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
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.CelebA5bit(
train=True, transform=transforms.Compose([
transforms.ToPILImage(),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
lambda x: add_noise(x, nvals=32),
])
), batch_size=args.batchsize, shuffle=True, num_workers=args.nworkers
)
test_loader = torch.utils.data.DataLoader(
datasets.CelebA5bit(train=False, transform=transforms.Compose([
lambda x: add_noise(x, nvals=32),
])), batch_size=args.val_batchsize, shuffle=False, num_workers=args.nworkers
)
elif args.data == 'imagenet32':
im_dim = 3
init_layer = layers.LogitTransform(0.05)
if args.imagesize != 32:
logger.info('Changing image size to 32.')
args.imagesize = 32
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
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.CelebA5bit(
train=True,
transform=transforms.Compose([
transforms.ToPILImage(),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
lambda x: add_noise(x, nvals=32),
])),
batch_size=args.batchsize,
shuffle=True,
num_workers=args.nworkers)
test_loader = torch.utils.data.DataLoader(datasets.CelebA5bit(
train=False,
transform=transforms.Compose([
lambda x: add_noise(x, nvals=32),
])),
batch_size=args.val_batchsize,
shuffle=False,
num_workers=args.nworkers)
elif args.data == 'imagenet32':
