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