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# Reference for `ultralytics/nn/autobackend.py`

Note

This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/autobackend.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!

## `` ultralytics.nn.autobackend.AutoBackend

    AutoBackend(
        model: str | Module = "yolo11n.pt",
        device: device = torch.device("cpu"),
        dnn: bool = False,
        data: str | Path | None = None,
        fp16: bool = False,
        fuse: bool = True,
        verbose: bool = True,
    )

Bases: `Module`

Handle dynamic backend selection for running inference using Ultralytics YOLO models.

The AutoBackend class is designed to provide an abstraction layer for various inference engines. It supports a wide range of formats, each with specific naming conventions as outlined below:

    Supported Formats and Naming Conventions:
        | Format                | File Suffix       |
        | --------------------- | ----------------- |
        | PyTorch               | *.pt              |
        | TorchScript           | *.torchscript     |
        | ONNX Runtime          | *.onnx            |
        | ONNX OpenCV DNN       | *.onnx (dnn=True) |
        | OpenVINO              | *openvino_model/  |
        | CoreML                | *.mlpackage       |
        | TensorRT              | *.engine          |
        | TensorFlow SavedModel | *_saved_model/    |
        | TensorFlow GraphDef   | *.pb              |
        | TensorFlow Lite       | *.tflite          |
        | TensorFlow Edge TPU   | *_edgetpu.tflite  |
        | PaddlePaddle          | *_paddle_model/   |
        | MNN                   | *.mnn             |
        | NCNN                  | *_ncnn_model/     |
        | IMX                   | *_imx_model/      |
        | RKNN                  | *_rknn_model/     |

Attributes:

Name | Type | Description
--- | --- | ---
`model` | `Module` | The loaded YOLO model.
`device` | `device` | The device (CPU or GPU) on which the model is loaded.
`task` | `str` | The type of task the model performs (detect, segment, classify, pose).
`names` | `dict` | A dictionary of class names that the model can detect.
`stride` | `int` | The model stride, typically 32 for YOLO models.
`fp16` | `bool` | Whether the model uses half-precision (FP16) inference.
`nhwc` | `bool` | Whether the model expects NHWC input format instead of NCHW.
`pt` | `bool` | Whether the model is a PyTorch model.
`jit` | `bool` | Whether the model is a TorchScript model.
`onnx` | `bool` | Whether the model is an ONNX model.
`xml` | `bool` | Whether the model is an OpenVINO model.
`engine` | `bool` | Whether the model is a TensorRT engine.
`coreml` | `bool` | Whether the model is a CoreML model.
`saved_model` | `bool` | Whether the model is a TensorFlow SavedModel.
`pb` | `bool` | Whether the model is a TensorFlow GraphDef.
`tflite` | `bool` | Whether the model is a TensorFlow Lite model.
`edgetpu` | `bool` | Whether the model is a TensorFlow Edge TPU model.
`tfjs` | `bool` | Whether the model is a TensorFlow.js model.
`paddle` | `bool` | Whether the model is a PaddlePaddle model.
`mnn` | `bool` | Whether the model is an MNN model.
`ncnn` | `bool` | Whether the model is an NCNN model.
`imx` | `bool` | Whether the model is an IMX model.
`rknn` | `bool` | Whether the model is an RKNN model.
`triton` | `bool` | Whether the model is a Triton Inference Server model.

Methods:

Name | Description
--- | ---
`forward` | Run inference on an input image.
`from_numpy` | Convert numpy array to tensor.
`warmup` | Warm up the model with a dummy input.
`_model_type` | Determine the model type from file path.

Examples:

    >>> model = AutoBackend(model="yolo11n.pt", device="cuda")
    >>> results = model(img)

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`model` | `str | Module` | Path to the model weights file or a module instance. | `'yolo11n.pt'`
`device` | `device` | Device to run the model on. | `device('cpu')`
`dnn` | `bool` | Use OpenCV DNN module for ONNX inference. | `False`
`data` | `str | Path` | Path to the additional data.yaml file containing class names. | `None`
`fp16` | `bool` | Enable half-precision inference. Supported only on specific backends. | `False`
`fuse` | `bool` | Fuse Conv2D + BatchNorm layers for optimization. | `True`
`verbose` | `bool` | Enable verbose logging. | `True`

Source code in `ultralytics/nn/autobackend.py`

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    @torch.no_grad()
    def __init__(
        self,
        model: str | torch.nn.Module = "yolo11n.pt",
        device: torch.device = torch.device("cpu"),
        dnn: bool = False,
        data: str | Path | None = None,
        fp16: bool = False,
        fuse: bool = True,
        verbose: bool = True,
    ):
        """
        Initialize the AutoBackend for inference.

