def METHOD_NAME():
    args = parse_args()
    input_shape = tuple(map(int, args.net_input_dims.split(',')))
    origin_img = cv2.imread(args.input)  #BGR HWC
    img = preproc(origin_img, input_shape)
    data = {'data': img}  # input name from model
    output = dict()
    if args.model_def.endswith('.prototxt') and args.model_data.endswith('.caffemodel'):
        output = caffe_inference(data, args.model_def, args.model_data, False)
    elif args.model_def.endswith('.mlir'):
        output = mlir_inference(data, args.model_def, False)
    elif args.model_def.endswith(".bmodel"):
        output = model_inference(data, args.model_def)
    else:
        raise RuntimeError("not support modle file:{}".format(args.model_def))
    fix_img = postproc(output, origin_img, args.category_file, top_k=5)
    cv2.imwrite(args.output, fix_img)