Reference for ultralytics/nn/backends/mnn.py

class MNNBackend(BaseBackend):

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
    return [x.read().copy() for x in output_var]

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