if args.num_epochs is None:
args.num_epochs = 100
elif (args.dataset.startswith('imagenet')
or args.dataset == 'bird_or_bicycle'):
if args.lr is None:
args.lr = 1e-1
if args.lr_schedule is None:
args.lr_schedule = '30,60,80'
if args.num_epochs is None:
args.num_epochs = 90
torch.manual_seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
dataset, model = get_dataset_model(args)
if isinstance(model, FeatureModel):
model.allow_train()
if torch.cuda.is_available():
model.cuda()
if args.lpips_model is not None:
_, lpips_model = get_dataset_model(args,
checkpoint_fname=args.lpips_model)
if torch.cuda.is_available():
lpips_model.cuda()
train_loader, val_loader = dataset.make_loaders(workers=4,
batch_size=args.batch_size)
attacks = [eval(attack_str) for attack_str in args.attack]
add_dataset_model_arguments(parser, include_checkpoint=True)
parser.add_argument('attacks', metavar='attack', type=str, nargs='+',
help='attack names')
parser.add_argument('--batch_size', type=int, default=100,
help='number of examples/minibatch')
parser.add_argument('--parallel', type=int, default=1,
help='number of GPUs to train on')
parser.add_argument('--num_batches', type=int, required=False,
help='number of batches (default entire dataset)')
parser.add_argument('--per_example', action='store_true', default=False,
help='output per-example accuracy')
parser.add_argument('--output', type=str, help='output CSV')
args = parser.parse_args()
dataset, model = get_dataset_model(args)
_, val_loader = dataset.make_loaders(1, args.batch_size, only_val=True)
model.eval()
if torch.cuda.is_available():
model.cuda()
attack_names: List[str] = args.attacks
attacks = [eval(attack_name) for attack_name in attack_names]
# Parallelize
if torch.cuda.is_available():
device_ids = list(range(args.parallel))
model = nn.DataParallel(model, device_ids)
attacks = [nn.DataParallel(attack, device_ids) for attack in attacks]
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Common corruptions evaluation')
add_dataset_model_arguments(parser, include_checkpoint=True)
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument('--num_batches',
type=int,
required=False,
help='number of batches (default entire dataset)')
parser.add_argument('--output', type=str, help='output CSV')
args = parser.parse_args()
_, model = get_dataset_model(args)
dataset_cls = DATASETS[args.dataset]
alexnet_args = copy.deepcopy(args)
alexnet_args.arch = 'alexnet'
alexnet_args.checkpoint = None
if args.dataset == 'cifar10c':
alexnet_checkpoint_fname = 'data/checkpoints/alexnet_cifar.pt'
elif args.dataset == 'imagenet100c':
alexnet_checkpoint_fname = 'data/checkpoints/alexnet_imagenet100.pt'
else:
raise ValueError(f'Invalid dataset "{args.dataset}"')
_, alexnet = get_dataset_model(alexnet_args,
checkpoint_fname=alexnet_checkpoint_fname)
model.eval()