Reference for ultralytics/engine/exporter.py

class Exporter:
    """A class for exporting YOLO models to various formats.

    This class provides functionality to export YOLO models to different formats including ONNX, TensorRT, CoreML,
    TensorFlow, and others. It handles format validation, device selection, model preparation, and the actual export
    process for each supported format.

    Attributes:
        args (SimpleNamespace): Configuration arguments for the exporter.
        callbacks (dict): Dictionary of callback functions for different export events.
        im (torch.Tensor): Input tensor for model inference during export.
        model (torch.nn.Module): The YOLO model to be exported.
        file (Path): Path to the model file being exported.
        output_shape (tuple): Shape of the model output tensor(s).
        pretty_name (str): Formatted model name for display purposes.
        metadata (dict): Model metadata including description, author, version, etc.
        device (torch.device): Device on which the model is loaded.
        imgsz (tuple): Input image size for the model.

    Methods:
        __call__: Main export method that handles the export process.
        get_int8_calibration_dataloader: Build dataloader for INT8 calibration.
        export_torchscript: Export model to TorchScript format.
        export_onnx: Export model to ONNX format.
        export_openvino: Export model to OpenVINO format.
        export_paddle: Export model to PaddlePaddle format.
        export_mnn: Export model to MNN format.
        export_ncnn: Export model to NCNN format.
        export_coreml: Export model to CoreML format.
        export_engine: Export model to TensorRT format.
        export_saved_model: Export model to TensorFlow SavedModel format.
        export_pb: Export model to TensorFlow GraphDef format.
        export_tflite: Export model to TensorFlow Lite format.
        export_edgetpu: Export model to Edge TPU format.
        export_tfjs: Export model to TensorFlow.js format.
        export_rknn: Export model to RKNN format.
        export_imx: Export model to IMX format.

    Examples:
        Export a YOLOv8 model to ONNX format
        >>> from ultralytics.engine.exporter import Exporter
        >>> exporter = Exporter()
        >>> exporter(model="yolov8n.pt")  # exports to yolov8n.onnx

        Export with specific arguments
        >>> args = {"format": "onnx", "dynamic": True, "half": True}
        >>> exporter = Exporter(overrides=args)
        >>> exporter(model="yolov8n.pt")
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
        """Initialize the Exporter class.

        Args:
            cfg (str, optional): Path to a configuration file.
            overrides (dict, optional): Configuration overrides.
            _callbacks (dict, optional): Dictionary of callback functions.
        """
        self.args = get_cfg(cfg, overrides)
        self.callbacks = _callbacks or callbacks.get_default_callbacks()
        callbacks.add_integration_callbacks(self)

def __call__(self, model=None) -> str:
    """Return list of exported files/dirs after running callbacks."""
    t = time.time()
    fmt = self.args.format.lower()  # to lowercase
    if fmt in {"tensorrt", "trt"}:  # 'engine' aliases
        fmt = "engine"
    if fmt in {"mlmodel", "mlpackage", "mlprogram", "apple", "ios", "coreml"}:  # 'coreml' aliases
        fmt = "coreml"
    fmts_dict = export_formats()
    fmts = tuple(fmts_dict["Argument"][1:])  # available export formats
    if fmt not in fmts:
        import difflib

        # Get the closest match if format is invalid
        matches = difflib.get_close_matches(fmt, fmts, n=1, cutoff=0.6)  # 60% similarity required to match
        if not matches:
            msg = "Model is already in PyTorch format." if fmt == "pt" else f"Invalid export format='{fmt}'."
            raise ValueError(f"{msg} Valid formats are {fmts}")
        LOGGER.warning(f"Invalid export format='{fmt}', updating to format='{matches[0]}'")
        fmt = matches[0]
    flags = [x == fmt for x in fmts]
    if sum(flags) != 1:
        raise ValueError(f"Invalid export format='{fmt}'. Valid formats are {fmts}")
    (
        jit,
        onnx,
        xml,
        engine,
        coreml,
        saved_model,
        pb,
        tflite,
        edgetpu,
        tfjs,
        paddle,
        mnn,
        ncnn,
        imx,
        rknn,
        executorch,
    ) = flags  # export booleans

    is_tf_format = any((saved_model, pb, tflite, edgetpu, tfjs))

    # Device
    dla = None
    if engine and self.args.device is None:
        LOGGER.warning("TensorRT requires GPU export, automatically assigning device=0")
        self.args.device = "0"
    if engine and "dla" in str(self.args.device):  # convert int/list to str first
        dla = self.args.device.rsplit(":", 1)[-1]
        self.args.device = "0"  # update device to "0"
        assert dla in {"0", "1"}, f"Expected self.args.device='dla:0' or 'dla:1, but got {self.args.device}."
    if imx and self.args.device is None and torch.cuda.is_available():
        LOGGER.warning("Exporting on CPU while CUDA is available, setting device=0 for faster export on GPU.")
        self.args.device = "0"  # update device to "0"
    self.device = select_device("cpu" if self.args.device is None else self.args.device)

