print(len(train_loader),len(val_loader),len(test_loader))
m = models.vgg19(pretrained=True).to(DEVICE)
image_mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
image_std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
model = NormalizedModel(model=m, mean=image_mean, std=image_std).to(DEVICE)
# weight = './weights/imagenet_resnet152_jpeg/Imagenetacc0.9642857142857143_20.pth'
# loaded_state_dict = torch.load(weight)
# model.load_state_dict(loaded_state_dict)
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
optimizer = SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
if args.adv == 0:
scheduler = lr_scheduler.StepLR(optimizer, step_size=args.lr_step, gamma=args.lr_decay)
else:
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[60, 120, 160], gamma=0.2)
attacker = DDN(steps=args.steps, device=DEVICE)
max_loss = torch.log(torch.tensor(10.)).item() # for callback
best_acc = 0
best_epoch = 0
valacc_final = 0
max_loss = torch.log(torch.tensor(1000.)).item() # for callback
shuffle=True,
num_workers=args.workers,
pin_memory=True)
test_loader = data.DataLoader(test_set,
batch_size=100,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
m = wide_resnet(num_classes=10, depth=28, widen_factor=10, dropRate=args.drop)
model = NormalizedModel(model=m, mean=image_mean, std=image_std).to(
DEVICE) # keep images in the [0, 1] range
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
optimizer = SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.adv == 0:
scheduler = lr_scheduler.StepLR(optimizer,
step_size=args.lr_step,
gamma=args.lr_decay)
else:
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=[60, 120, 160],
gamma=0.2)
max_loss = torch.log(torch.tensor(10.)).item() # for callback
best_acc = 0
best_epoch = 0