eval_retrieval_feature_extract.py [49:70]:
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    parser.add_argument('--pretrained_model', type=str, required=True, help="pretrained model path")
    parser.add_argument('--output_dir', type=str, default='./eval_output', help='output director')

    # msic
    parser.add_argument("--local_rank", type=int, help='local rank for DistributedDataParallel')
    args = parser.parse_args()
    return args


def get_loader(args):
    val_transform_list = []
    if args.resize > 0:
        val_transform_list.append(transforms.Resize(args.resize))
    val_transform_list.append(transforms.CenterCrop(args.cropsize))
    val_transform_list.append(transforms.ToTensor())
    val_transform_list.append(transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))
    val_transform = transforms.Compose(val_transform_list)
    clipdataset = build_dataset(
        dataset=args.dataset,
        root=args.data_dir,
        split=args.datasplit,
        transform=val_transform,
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eval_svm_feature_extract.py [43:64]:
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    parser.add_argument('--pretrained_model', type=str, required=True, help="pretrained model path")
    parser.add_argument('--output_dir', type=str, default='./eval_output', help='output director')

    # msic
    parser.add_argument("--local_rank", type=int, help='local rank for DistributedDataParallel')
    args = parser.parse_args()
    return args


def get_loader(args):
    val_transform_list = []
    if args.resize > 0:
        val_transform_list.append(transforms.Resize(args.resize))
    val_transform_list.append(transforms.CenterCrop(args.cropsize))
    val_transform_list.append(transforms.ToTensor())
    val_transform_list.append(transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))
    val_transform = transforms.Compose(val_transform_list)
    clipdataset = build_dataset(
        dataset=args.dataset,
        root=args.data_dir,
        split=args.datasplit,
        transform=val_transform,
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