    # Argument compatibility checks
    fmt_keys = fmts_dict["Arguments"][flags.index(True) + 1]
    validate_args(fmt, self.args, fmt_keys)
    if imx:
        if not self.args.int8:
            LOGGER.warning("IMX export requires int8=True, setting int8=True.")
            self.args.int8 = True
        if not self.args.nms and model.task in {"detect", "pose"}:
            LOGGER.warning("IMX export requires nms=True, setting nms=True.")
            self.args.nms = True
        if model.task not in {"detect", "pose", "classify"}:
            raise ValueError("IMX export only supported for detection, pose estimation, and classification models.")
    if not hasattr(model, "names"):
        model.names = default_class_names()
    model.names = check_class_names(model.names)
    if self.args.half and self.args.int8:
        LOGGER.warning("half=True and int8=True are mutually exclusive, setting half=False.")
        self.args.half = False
    if self.args.half and (onnx or jit) and self.device.type == "cpu":
        LOGGER.warning("half=True only compatible with GPU export, i.e. use device=0, setting half=False.")
        self.args.half = False
    self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2)  # check image size
    if self.args.optimize:
        assert not ncnn, "optimize=True not compatible with format='ncnn', i.e. use optimize=False"
        assert self.device.type == "cpu", "optimize=True not compatible with cuda devices, i.e. use device='cpu'"
    if rknn:
        if not self.args.name:
            LOGGER.warning(
                "Rockchip RKNN export requires a missing 'name' arg for processor type. "
                "Using default name='rk3588'."
            )
            self.args.name = "rk3588"
        self.args.name = self.args.name.lower()
        assert self.args.name in RKNN_CHIPS, (
            f"Invalid processor name '{self.args.name}' for Rockchip RKNN export. Valid names are {RKNN_CHIPS}."
        )
    if self.args.nms:
        assert not isinstance(model, ClassificationModel), "'nms=True' is not valid for classification models."
        assert not tflite or not ARM64 or not LINUX, "TFLite export with NMS unsupported on ARM64 Linux"
        assert not is_tf_format or TORCH_1_13, "TensorFlow exports with NMS require torch>=1.13"
        assert not onnx or TORCH_1_13, "ONNX export with NMS requires torch>=1.13"
        if getattr(model, "end2end", False) or isinstance(model.model[-1], RTDETRDecoder):
            LOGGER.warning("'nms=True' is not available for end2end models. Forcing 'nms=False'.")
            self.args.nms = False
        self.args.conf = self.args.conf or 0.25  # set conf default value for nms export
    if (engine or coreml or self.args.nms) and self.args.dynamic and self.args.batch == 1:
        LOGGER.warning(
            f"'dynamic=True' model with '{'nms=True' if self.args.nms else f'format={self.args.format}'}' requires max batch size, i.e. 'batch=16'"
        )
    if edgetpu:
        if not LINUX or ARM64:
            raise SystemError(
                "Edge TPU export only supported on non-aarch64 Linux. See https://coral.ai/docs/edgetpu/compiler"
            )
        elif self.args.batch != 1:  # see github.com/ultralytics/ultralytics/pull/13420
            LOGGER.warning("Edge TPU export requires batch size 1, setting batch=1.")
            self.args.batch = 1
    if isinstance(model, WorldModel):
        LOGGER.warning(
            "YOLOWorld (original version) export is not supported to any format. "
            "YOLOWorldv2 models (i.e. 'yolov8s-worldv2.pt') only support export to "
            "(torchscript, onnx, openvino, engine, coreml) formats. "
            "See https://docs.ultralytics.com/models/yolo-world for details."
        )
        model.clip_model = None  # openvino int8 export error: https://github.com/ultralytics/ultralytics/pull/18445
    if self.args.int8 and not self.args.data:
        self.args.data = DEFAULT_CFG.data or TASK2DATA[getattr(model, "task", "detect")]  # assign default data
        LOGGER.warning(
            f"INT8 export requires a missing 'data' arg for calibration. Using default 'data={self.args.data}'."
        )
    if tfjs and (ARM64 and LINUX):
        raise SystemError("TF.js exports are not currently supported on ARM64 Linux")
    # Recommend OpenVINO if export and Intel CPU
    if SETTINGS.get("openvino_msg"):
        if is_intel():
            LOGGER.info(
                "💡 ProTip: Export to OpenVINO format for best performance on Intel hardware."
                " Learn more at https://docs.ultralytics.com/integrations/openvino/"
            )
        SETTINGS["openvino_msg"] = False

    # Input
    im = torch.zeros(self.args.batch, model.yaml.get("channels", 3), *self.imgsz).to(self.device)
    file = Path(
        getattr(model, "pt_path", None) or getattr(model, "yaml_file", None) or model.yaml.get("yaml_file", "")
    )
    if file.suffix in {".yaml", ".yml"}:
        file = Path(file.name)

    # Update model
    model = deepcopy(model).to(self.device)
    for p in model.parameters():
        p.requires_grad = False
    model.eval()
    model.float()
    model = model.fuse()

    if imx:
        from ultralytics.utils.export.imx import FXModel

        model = FXModel(model, self.imgsz)
    if tflite or edgetpu:
        from ultralytics.utils.export.tensorflow import tf_wrapper

        model = tf_wrapper(model)
    for m in model.modules():
        if isinstance(m, Classify):
            m.export = True
        if isinstance(m, (Detect, RTDETRDecoder)):  # includes all Detect subclasses like Segment, Pose, OBB
            m.dynamic = self.args.dynamic
            m.export = True
            m.format = self.args.format
            m.max_det = self.args.max_det
            m.xyxy = self.args.nms and not coreml
            if hasattr(model, "pe") and hasattr(m, "fuse"):  # for YOLOE models
                m.fuse(model.pe.to(self.device))
        elif isinstance(m, C2f) and not is_tf_format:
            # EdgeTPU does not support FlexSplitV while split provides cleaner ONNX graph
            m.forward = m.forward_split

    y = None
    for _ in range(2):  # dry runs
        y = NMSModel(model, self.args)(im) if self.args.nms and not coreml and not imx else model(im)
    if self.args.half and (onnx or jit) and self.device.type != "cpu":
        im, model = im.half(), model.half()  # to FP16

