Reference for ultralytics/nn/backends/ncnn.py

class NCNNBackend(BaseBackend):

def forward(self, im: torch.Tensor) -> list[np.ndarray]:
    """Run inference using the NCNN runtime.

    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 layer.
    """
    mat_in = self.pyncnn.Mat(im[0].cpu().numpy())
    with self.net.create_extractor() as ex:
        ex.input(self.net.input_names()[0], mat_in)
        # Sort output names as temporary fix for pnnx issue
        y = [np.array(ex.extract(x)[1])[None] for x in sorted(self.net.output_names())]
    return y

def load_model(self, weight: str | Path) -> None:
    """Load an NCNN model from a .param/.bin file pair or model directory.

    Args:
        weight (str | Path): Path to the .param file or directory containing NCNN model files.
    """
    LOGGER.info(f"Loading {weight} for NCNN inference...")
    check_requirements("ncnn", cmds="--no-deps")
    import ncnn as pyncnn

    self.pyncnn = pyncnn
    self.net = pyncnn.Net()

    # Setup Vulkan if available
    if isinstance(self.device, str) and self.device.startswith("vulkan"):
        self.net.opt.use_vulkan_compute = True
        self.net.set_vulkan_device(int(self.device.split(":")[1]))
        self.device = torch.device("cpu")
    else:
        self.net.opt.use_vulkan_compute = False

    w = Path(weight)
    if not w.is_file():
        w = next(w.glob("*.param"))

    self.net.load_param(str(w))
    self.net.load_model(str(w.with_suffix(".bin")))

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

        self.apply_metadata(YAML.load(metadata_file))