def METHOD_NAME(self, img):
    img_info = {"id": 0}
    height, width = img.shape[:2]
    img_info["height"] = height
    img_info["width"] = width
    meta = dict(img_info=img_info, raw_img=img, img=img)
    meta = self.pipeline(None, meta, self.cfg.data.val.input_size)
    meta["img"] = torch.from_numpy(meta["img"].transpose(2, 0, 1)).to(self.device)
    _input = meta["img"]
    _height = torch.tensor(height)
    _width = torch.tensor(width)
    _warp_matrix = torch.from_numpy(meta["warp_matrix"])
    return _input, _height, _width, _warp_matrix