train=True, transform=test_transform) test_data = dset.CIFAR10('/share/data/vision-greg/cifarpy', train=False, transform=test_transform) num_classes = 10 else: train_data = dset.CIFAR100('/share/data/vision-greg/cifarpy', train=True, transform=test_transform) test_data = dset.CIFAR100('/share/data/vision-greg/cifarpy', train=False, transform=test_transform) num_classes = 100 train_data, val_data = validation_split(train_data, val_share=0.1) val_loader = torch.utils.data.DataLoader(val_data, batch_size=args.test_bs, shuffle=False, num_workers=args.prefetch, pin_memory=True) test_loader = torch.utils.data.DataLoader(test_data, batch_size=args.test_bs, shuffle=False, num_workers=args.prefetch, pin_memory=True) # Create model if 'allconv' in args.method_name: net = AllConvNet(num_classes) else:
torch.manual_seed(1) np.random.seed(1) train_data_in = svhn.SVHN('/share/data/vision-greg/svhn/', split='train_and_extra', transform=trn.ToTensor(), download=False) test_data = svhn.SVHN('/share/data/vision-greg/svhn/', split='test', transform=trn.ToTensor(), download=False) num_classes = 10 calib_indicator = '' if args.calibration: train_data_in, val_data = validation_split(train_data_in, val_share=5000 / 604388.) calib_indicator = 'calib_' tiny_images = TinyImages(transform=trn.Compose([ trn.ToTensor(), trn.ToPILImage(), trn.RandomHorizontalFlip(), trn.ToTensor() ])) train_loader_in = torch.utils.data.DataLoader(train_data_in, batch_size=args.batch_size, shuffle=True, num_workers=args.prefetch, pin_memory=True)