])), batch_size=args.val_batchsize, shuffle=False, num_workers=args.nworkers, ) elif args.data == 'celebahq': im_dim = 3 init_layer = layers.LogitTransform(0.05) if args.imagesize != 256: logger.info('Changing image size to 256.') args.imagesize = 256 train_loader = torch.utils.data.DataLoader(datasets.CelebAHQ( train=True, transform=transforms.Compose([ transforms.ToPILImage(), transforms.RandomHorizontalFlip(), transforms.ToTensor(), reduce_bits, lambda x: add_noise(x, nvals=2**args.nbits), ])), batch_size=args.batchsize, shuffle=True, num_workers=args.nworkers) test_loader = torch.utils.data.DataLoader(datasets.CelebAHQ( train=False, transform=transforms.Compose([ reduce_bits, lambda x: add_noise(x, nvals=2**args.nbits), ])), batch_size=args.val_batchsize, shuffle=False,
batch_size=args.val_batchsize, shuffle=False, num_workers=args.nworkers, ) elif args.data == 'celebahq': im_dim = 3 init_layer = layers.LogitTransform(0.05) if args.imagesize != 256: logger.info('Changing image size to 256.') args.imagesize = 256 train_loader = torch.utils.data.DataLoader( datasets.CelebAHQ( train=True, transform=transforms.Compose([ transforms.ToPILImage(), transforms.RandomHorizontalFlip(), transforms.ToTensor(), reduce_bits, lambda x: add_noise(x, nvals=2**args.nbits), ]) ), batch_size=args.batchsize, shuffle=True, num_workers=args.nworkers ) test_loader = torch.utils.data.DataLoader( datasets.CelebAHQ( train=False, transform=transforms.Compose([ reduce_bits, lambda x: add_noise(x, nvals=2**args.nbits), ]) ), batch_size=args.val_batchsize, shuffle=False, num_workers=args.nworkers ) elif args.data == 'celeba_5bit': im_dim = 3
checkpoint = torch.load(model_name) args = checkpoint['args'] args.val_batchsize = 6 args.batchsize = 6 # define data if args.data == 'celeba64_5bit': im_dim = 3 init_layer = layers.LogitTransform(0.05) if args.imagesize != 64: args.imagesize = 64 # no dequantization for attack! train_loader = torch.utils.data.DataLoader(datasets.CelebAHQ( train=True, transform=transforms.Compose([ transforms.ToPILImage(), transforms.Resize(args.imagesize), transforms.ToTensor(), reduce_bits ])), batch_size=args.batchsize, shuffle=False, num_workers=args.nworkers) # no dequantization for attack! test_loader = torch.utils.data.DataLoader(datasets.CelebAHQ( train=False, transform=transforms.Compose( [transforms.ToPILImage(), transforms.ToTensor(), reduce_bits])), batch_size=args.val_batchsize, shuffle=False, num_workers=args.nworkers)