type=str,
help='datatset stroage directory',
default='/data/datasets')
args = vars(parser.parse_args())
print(args)
modeldir = get_modeldir_ens(args['ind'], args['model_arch'])
ensemble_num = args['model_num']
# ----- load dataset -----
transform = get_transform(args['ind'])
std = get_std(args['ind'])
ind_test_loader = get_dataloader(args['ind'],
transform,
"test",
dataroot=args['dataroot'],
batch_size=args['batch_size'])
ood_test_loader = get_dataloader(args['ood'],
transform,
"test",
dataroot=args['dataroot'],
batch_size=args['batch_size'])
ind_dataloader_val_for_train, ind_dataloader_val_for_test, ind_dataloader_test = split_dataloader(
args['ind'], ind_test_loader, [500, 500, -1])
ood_dataloader_val_for_train, ood_dataloader_val_for_test, ood_dataloader_test = split_dataloader(
args['ood'], ood_test_loader, [500, 500, -1])
# ----- Calculating and averaging maximum softmax probabilities -----
from lib.inference.ODIN import get_ODIN_score
best_temperature = 1.0
args = vars(parser.parse_args())
print(args)
# ----- load pre-trained model -----
model = get_model(args['ind'], args['model_arch'])
# ----- load dataset -----
transform = get_transform(args['ind'])
std = get_std(args['ind'])
img_size = get_img_size(args['ind'])
inp_channel = get_inp_channel(args['ind'])
batch_size = args['batch_size'] # recommend: 64 for ImageNet, CelebA, MS1M
ind_train_loader = get_dataloader(args['ind'],
transform,
"train",
dataroot=args['dataroot'],
batch_size=batch_size)
ind_test_loader = get_dataloader(args['ind'],
transform,
"test",
dataroot=args['dataroot'],
batch_size=batch_size)
ood_test_loader = get_dataloader(args['ood'],
transform,
"test",
dataroot=args['dataroot'],
batch_size=batch_size)
ind_dataloader_val_for_train, ind_dataloader_val_for_test, ind_dataloader_test = split_dataloader(
args['ind'], ind_test_loader, [500, 500, -1], random=True)
ood_dataloader_val_for_train, ood_dataloader_val_for_test, ood_dataloader_test = split_dataloader(