    # Filter warnings
    warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)  # suppress TracerWarning
    warnings.filterwarnings("ignore", category=UserWarning)  # suppress shape prim::Constant missing ONNX warning
    warnings.filterwarnings("ignore", category=DeprecationWarning)  # suppress CoreML np.bool deprecation warning

    # Assign
    self.im = im
    self.model = model
    self.file = file
    self.output_shape = (
        tuple(y.shape)
        if isinstance(y, torch.Tensor)
        else tuple(tuple(x.shape if isinstance(x, torch.Tensor) else []) for x in y)
    )
    self.pretty_name = Path(self.model.yaml.get("yaml_file", self.file)).stem.replace("yolo", "YOLO")
    data = model.args["data"] if hasattr(model, "args") and isinstance(model.args, dict) else ""
    description = f"Ultralytics {self.pretty_name} model {f'trained on {data}' if data else ''}"
    self.metadata = {
        "description": description,
        "author": "Ultralytics",
        "date": datetime.now().isoformat(),
        "version": __version__,
        "license": "AGPL-3.0 License (https://ultralytics.com/license)",
        "docs": "https://docs.ultralytics.com",
        "stride": int(max(model.stride)),
        "task": model.task,
        "batch": self.args.batch,
        "imgsz": self.imgsz,
        "names": model.names,
        "args": {k: v for k, v in self.args if k in fmt_keys},
        "channels": model.yaml.get("channels", 3),
    }  # model metadata
    if dla is not None:
        self.metadata["dla"] = dla  # make sure `AutoBackend` uses correct dla device if it has one
    if model.task == "pose":
        self.metadata["kpt_shape"] = model.model[-1].kpt_shape
        if hasattr(model, "kpt_names"):
            self.metadata["kpt_names"] = model.kpt_names

    LOGGER.info(
        f"\n{colorstr('PyTorch:')} starting from '{file}' with input shape {tuple(im.shape)} BCHW and "
        f"output shape(s) {self.output_shape} ({file_size(file):.1f} MB)"
    )
    self.run_callbacks("on_export_start")
    # Exports
    f = [""] * len(fmts)  # exported filenames
    if jit:  # TorchScript
        f[0] = self.export_torchscript()
    if engine:  # TensorRT required before ONNX
        f[1] = self.export_engine(dla=dla)
    if onnx:  # ONNX
        f[2] = self.export_onnx()
    if xml:  # OpenVINO
        f[3] = self.export_openvino()
    if coreml:  # CoreML
        f[4] = self.export_coreml()
    if is_tf_format:  # TensorFlow formats
        self.args.int8 |= edgetpu
        f[5], keras_model = self.export_saved_model()
        if pb or tfjs:  # pb prerequisite to tfjs
            f[6] = self.export_pb(keras_model=keras_model)
        if tflite:
            f[7] = self.export_tflite()
        if edgetpu:
            f[8] = self.export_edgetpu(tflite_model=Path(f[5]) / f"{self.file.stem}_full_integer_quant.tflite")
        if tfjs:
            f[9] = self.export_tfjs()
    if paddle:  # PaddlePaddle
        f[10] = self.export_paddle()
    if mnn:  # MNN
        f[11] = self.export_mnn()
    if ncnn:  # NCNN
        f[12] = self.export_ncnn()
    if imx:
        f[13] = self.export_imx()
    if rknn:
        f[14] = self.export_rknn()
    if executorch:
        f[15] = self.export_executorch()

    # Finish
    f = [str(x) for x in f if x]  # filter out '' and None
    if any(f):
        f = str(Path(f[-1]))
        square = self.imgsz[0] == self.imgsz[1]
        s = (
            ""
            if square
            else f"WARNING ⚠️ non-PyTorch val requires square images, 'imgsz={self.imgsz}' will not "
            f"work. Use export 'imgsz={max(self.imgsz)}' if val is required."
        )
        imgsz = self.imgsz[0] if square else str(self.imgsz)[1:-1].replace(" ", "")
        predict_data = f"data={data}" if model.task == "segment" and pb else ""
        q = "int8" if self.args.int8 else "half" if self.args.half else ""  # quantization
        LOGGER.info(
            f"\nExport complete ({time.time() - t:.1f}s)"
            f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
            f"\nPredict:         yolo predict task={model.task} model={f} imgsz={imgsz} {q} {predict_data}"
            f"\nValidate:        yolo val task={model.task} model={f} imgsz={imgsz} data={data} {q} {s}"
            f"\nVisualize:       https://netron.app"
        )

    self.run_callbacks("on_export_end")
    return f  # return list of exported files/dirs

def _add_tflite_metadata(self, file):
    """Add metadata to *.tflite models per https://ai.google.dev/edge/litert/models/metadata."""
    import zipfile

    with zipfile.ZipFile(file, "a", zipfile.ZIP_DEFLATED) as zf:
        zf.writestr("metadata.json", json.dumps(self.metadata, indent=2))

def _pipeline_coreml(self, model, weights_dir=None, prefix=colorstr("CoreML Pipeline:")):
    """Create CoreML pipeline with NMS for YOLO detection models."""
    import coremltools as ct

    LOGGER.info(f"{prefix} starting pipeline with coremltools {ct.__version__}...")

