val_correct = 0.0
adv_epsilon, adv_iterations, adv_step = adversary_settings
adversary = L2PGDAttack(
model,
loss_fn=torch.nn.CrossEntropyLoss(reduction="sum"),
eps=adv_epsilon,
nb_iter=adv_iterations,
eps_iter=adv_step,
rand_init=True,
clip_min=-1.0,
clip_max=1.0,
targeted=False,
)
for examples, labels in val_loader:
examples, labels = examples.to(device), labels.to(device)
# need to explicitly represent 1 input channel
examples = torch.unsqueeze(examples, 1)
outputs = model(examples)
# Normal
loss = criterion(outputs, labels)
val_loss += loss.data.item()
_, predictions = torch.max(outputs, 1)
val_correct += (predictions == labels).sum().item()
val_total += labels.shape[0]
# Generate adversarial training examples
adv_examples = adversary.perturb(examples, labels)
adv_outputs = model(adv_examples)
adv_loss = criterion(adv_outputs, labels)
adv_val_loss += adv_loss.data.item()
_, predictions = torch.max(adv_outputs, 1)
adv_val_correct += (predictions == labels).sum().item()
for param in model.parameters():
param.requires_grad = True
return val_loss / len(
val_loader), 100 * val_correct / val_total, adv_val_loss / len(
val_loader), 100 * adv_val_correct / val_total
def train_model(
train_adversary_settings,
val_adversary_settings,
model,
optimizer,
criterion,
train_loader,
val_loader,
num_epochs,
tensorboard_writer,
device,
save_settings,
):
do_saving, save_directory, save_filename, starting_epoch, best_acc = save_settings
# Initialize PGD adversary
adv_epsilon, adv_iterations, adv_step = train_adversary_settings
adversary = L2PGDAttack(
model,
loss_fn=torch.nn.CrossEntropyLoss(reduction="sum"),
eps=adv_epsilon,
nb_iter=adv_iterations,
eps_iter=adv_step,
rand_init=True,
clip_min=0.0,
clip_max=1.0,
targeted=False,
)
for e in range(num_epochs):
# Adversarial training loop
train_loss, train_acc = train_loop_adversarial(adversary, model,
optimizer, criterion,
train_loader, device)
tensorboard_writer.add_scalar("Loss/Train", train_loss,
starting_epoch + e)
tensorboard_writer.add_scalar("Acc/Train", train_acc,
starting_epoch + e)
val_loss, val_acc, adv_val_loss, adv_val_acc = validate(
val_adversary_settings, model, criterion, val_loader, device)
tensorboard_writer.add_scalar("Loss/Val", val_loss, starting_epoch + e)
tensorboard_writer.add_scalar("Acc/Val", val_acc, starting_epoch + e)
tensorboard_writer.add_scalar("Loss/Adv_Val", adv_val_loss,
starting_epoch + e)
tensorboard_writer.add_scalar("Acc/Adv_Val", adv_val_acc,
starting_epoch + e)
if do_saving:
is_best = False
if val_acc > best_acc:
best_acc = val_acc
is_best = True
state = {
"model_state": model.state_dict(),
"accuracy": val_acc,
"epoch": starting_epoch + e,
"optimizer_state": optimizer.state_dict(),
}
save_checkpoint(state, save_directory, save_filename, is_best)
return [
float(correct[:k].reshape(-1).float().sum(0,
keepdim=True).cpu().numpy())
for k in topk
]
resnet = models.resnet50(pretrained=True)
resnet = resnet.cuda()
model.visual = model.visual.eval()
resnet_adversary = L2PGDAttack(resnet,
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps=9.9756,
nb_iter=20,
eps_iter=1.24695,
rand_init=True,
clip_min=-2.1179,
clip_max=2.6400,
targeted=False)
adversary = L2PGDAttack(model.visual,
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps=14.2737,
nb_iter=20,
eps_iter=1.784,
rand_init=True,
clip_min=-2.1179,
clip_max=2.6400,
targeted=False)
with torch.no_grad():
print('Adv acc:', accu)
return test_adv_data, test_adv_labels
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--gpu_id', type=str, nargs='?', default='0', help="device id to run")
parser.add_argument('--eps', default=80/255, type=float, help='eps')
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
attacker = L2PGDAttack(model, loss_fn=nn.CrossEntropyLoss().cuda(), eps=args.eps,
nb_iter=100, eps_iter=0.01, rand_init=True, clip_min=0, clip_max=1.0,
targeted=False)
adv_data_save_path = os.path.join('dataset','l2_mnistm_advT')
os.makedirs(adv_data_save_path, exist_ok=True)
########### generating train
train_adv_data, train_adv_labels = train()
np.save(adv_data_save_path + '/train_eps' + str(args.eps), [train_adv_data, train_adv_labels])
########### generating test
test_adv_data, test_adv_labels = test()
np.save(adv_data_save_path + '/test_eps' + str(args.eps), [test_adv_data, test_adv_labels])
