def build_dataset()

in datasets.py [0:0]

• 29 lines of code
• 11 McCabe index (conditional complexity)

def build_dataset(is_train, args):
    transform = build_transform(is_train, args)

    if args.data_set == 'CIFAR100':
        dataset = datasets.CIFAR100(args.data_path, train=is_train, transform=transform)
        nb_classes = 100
    elif args.data_set == 'CIFAR10':
        dataset = datasets.CIFAR10(args.data_path, train=is_train, transform=transform)
        nb_classes = 10
    elif args.data_set == 'IMNET':
        root = os.path.join(args.data_path, 'train' if is_train else 'val')
        dataset = datasets.ImageFolder(root, transform=transform)
        nb_classes = 1000
    elif args.data_set == 'INAT':
        dataset = INatDataset(args.data_path, train=is_train, year=2018,
                              category=args.inat_category, transform=transform)
        nb_classes = dataset.nb_classes
    elif args.data_set == 'INAT19':
        dataset = INatDataset(args.data_path, train=is_train, year=2019,
                              category=args.inat_category, transform=transform)
        nb_classes = dataset.nb_classes
    elif args.data_set == 'CARS':
        root = os.path.join(args.data_path, 'train' if is_train else 'test')
        dataset = datasets.ImageFolder(root, transform=transform)
        nb_classes = 196
    elif args.data_set == 'FLOWERS':
        root = os.path.join(args.data_path, 'train' if is_train else 'test')
        dataset = datasets.ImageFolder(root, transform=transform)
        nb_classes = 102

    return dataset, nb_classes