    # Output shapes
    spec = model.get_spec()
    outs = list(iter(spec.description.output))
    if self.args.format == "mlmodel":  # mlmodel doesn't infer shapes automatically
        outs[0].type.multiArrayType.shape[:] = self.output_shape[2], self.output_shape[1] - 4
        outs[1].type.multiArrayType.shape[:] = self.output_shape[2], 4

    # Checks
    names = self.metadata["names"]
    nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height
    nc = outs[0].type.multiArrayType.shape[-1]
    if len(names) != nc:  # Hack fix for MLProgram NMS bug https://github.com/ultralytics/ultralytics/issues/22309
        names = {**names, **{i: str(i) for i in range(len(names), nc)}}

    # Model from spec
    model = ct.models.MLModel(spec, weights_dir=weights_dir)

    # Create NMS protobuf
    nms_spec = ct.proto.Model_pb2.Model()
    nms_spec.specificationVersion = spec.specificationVersion
    for i in range(len(outs)):
        decoder_output = model._spec.description.output[i].SerializeToString()
        nms_spec.description.input.add()
        nms_spec.description.input[i].ParseFromString(decoder_output)
        nms_spec.description.output.add()
        nms_spec.description.output[i].ParseFromString(decoder_output)

    output_names = ["confidence", "coordinates"]
    for i, name in enumerate(output_names):
        nms_spec.description.output[i].name = name

    for i, out in enumerate(outs):
        ma_type = nms_spec.description.output[i].type.multiArrayType
        ma_type.shapeRange.sizeRanges.add()
        ma_type.shapeRange.sizeRanges[0].lowerBound = 0
        ma_type.shapeRange.sizeRanges[0].upperBound = -1
        ma_type.shapeRange.sizeRanges.add()
        ma_type.shapeRange.sizeRanges[1].lowerBound = out.type.multiArrayType.shape[-1]
        ma_type.shapeRange.sizeRanges[1].upperBound = out.type.multiArrayType.shape[-1]
        del ma_type.shape[:]

    nms = nms_spec.nonMaximumSuppression
    nms.confidenceInputFeatureName = outs[0].name  # 1x507x80
    nms.coordinatesInputFeatureName = outs[1].name  # 1x507x4
    nms.confidenceOutputFeatureName = output_names[0]
    nms.coordinatesOutputFeatureName = output_names[1]
    nms.iouThresholdInputFeatureName = "iouThreshold"
    nms.confidenceThresholdInputFeatureName = "confidenceThreshold"
    nms.iouThreshold = self.args.iou
    nms.confidenceThreshold = self.args.conf
    nms.pickTop.perClass = True
    nms.stringClassLabels.vector.extend(names.values())
    nms_model = ct.models.MLModel(nms_spec)

    # Pipeline models together
    pipeline = ct.models.pipeline.Pipeline(
        input_features=[
            ("image", ct.models.datatypes.Array(3, ny, nx)),
            ("iouThreshold", ct.models.datatypes.Double()),
            ("confidenceThreshold", ct.models.datatypes.Double()),
        ],
        output_features=output_names,
    )
    pipeline.add_model(model)
    pipeline.add_model(nms_model)

    # Correct datatypes
    pipeline.spec.description.input[0].ParseFromString(model._spec.description.input[0].SerializeToString())
    pipeline.spec.description.output[0].ParseFromString(nms_model._spec.description.output[0].SerializeToString())
    pipeline.spec.description.output[1].ParseFromString(nms_model._spec.description.output[1].SerializeToString())

    # Update metadata
    pipeline.spec.specificationVersion = spec.specificationVersion
    pipeline.spec.description.metadata.userDefined.update(
        {"IoU threshold": str(nms.iouThreshold), "Confidence threshold": str(nms.confidenceThreshold)}
    )

    # Save the model
    model = ct.models.MLModel(pipeline.spec, weights_dir=weights_dir)
    model.input_description["image"] = "Input image"
    model.input_description["iouThreshold"] = f"(optional) IoU threshold override (default: {nms.iouThreshold})"
    model.input_description["confidenceThreshold"] = (
        f"(optional) Confidence threshold override (default: {nms.confidenceThreshold})"
    )
    model.output_description["confidence"] = 'Boxes × Class confidence (see user-defined metadata "classes")'
    model.output_description["coordinates"] = "Boxes × [x, y, width, height] (relative to image size)"
    LOGGER.info(f"{prefix} pipeline success")
    return model

def add_callback(self, event: str, callback):
    """Append the given callback to the specified event."""
    self.callbacks[event].append(callback)

@try_export
def export_coreml(self, prefix=colorstr("CoreML:")):
    """Export YOLO model to CoreML format."""
    mlmodel = self.args.format.lower() == "mlmodel"  # legacy *.mlmodel export format requested
    check_requirements("coremltools>=8.0")
    import coremltools as ct