adversary = LinfPGDAttack(net,
loss_fn=cross_entropy_loss(),
eps=args.eps,
nb_iter=40,
eps_iter=0.01,
rand_init=True,
clip_min=0.0,
clip_max=1.0,
targeted=False)
else:
adversary = L2PGDAttack(net,
loss_fn=csl,
eps=args.eps,
nb_iter=10,
eps_iter=12.75,
rand_init=True,
clip_min=0.0,
clip_max=255.,
targeted=False)
#adversary = PGDAttack(net, loss_fn=csl, eps=args.eps, nb_iter=10, eps_iter=12.75, rand_init=True, clip_min=0.0,clip_max=255., targeted=False)
#adversary = L2BasicIterativeAttack(net, loss_fn=csl, eps=args.eps, nb_iter=10, eps_iter=12.75, clip_min=0.0, clip_max=255., targeted=False)
#adversary =MomentumIterativeAttack(net, loss_fn=csl, eps=args.eps, nb_iter=10, eps_iter=12.75, clip_min=0.0, clip_max=255., targeted=False)
# adversary = JSMA(net, num_classes = 24, clip_min=0.0, clip_max=255.,theta=1.0, gamma=1.0,loss_fn=nn.CrossEntropyLoss())
net.eval()
# net2.eval()
# net3.eval()
# net4.eval()
# net5.eval()
correct = 0
def validate(val_loader, model, criterion, args):
print("validating")
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
top5 = AverageMeter('Acc@5', ':6.2f')
progress = ProgressMeter(len(val_loader), [batch_time, losses, top1, top5],
prefix='Test: ')
# switch to evaluate mode
model.eval()
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
with torch.no_grad():
# adversary = LinfPGDAttack(
# model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=0.15,
# nb_iter=40, eps_iter=0.01, rand_init=True, clip_min=0.0, clip_max=1.0,
# targeted=False)
adversary = L2PGDAttack(model,
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps=14.2737,
nb_iter=20,
eps_iter=1.784,
rand_init=True,
clip_min=-2.1179,
clip_max=2.6400,
targeted=False)
end = time.time()
print("enumerate dataloader")
for i, (images, target) in enumerate(val_loader):
# print(images)
if args.gpu is not None:
images = images.cuda(args.gpu, non_blocking=True)
if torch.cuda.is_available():
target = target.cuda(args.gpu, non_blocking=True)
with torch.enable_grad():
adv_untargeted = adversary.perturb(images.cuda(),
target.cuda())
# compute output
if args.arch == 'simclr':
output = model(adv_untargeted)
elif args.arch == 'linf_4' or args.arch == 'linf_8' or args.arch == 'l2_3':
output = model((adv_untargeted))
elif args.arch == 'resnet50_l2_eps1':
output = model(adv_untargeted)
else:
output = model((adv_untargeted))
loss = criterion(output, target)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), images.size(0))
top1.update(acc1[0], images.size(0))
top5.update(acc5[0], images.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
# TODO: this should also be done with the ProgressMeter
print(args.arch)
print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'.format(top1=top1,
top5=top5))
accuracy_array = []
accuracy_array.append(top1.avg)
accuracy_array.append(top5.avg)
np.save(f'/content/gdrive/MyDrive/model_adv_loss/{args.arch}_accuracy.npy',
accuracy_array)
return top1.avg, top5.avg
adversary = LinfPGDAttack(
model.visual, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=4.7579/1020,
nb_iter=20, eps_iter=0.000233, rand_init=True, clip_min=-2.1179, clip_max=2.6400,
targeted=False)
if args.attack=="inf4":
adversary = LinfPGDAttack(
model.visual, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=19.0316/255,
nb_iter=20, eps_iter=47.579/5100, rand_init=True, clip_min=-2.1179, clip_max=2.6400,
targeted=False)
if args.attack=='2':
# adversary = L2PGDAttack(
# self.model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=14.2737,
# nb_iter=20, eps_iter=1.784, rand_init=True, clip_min=-2.1179, clip_max=2.6400,
# targeted=False)
adversary = L2PGDAttack(
model.visual, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=0.7137,
nb_iter=20, eps_iter=0.09, rand_init=True, clip_min=-2.1179, clip_max=2.6400,
targeted=False)
with torch.no_grad():
top1, top5, n = 0., 0., 0.
for i, (images, target) in enumerate(tqdm(loader)):
images = images.cuda()
target = target.cuda()
with torch.enable_grad():
adv_untargeted = adversary.perturb(images, target)
# predict
image_features = model.encode_image(adv_untargeted)
image_features /= image_features.norm(dim=-1, keepdim=True)
logits = 100. * image_features @ zeroshot_weights
# measure accuracy
acc1, acc5 = accuracy(logits, target, topk=(1, 5))
