Reference for ultralytics/utils/export/openvino.py

def torch2openvino(
    model: torch.nn.Module,
    im: torch.Tensor | list[torch.Tensor] | tuple[torch.Tensor, ...],
    output_dir: Path | str | None = None,
    dynamic: bool = False,
    half: bool = False,
    int8: bool = False,
    calibration_dataset: Any | None = None,
    ignored_scope: dict | None = None,
    prefix: str = "",
) -> Any:
    """Export a PyTorch model to OpenVINO format with optional INT8 quantization.

    Args:
        model (torch.nn.Module): The model to export (may be NMS-wrapped).
        im (torch.Tensor | list[torch.Tensor] | tuple[torch.Tensor, ...]): Example input tensor(s) for tracing.
        output_dir (Path | str | None): Directory to save the exported OpenVINO model.
        dynamic (bool): Whether to use dynamic input shapes.
        half (bool): Whether to compress to FP16.
        int8 (bool): Whether to apply INT8 quantization.
        calibration_dataset (nncf.Dataset | None): Dataset for INT8 calibration (required when ``int8=True``).
        ignored_scope (dict | None): Kwargs passed to ``nncf.IgnoredScope`` for head patterns.
        prefix (str): Prefix for log messages.

    Returns:
        (ov.Model): The converted OpenVINO model.
    """
    import openvino as ov

    LOGGER.info(f"\n{prefix} starting export with openvino {ov.__version__}...")

    input_shape = [i.shape for i in im] if isinstance(im, (list, tuple)) else im.shape
    ov_model = ov.convert_model(model, input=None if dynamic else input_shape, example_input=im)
    if int8:
        import nncf

        ov_model = nncf.quantize(
            model=ov_model,
            calibration_dataset=calibration_dataset,
            preset=nncf.QuantizationPreset.MIXED,
            ignored_scope=ignored_scope,
        )

    if output_dir is not None:
        output_dir = Path(output_dir)
        output_dir.mkdir(parents=True, exist_ok=True)
        output_file = output_dir / "model.xml"
        ov.save_model(ov_model, output_file, compress_to_fp16=half)
    return ov_model