    LOGGER.info(f"\n{prefix} starting export with coremltools {ct.__version__}...")
    assert not WINDOWS, "CoreML export is not supported on Windows, please run on macOS or Linux."
    assert TORCH_1_11, "CoreML export requires torch>=1.11"
    if self.args.batch > 1:
        assert self.args.dynamic, (
            "batch sizes > 1 are not supported without 'dynamic=True' for CoreML export. Please retry at 'dynamic=True'."
        )
    if self.args.dynamic:
        assert not self.args.nms, (
            "'nms=True' cannot be used together with 'dynamic=True' for CoreML export. Please disable one of them."
        )
        assert self.model.task != "classify", "'dynamic=True' is not supported for CoreML classification models."
    f = self.file.with_suffix(".mlmodel" if mlmodel else ".mlpackage")
    if f.is_dir():
        shutil.rmtree(f)

    classifier_config = None
    if self.model.task == "classify":
        classifier_config = ct.ClassifierConfig(list(self.model.names.values()))
        model = self.model
    elif self.model.task == "detect":
        model = IOSDetectModel(self.model, self.im, mlprogram=not mlmodel) if self.args.nms else self.model
    else:
        if self.args.nms:
            LOGGER.warning(f"{prefix} 'nms=True' is only available for Detect models like 'yolo11n.pt'.")
            # TODO CoreML Segment and Pose model pipelining
        model = self.model
    ts = torch.jit.trace(model.eval(), self.im, strict=False)  # TorchScript model

    if self.args.dynamic:
        input_shape = ct.Shape(
            shape=(
                ct.RangeDim(lower_bound=1, upper_bound=self.args.batch, default=1),
                self.im.shape[1],
                ct.RangeDim(lower_bound=32, upper_bound=self.imgsz[0] * 2, default=self.imgsz[0]),
                ct.RangeDim(lower_bound=32, upper_bound=self.imgsz[1] * 2, default=self.imgsz[1]),
            )
        )
        inputs = [ct.TensorType("image", shape=input_shape)]
    else:
        inputs = [ct.ImageType("image", shape=self.im.shape, scale=1 / 255, bias=[0.0, 0.0, 0.0])]

    # Based on apple's documentation it is better to leave out the minimum_deployment target and let that get set
    # Internally based on the model conversion and output type.
    # Setting minimum_depoloyment_target >= iOS16 will require setting compute_precision=ct.precision.FLOAT32.
    # iOS16 adds in better support for FP16, but none of the CoreML NMS specifications handle FP16 as input.
    ct_model = ct.convert(
        ts,
        inputs=inputs,
        classifier_config=classifier_config,
        convert_to="neuralnetwork" if mlmodel else "mlprogram",
    )
    bits, mode = (8, "kmeans") if self.args.int8 else (16, "linear") if self.args.half else (32, None)
    if bits < 32:
        if "kmeans" in mode:
            check_requirements("scikit-learn")  # scikit-learn package required for k-means quantization
        if mlmodel:
            ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
        elif bits == 8:  # mlprogram already quantized to FP16
            import coremltools.optimize.coreml as cto

            op_config = cto.OpPalettizerConfig(mode="kmeans", nbits=bits, weight_threshold=512)
            config = cto.OptimizationConfig(global_config=op_config)
            ct_model = cto.palettize_weights(ct_model, config=config)
    if self.args.nms and self.model.task == "detect":
        ct_model = self._pipeline_coreml(ct_model, weights_dir=None if mlmodel else ct_model.weights_dir)

    m = self.metadata  # metadata dict
    ct_model.short_description = m.pop("description")
    ct_model.author = m.pop("author")
    ct_model.license = m.pop("license")
    ct_model.version = m.pop("version")
    ct_model.user_defined_metadata.update({k: str(v) for k, v in m.items()})
    if self.model.task == "classify":
        ct_model.user_defined_metadata.update({"com.apple.coreml.model.preview.type": "imageClassifier"})

    try:
        ct_model.save(str(f))  # save *.mlpackage
    except Exception as e:
        LOGGER.warning(
            f"{prefix} CoreML export to *.mlpackage failed ({e}), reverting to *.mlmodel export. "
            f"Known coremltools Python 3.11 and Windows bugs https://github.com/apple/coremltools/issues/1928."
        )
        f = f.with_suffix(".mlmodel")
        ct_model.save(str(f))
    return f

@try_export
def export_edgetpu(self, tflite_model="", prefix=colorstr("Edge TPU:")):
    """Export YOLO model to Edge TPU format https://coral.ai/docs/edgetpu/models-intro/."""
    cmd = "edgetpu_compiler --version"
    help_url = "https://coral.ai/docs/edgetpu/compiler/"
    assert LINUX, f"export only supported on Linux. See {help_url}"
    if subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, shell=True).returncode != 0:
        LOGGER.info(f"\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}")
        sudo = "sudo " if is_sudo_available() else ""
        for c in (
            f"{sudo}mkdir -p /etc/apt/keyrings",
            f"curl -fsSL https://packages.cloud.google.com/apt/doc/apt-key.gpg | {sudo}gpg --dearmor -o /etc/apt/keyrings/google.gpg",
            f'echo "deb [signed-by=/etc/apt/keyrings/google.gpg] https://packages.cloud.google.com/apt coral-edgetpu-stable main" | {sudo}tee /etc/apt/sources.list.d/coral-edgetpu.list',
            f"{sudo}apt-get update",
            f"{sudo}apt-get install -y edgetpu-compiler",
        ):
            subprocess.run(c, shell=True, check=True)

    ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().rsplit(maxsplit=1)[-1]
    LOGGER.info(f"\n{prefix} starting export with Edge TPU compiler {ver}...")
    tflite2edgetpu(tflite_file=tflite_model, output_dir=tflite_model.parent, prefix=prefix)
    f = str(tflite_model).replace(".tflite", "_edgetpu.tflite")  # Edge TPU model
    self._add_tflite_metadata(f)
    return f

@try_export
def export_engine(self, dla=None, prefix=colorstr("TensorRT:")):
    """Export YOLO model to TensorRT format https://developer.nvidia.com/tensorrt."""
    assert self.im.device.type != "cpu", "export running on CPU but must be on GPU, i.e. use 'device=0'"
    f_onnx = self.export_onnx()  # run before TRT import https://github.com/ultralytics/ultralytics/issues/7016

    try:
        import tensorrt as trt
    except ImportError:
        if LINUX:
            cuda_version = torch.version.cuda.split(".")[0]
            check_requirements(f"tensorrt-cu{cuda_version}>7.0.0,!=10.1.0")
        import tensorrt as trt
    check_version(trt.__version__, ">=7.0.0", hard=True)
    check_version(trt.__version__, "!=10.1.0", msg="https://github.com/ultralytics/ultralytics/pull/14239")

    # Setup and checks
    LOGGER.info(f"\n{prefix} starting export with TensorRT {trt.__version__}...")
    assert Path(f_onnx).exists(), f"failed to export ONNX file: {f_onnx}"
    f = self.file.with_suffix(".engine")  # TensorRT engine file
    onnx2engine(
        f_onnx,
        f,
        self.args.workspace,
        self.args.half,
        self.args.int8,
        self.args.dynamic,
        self.im.shape,
        dla=dla,
        dataset=self.get_int8_calibration_dataloader(prefix) if self.args.int8 else None,
        metadata=self.metadata,
        verbose=self.args.verbose,
        prefix=prefix,
    )

    return f

@try_export
def export_executorch(self, prefix=colorstr("ExecuTorch:")):
    """Exports a model to ExecuTorch (.pte) format into a dedicated directory and saves the required metadata,
    following Ultralytics conventions.
    """
    LOGGER.info(f"\n{prefix} starting export with ExecuTorch...")
    assert TORCH_2_9, f"ExecuTorch export requires torch>=2.9.0 but torch=={TORCH_VERSION} is installed"
    # TorchAO release compatibility table bug https://github.com/pytorch/ao/issues/2919
    # Setuptools bug: https://github.com/pypa/setuptools/issues/4483
    check_requirements("setuptools<71.0.0")  # Setuptools bug: https://github.com/pypa/setuptools/issues/4483
    check_requirements(("executorch==1.0.0", "flatbuffers"))

    import torch
    from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner
    from executorch.exir import to_edge_transform_and_lower

    file_directory = Path(str(self.file).replace(self.file.suffix, "_executorch_model"))
    file_directory.mkdir(parents=True, exist_ok=True)

    file_pte = file_directory / self.file.with_suffix(".pte").name
    sample_inputs = (self.im,)

    et_program = to_edge_transform_and_lower(
        torch.export.export(self.model, sample_inputs), partitioner=[XnnpackPartitioner()]
    ).to_executorch()

    with open(file_pte, "wb") as file:
        file.write(et_program.buffer)

    YAML.save(file_directory / "metadata.yaml", self.metadata)

    return str(file_directory)

@try_export
def export_imx(self, prefix=colorstr("IMX:")):
    """Export YOLO model to IMX format."""
    assert LINUX, (
        "export only supported on Linux. "
        "See https://developer.aitrios.sony-semicon.com/en/raspberrypi-ai-camera/documentation/imx500-converter"
    )
    assert not IS_PYTHON_3_12, "IMX export requires Python>=3.8;<3.12"
    assert not TORCH_2_9, f"IMX export requires PyTorch<2.9. Current PyTorch version is {TORCH_VERSION}."
    if getattr(self.model, "end2end", False):
        raise ValueError("IMX export is not supported for end2end models.")
    check_requirements(
        ("model-compression-toolkit>=2.4.1", "sony-custom-layers>=0.3.0", "edge-mdt-tpc>=1.1.0", "pydantic<=2.11.7")
    )
    check_requirements("imx500-converter[pt]>=3.16.1")  # Separate requirements for imx500-converter
    check_requirements("mct-quantizers>=1.6.0")  # Separate for compatibility with model-compression-toolkit

