Reference for ultralytics/nn/backends/mnn.py#

class MNNBackend(BaseBackend):
    """MNN (Mobile Neural Network) inference backend.

    Loads and runs inference with MNN models (.mnn files) using the Alibaba MNN framework. Optimized for mobile and edge
    deployment with configurable thread count and precision.
    """

def forward(self, im: torch.Tensor) -> list:
    """Run inference using the MNN runtime.

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

    Returns:
        (list): Model predictions as a list of numpy arrays.
    """
    input_var = self.expr.const(im.data_ptr(), im.shape)
    output_var = self.net.onForward([input_var])
    # NOTE: need this copy(), or it'd get incorrect results on ARM devices
    if output_var:
        return [x.read().copy() for x in output_var]
    if self.metadata.get("args", {}).get("nms") and self.task in {"detect", "pose"}:
        return [np.empty((im.shape[0], 0, 6))]
    raise RuntimeError("Alibaba MNN inference returned no output tensors.")

def load_model(self, weight: str | Path) -> None:
    """Load an Alibaba MNN model from a .mnn file.

    Args:
        weight (str | Path): Path to the .mnn model file.
    """
    LOGGER.info(f"Loading {weight} for MNN inference...")
    check_requirements("MNN")
    import MNN

    config = {"precision": "low", "backend": "CPU", "numThread": (os.cpu_count() + 1) // 2}
    rt = MNN.nn.create_runtime_manager((config,))
    self.net = MNN.nn.load_module_from_file(weight, [], [], runtime_manager=rt, rearrange=True)
    self.expr = MNN.expr

    # Load metadata from bizCode
    info = self.net.get_info()
    if "bizCode" in info:
        try:
            self.apply_metadata(json.loads(info["bizCode"]))
        except json.JSONDecodeError:
            pass