def METHOD_NAME(frame_resize: np.ndarray, batch_input_shape: Tuple[int, int],
              ori_shape: Tuple[int, int]) -> dict:
    assert frame_resize.shape[:2] == batch_input_shape
    data_sample = DetDataSample()
    data_sample.set_metainfo({
        'img_shape':
        batch_input_shape,
        'ori_shape':
        ori_shape,
        'scale_factor': (batch_input_shape[0] / ori_shape[0],
                         batch_input_shape[1] / ori_shape[1])
    })
    frame_resize = torch.from_numpy(frame_resize).permute((2, 0, 1)).cuda()
    data = {'inputs': [frame_resize], 'data_samples': [data_sample]}
    return data