scheduler.step()
cudnn.benchmark = True
model.train()
requires_grad_(m, True)
accs = AverageMeter()
losses = AverageMeter()
attack_norms = AverageMeter()
length = len(train_loader)
for i, (images, labels) in enumerate(tqdm.tqdm(train_loader, ncols=80)):
images, labels = images.to(DEVICE), labels.to(DEVICE)
#原图loss
#logits_clean = model.forward(images)
logits_clean, feature_clean = model.forward(images)
#loss = F.cross_entropy(logits_clean, labels)
if args.adv is not None and epoch >= args.adv:
model.eval()
requires_grad_(m, False)
adv = attacker.attack(model, images, labels)
l2_norms = (adv - images).view(args.batch_size, -1).norm(2, 1)
mean_norm = l2_norms.mean()
if args.max_norm:
adv = torch.renorm(
adv - images, p=2, dim=0, maxnorm=args.max_norm) + images
attack_norms.append(mean_norm.item())
requires_grad_(m, True)
model.train()
model.train()
requires_grad_(m, True)
accs = AverageMeter()
losses = AverageMeter()
attack_norms = AverageMeter()
length = len(train_loader)
for i, (images, labels) in enumerate(tqdm.tqdm(train_loader, ncols=80)):
images, labels = images.to(DEVICE), labels.to(DEVICE)
# augment inputs with noise
noise = torch.randn_like(images, device='cuda') * args.noise_sd
images_noise = images + noise
#原图loss
#logits_clean = model.forward(images)
logits_clean = model.forward(images_noise)
loss = F.cross_entropy(logits_clean, labels)
if args.adv is not None and epoch >= args.adv:
model.eval()
requires_grad_(m, False)
adv = attacker.attack(model, images, labels, noise=noise)
l2_norms = (adv - images).view(args.batch_size, -1).norm(2, 1)
mean_norm = l2_norms.mean()
if args.max_norm:
adv = torch.renorm(
adv - images, p=2, dim=0, maxnorm=args.max_norm) + images
attack_norms.append(mean_norm.item())
requires_grad_(m, True)
model.train()
adv_noise = adv + noise
scheduler.step()
cudnn.benchmark = True
model.train()
requires_grad_(m, True)
accs = AverageMeter()
losses = AverageMeter()
attack_norms = AverageMeter()
length = len(train_loader)
for i, (images, labels) in enumerate(tqdm.tqdm(train_loader, ncols=80)):
images, labels = images.to(DEVICE), labels.to(DEVICE)
#原图loss
#logits_clean = model.forward(images)
logits = model.forward(images)
loss = F.cross_entropy(logits, labels)
if args.adv is not None and epoch >= args.adv:
model.eval()
requires_grad_(m, False)
adv = attacker.attack(model, images, labels)
l2_norms = (adv - images).view(args.batch_size, -1).norm(2, 1)
mean_norm = l2_norms.mean()
if args.max_norm:
adv = torch.renorm(
adv - images, p=2, dim=0, maxnorm=args.max_norm) + images
attack_norms.append(mean_norm.item())
requires_grad_(m, True)
model.train()
ETA(), ' ',
FileTransferSpeed()
]
pbar = ProgressBar(widgets=widgets)
length = len(train_loader)
i = 0
sigma = 0.5
for batch_data in pbar(train_loader):
images, labels = batch_data['image'].to(
DEVICE), batch_data['label_idx'].to(DEVICE)
#images, labels = images.to(DEVICE), labels.to(DEVICE)
#原图loss
#logits_clean = model.forward(images)
logits_clean = model.forward(images)
loss = F.cross_entropy(logits_clean, labels)
if args.adv is not None and epoch >= args.adv:
model.eval()
requires_grad_(m, False)
adv = attacker.attack(model, images, labels)
l2_norms = (adv - images).view(args.batch_size, -1).norm(2, 1)
mean_norm = l2_norms.mean()
if args.max_norm:
adv = torch.renorm(
adv - images, p=2, dim=0, maxnorm=args.max_norm) + images
attack_norms.append(mean_norm.item())
requires_grad_(m, True)
model.train()