Reference for ultralytics/nn/backends/paddle.py

class PaddleBackend(BaseBackend):

def forward(self, im: torch.Tensor) -> list[np.ndarray]:
    """Run Baidu PaddlePaddle inference.

    Args:
        im (torch.Tensor): Input image tensor in BCHW format, normalized to [0, 1].

    Returns:
        (list[np.ndarray]): Model predictions as a list of numpy arrays, one per output handle.
    """
    self.input_handle.copy_from_cpu(im.cpu().numpy().astype(np.float32))
    self.predictor.run()
    return [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]

def load_model(self, weight: str | Path) -> None:
    """Load a Baidu PaddlePaddle model from a directory containing .json and .pdiparams files.

    Args:
        weight (str | Path): Path to the model directory or .pdiparams file.
    """
    cuda = isinstance(self.device, torch.device) and torch.cuda.is_available() and self.device.type != "cpu"
    LOGGER.info(f"Loading {weight} for PaddlePaddle inference...")
    if cuda:
        check_requirements("paddlepaddle-gpu>=3.0.0,!=3.3.0")
    elif ARM64:
        check_requirements("paddlepaddle==3.0.0")
    else:
        check_requirements("paddlepaddle>=3.0.0,!=3.3.0")

    import paddle.inference as pdi

    w = Path(weight)
    model_file, params_file = None, None

    if w.is_dir():
        model_file = next(w.rglob("*.json"), None)
        params_file = next(w.rglob("*.pdiparams"), None)
    elif w.suffix == ".pdiparams":
        model_file = w.with_name("model.json")
        params_file = w

    if not (model_file and params_file and model_file.is_file() and params_file.is_file()):
        raise FileNotFoundError(f"Paddle model not found in {w}. Both .json and .pdiparams files are required.")

    config = pdi.Config(str(model_file), str(params_file))
    if cuda:
        config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=self.device.index or 0)

    self.predictor = pdi.create_predictor(config)
    self.input_handle = self.predictor.get_input_handle(self.predictor.get_input_names()[0])
    self.output_names = self.predictor.get_output_names()

    # Load metadata
    metadata_file = (w if w.is_dir() else w.parent) / "metadata.yaml"
    if metadata_file.exists():
        from ultralytics.utils import YAML

        self.apply_metadata(YAML.load(metadata_file))