])), batch_size=args.batchsize, shuffle=True, num_workers=args.nworkers ) test_loader = torch.utils.data.DataLoader( datasets.Imagenet32(train=False, transform=transforms.Compose([ add_noise, ])), batch_size=args.val_batchsize, shuffle=False, num_workers=args.nworkers ) elif args.data == 'imagenet64': im_dim = 3 init_layer = layers.LogitTransform(0.05) if args.imagesize != 64: logger.info('Changing image size to 64.') args.imagesize = 64 train_loader = torch.utils.data.DataLoader( datasets.Imagenet64(train=True, transform=transforms.Compose([ add_noise, ])), batch_size=args.batchsize, shuffle=True, num_workers=args.nworkers ) test_loader = torch.utils.data.DataLoader( datasets.Imagenet64(train=False, transform=transforms.Compose([ add_noise, ])), batch_size=args.val_batchsize, shuffle=False, num_workers=args.nworkers ) if args.task in ['classification', 'hybrid']: try: n_classes except NameError: raise ValueError('Cannot perform classification with {}'.format(args.data)) else: n_classes = 1
num_workers=args.nworkers) test_loader = torch.utils.data.DataLoader(datasets.Imagenet32( train=False, transform=transforms.Compose([ add_noise, ])), batch_size=args.val_batchsize, shuffle=False, num_workers=args.nworkers) elif args.data == 'imagenet64': im_dim = 3 init_layer = layers.LogitTransform(0.05) if args.imagesize != 64: logger.info('Changing image size to 64.') args.imagesize = 64 train_loader = torch.utils.data.DataLoader(datasets.Imagenet64( train=True, transform=transforms.Compose([ add_noise, ])), batch_size=args.batchsize, shuffle=True, num_workers=args.nworkers) test_loader = torch.utils.data.DataLoader(datasets.Imagenet64( train=False, transform=transforms.Compose([ add_noise, ])), batch_size=args.val_batchsize, shuffle=False, num_workers=args.nworkers) if args.task in ['classification', 'hybrid']: try: n_classes