    # Install Java>=17
    try:
        java_output = subprocess.run(["java", "--version"], check=True, capture_output=True).stdout.decode()
        version_match = re.search(r"(?:openjdk|java) (\d+)", java_output)
        java_version = int(version_match.group(1)) if version_match else 0
        assert java_version >= 17, "Java version too old"
    except (FileNotFoundError, subprocess.CalledProcessError, AssertionError):
        cmd = None
        if IS_UBUNTU or IS_DEBIAN_TRIXIE:
            LOGGER.info(f"\n{prefix} installing Java 21 for Ubuntu...")
            cmd = (["sudo"] if is_sudo_available() else []) + ["apt", "install", "-y", "openjdk-21-jre"]
        elif IS_RASPBERRYPI or IS_DEBIAN_BOOKWORM:
            LOGGER.info(f"\n{prefix} installing Java 17 for Raspberry Pi or Debian ...")
            cmd = (["sudo"] if is_sudo_available() else []) + ["apt", "install", "-y", "openjdk-17-jre"]

        if cmd:
            subprocess.run(cmd, check=True)

    return torch2imx(
        self.model,
        self.file,
        self.args.conf,
        self.args.iou,
        self.args.max_det,
        metadata=self.metadata,
        dataset=self.get_int8_calibration_dataloader(prefix),
        prefix=prefix,
    )

@try_export
def export_mnn(self, prefix=colorstr("MNN:")):
    """Export YOLO model to MNN format using MNN https://github.com/alibaba/MNN."""
    f_onnx = self.export_onnx()  # get onnx model first

    check_requirements("MNN>=2.9.6")
    import MNN
    from MNN.tools import mnnconvert

    # Setup and checks
    LOGGER.info(f"\n{prefix} starting export with MNN {MNN.version()}...")
    assert Path(f_onnx).exists(), f"failed to export ONNX file: {f_onnx}"
    f = str(self.file.with_suffix(".mnn"))  # MNN model file
    args = ["", "-f", "ONNX", "--modelFile", f_onnx, "--MNNModel", f, "--bizCode", json.dumps(self.metadata)]
    if self.args.int8:
        args.extend(("--weightQuantBits", "8"))
    if self.args.half:
        args.append("--fp16")
    mnnconvert.convert(args)
    # remove scratch file for model convert optimize
    convert_scratch = Path(self.file.parent / ".__convert_external_data.bin")
    if convert_scratch.exists():
        convert_scratch.unlink()
    return f

@try_export
def export_ncnn(self, prefix=colorstr("NCNN:")):
    """Export YOLO model to NCNN format using PNNX https://github.com/pnnx/pnnx."""
    check_requirements("ncnn", cmds="--no-deps")  # no deps to avoid installing opencv-python
    check_requirements("pnnx")
    import ncnn
    import pnnx

    LOGGER.info(f"\n{prefix} starting export with NCNN {ncnn.__version__} and PNNX {pnnx.__version__}...")
    f = Path(str(self.file).replace(self.file.suffix, f"_ncnn_model{os.sep}"))

    ncnn_args = dict(
        ncnnparam=(f / "model.ncnn.param").as_posix(),
        ncnnbin=(f / "model.ncnn.bin").as_posix(),
        ncnnpy=(f / "model_ncnn.py").as_posix(),
    )

    pnnx_args = dict(
        ptpath=(f / "model.pt").as_posix(),
        pnnxparam=(f / "model.pnnx.param").as_posix(),
        pnnxbin=(f / "model.pnnx.bin").as_posix(),
        pnnxpy=(f / "model_pnnx.py").as_posix(),
        pnnxonnx=(f / "model.pnnx.onnx").as_posix(),
    )

    f.mkdir(exist_ok=True)  # make ncnn_model directory
    pnnx.export(self.model, inputs=self.im, **ncnn_args, **pnnx_args, fp16=self.args.half, device=self.device.type)

    for f_debug in ("debug.bin", "debug.param", "debug2.bin", "debug2.param", *pnnx_args.values()):
        Path(f_debug).unlink(missing_ok=True)

    YAML.save(f / "metadata.yaml", self.metadata)  # add metadata.yaml
    return str(f)

@try_export
def export_onnx(self, prefix=colorstr("ONNX:")):
    """Export YOLO model to ONNX format."""
    requirements = ["onnx>=1.12.0,<=1.19.1"]
    if self.args.simplify:
        requirements += ["onnxslim>=0.1.71", "onnxruntime" + ("-gpu" if torch.cuda.is_available() else "")]
    check_requirements(requirements)
    import onnx

    opset = self.args.opset or best_onnx_opset(onnx, cuda="cuda" in self.device.type)
    LOGGER.info(f"\n{prefix} starting export with onnx {onnx.__version__} opset {opset}...")
    if self.args.nms:
        assert TORCH_1_13, f"'nms=True' ONNX export requires torch>=1.13 (found torch=={TORCH_VERSION})"

    f = str(self.file.with_suffix(".onnx"))
    output_names = ["output0", "output1"] if self.model.task == "segment" else ["output0"]
    dynamic = self.args.dynamic
    if dynamic:
        dynamic = {"images": {0: "batch", 2: "height", 3: "width"}}  # shape(1,3,640,640)
        if isinstance(self.model, SegmentationModel):
            dynamic["output0"] = {0: "batch", 2: "anchors"}  # shape(1, 116, 8400)
            dynamic["output1"] = {0: "batch", 2: "mask_height", 3: "mask_width"}  # shape(1,32,160,160)
        elif isinstance(self.model, DetectionModel):
            dynamic["output0"] = {0: "batch", 2: "anchors"}  # shape(1, 84, 8400)
        if self.args.nms:  # only batch size is dynamic with NMS
            dynamic["output0"].pop(2)
    if self.args.nms and self.model.task == "obb":
        self.args.opset = opset  # for NMSModel

    with arange_patch(self.args):
        torch2onnx(
            NMSModel(self.model, self.args) if self.args.nms else self.model,
            self.im,
            f,
            opset=opset,
            input_names=["images"],
            output_names=output_names,
            dynamic=dynamic or None,
        )

    # Checks
    model_onnx = onnx.load(f)  # load onnx model

    # Simplify
    if self.args.simplify:
        try:
            import onnxslim

            LOGGER.info(f"{prefix} slimming with onnxslim {onnxslim.__version__}...")
            model_onnx = onnxslim.slim(model_onnx)

        except Exception as e:
            LOGGER.warning(f"{prefix} simplifier failure: {e}")

    # Metadata
    for k, v in self.metadata.items():
        meta = model_onnx.metadata_props.add()
        meta.key, meta.value = k, str(v)

    # IR version
    if getattr(model_onnx, "ir_version", 0) > 10:
        LOGGER.info(f"{prefix} limiting IR version {model_onnx.ir_version} to 10 for ONNXRuntime compatibility...")
        model_onnx.ir_version = 10

    onnx.save(model_onnx, f)
    return f

@try_export
def export_openvino(self, prefix=colorstr("OpenVINO:")):
    """Export YOLO model to OpenVINO format."""
    # OpenVINO <= 2025.1.0 error on macOS 15.4+: https://github.com/openvinotoolkit/openvino/issues/30023"
    check_requirements("openvino>=2025.2.0" if MACOS and MACOS_VERSION >= "15.4" else "openvino>=2024.0.0")
    import openvino as ov

    LOGGER.info(f"\n{prefix} starting export with openvino {ov.__version__}...")
    assert TORCH_2_1, f"OpenVINO export requires torch>=2.1 but torch=={TORCH_VERSION} is installed"
    ov_model = ov.convert_model(
        NMSModel(self.model, self.args) if self.args.nms else self.model,
        input=None if self.args.dynamic else [self.im.shape],
        example_input=self.im,
    )

    def serialize(ov_model, file):
        """Set RT info, serialize, and save metadata YAML."""
        ov_model.set_rt_info("YOLO", ["model_info", "model_type"])
        ov_model.set_rt_info(True, ["model_info", "reverse_input_channels"])
        ov_model.set_rt_info(114, ["model_info", "pad_value"])
        ov_model.set_rt_info([255.0], ["model_info", "scale_values"])
        ov_model.set_rt_info(self.args.iou, ["model_info", "iou_threshold"])
        ov_model.set_rt_info([v.replace(" ", "_") for v in self.model.names.values()], ["model_info", "labels"])
        if self.model.task != "classify":
            ov_model.set_rt_info("fit_to_window_letterbox", ["model_info", "resize_type"])

        ov.save_model(ov_model, file, compress_to_fp16=self.args.half)
        YAML.save(Path(file).parent / "metadata.yaml", self.metadata)  # add metadata.yaml

    if self.args.int8:
        fq = str(self.file).replace(self.file.suffix, f"_int8_openvino_model{os.sep}")
        fq_ov = str(Path(fq) / self.file.with_suffix(".xml").name)
        # INT8 requires nncf, nncf requires packaging>=23.2 https://github.com/openvinotoolkit/nncf/issues/3463
        check_requirements("packaging>=23.2")  # must be installed first to build nncf wheel
        check_requirements("nncf>=2.14.0")
        import nncf

        def transform_fn(data_item) -> np.ndarray:
            """Quantization transform function."""
            data_item: torch.Tensor = data_item["img"] if isinstance(data_item, dict) else data_item
            assert data_item.dtype == torch.uint8, "Input image must be uint8 for the quantization preprocessing"
            im = data_item.numpy().astype(np.float32) / 255.0  # uint8 to fp16/32 and 0-255 to 0.0-1.0
            return np.expand_dims(im, 0) if im.ndim == 3 else im

        # Generate calibration data for integer quantization
        ignored_scope = None
        if isinstance(self.model.model[-1], Detect):
            # Includes all Detect subclasses like Segment, Pose, OBB, WorldDetect, YOLOEDetect
            head_module_name = ".".join(list(self.model.named_modules())[-1][0].split(".")[:2])
            ignored_scope = nncf.IgnoredScope(  # ignore operations
                patterns=[
                    f".*{head_module_name}/.*/Add",
                    f".*{head_module_name}/.*/Sub*",
                    f".*{head_module_name}/.*/Mul*",
                    f".*{head_module_name}/.*/Div*",
                    f".*{head_module_name}\\.dfl.*",
                ],
                types=["Sigmoid"],
            )

        quantized_ov_model = nncf.quantize(
            model=ov_model,
            calibration_dataset=nncf.Dataset(self.get_int8_calibration_dataloader(prefix), transform_fn),
            preset=nncf.QuantizationPreset.MIXED,
            ignored_scope=ignored_scope,
        )
        serialize(quantized_ov_model, fq_ov)
        return fq

    f = str(self.file).replace(self.file.suffix, f"_openvino_model{os.sep}")
    f_ov = str(Path(f) / self.file.with_suffix(".xml").name)

    serialize(ov_model, f_ov)
    return f