        Args:
            model (str | torch.nn.Module): Path to the model weights file or a module instance.
            device (torch.device): Device to run the model on.
            dnn (bool): Use OpenCV DNN module for ONNX inference.
            data (str | Path, optional): Path to the additional data.yaml file containing class names.
            fp16 (bool): Enable half-precision inference. Supported only on specific backends.
            fuse (bool): Fuse Conv2D + BatchNorm layers for optimization.
            verbose (bool): Enable verbose logging.
        """
        super().__init__()
        nn_module = isinstance(model, torch.nn.Module)
        (
            pt,
            jit,
            onnx,
            xml,
            engine,
            coreml,
            saved_model,
            pb,
            tflite,
            edgetpu,
            tfjs,
            paddle,
            mnn,
            ncnn,
            imx,
            rknn,
            triton,
        ) = self._model_type("" if nn_module else model)
        fp16 &= pt or jit or onnx or xml or engine or nn_module or triton  # FP16
        nhwc = coreml or saved_model or pb or tflite or edgetpu or rknn  # BHWC formats (vs torch BCWH)
        stride, ch = 32, 3  # default stride and channels
        end2end, dynamic = False, False
        metadata, task = None, None

        # Set device
        cuda = isinstance(device, torch.device) and torch.cuda.is_available() and device.type != "cpu"  # use CUDA
        if cuda and not any([nn_module, pt, jit, engine, onnx, paddle]):  # GPU dataloader formats
            device = torch.device("cpu")
            cuda = False

        # Download if not local
        w = attempt_download_asset(model) if pt else model  # weights path

        # PyTorch (in-memory or file)
        if nn_module or pt:
            if nn_module:
                pt = True
                if fuse:
                    if IS_JETSON and is_jetson(jetpack=5):
                        # Jetson Jetpack5 requires device before fuse https://github.com/ultralytics/ultralytics/pull/21028
                        model = model.to(device)
                    model = model.fuse(verbose=verbose)
                model = model.to(device)
            else:  # pt file
                from ultralytics.nn.tasks import load_checkpoint

                model, _ = load_checkpoint(model, device=device, fuse=fuse)  # load model, ckpt

            # Common PyTorch model processing
            if hasattr(model, "kpt_shape"):
                kpt_shape = model.kpt_shape  # pose-only
            stride = max(int(model.stride.max()), 32)  # model stride
            names = model.module.names if hasattr(model, "module") else model.names  # get class names
            model.half() if fp16 else model.float()
            ch = model.yaml.get("channels", 3)
            for p in model.parameters():
                p.requires_grad = False
            self.model = model  # explicitly assign for to(), cpu(), cuda(), half()

        # TorchScript
        elif jit:
            import torchvision  # noqa - https://github.com/ultralytics/ultralytics/pull/19747

            LOGGER.info(f"Loading {w} for TorchScript inference...")
            extra_files = {"config.txt": ""}  # model metadata
            model = torch.jit.load(w, _extra_files=extra_files, map_location=device)
            model.half() if fp16 else model.float()
            if extra_files["config.txt"]:  # load metadata dict
                metadata = json.loads(extra_files["config.txt"], object_hook=lambda x: dict(x.items()))

        # ONNX OpenCV DNN
        elif dnn:
            LOGGER.info(f"Loading {w} for ONNX OpenCV DNN inference...")
            check_requirements("opencv-python>=4.5.4")
            net = cv2.dnn.readNetFromONNX(w)

        # ONNX Runtime and IMX
        elif onnx or imx:
            LOGGER.info(f"Loading {w} for ONNX Runtime inference...")
            check_requirements(("onnx", "onnxruntime-gpu" if cuda else "onnxruntime"))
            import onnxruntime

            providers = ["CPUExecutionProvider"]
            if cuda:
                if "CUDAExecutionProvider" in onnxruntime.get_available_providers():
                    providers.insert(0, "CUDAExecutionProvider")
                else:  # Only log warning if CUDA was requested but unavailable
                    LOGGER.warning("Failed to start ONNX Runtime with CUDA. Using CPU...")
                    device = torch.device("cpu")
                    cuda = False
            LOGGER.info(f"Using ONNX Runtime {providers[0]}")
            if onnx:
                session = onnxruntime.InferenceSession(w, providers=providers)
            else:
                check_requirements(
                    ["model-compression-toolkit>=2.4.1", "sony-custom-layers[torch]>=0.3.0", "onnxruntime-extensions"]
                )
                w = next(Path(w).glob("*.onnx"))
                LOGGER.info(f"Loading {w} for ONNX IMX inference...")
                import mct_quantizers as mctq
                from sony_custom_layers.pytorch.nms import nms_ort  # noqa

                session_options = mctq.get_ort_session_options()
                session_options.enable_mem_reuse = False  # fix the shape mismatch from onnxruntime
                session = onnxruntime.InferenceSession(w, session_options, providers=["CPUExecutionProvider"])

            output_names = [x.name for x in session.get_outputs()]
            metadata = session.get_modelmeta().custom_metadata_map
            dynamic = isinstance(session.get_outputs()[0].shape[0], str)
            fp16 = "float16" in session.get_inputs()[0].type
            if not dynamic:
                io = session.io_binding()
                bindings = []
                for output in session.get_outputs():
                    out_fp16 = "float16" in output.type
                    y_tensor = torch.empty(output.shape, dtype=torch.float16 if out_fp16 else torch.float32).to(device)
                    io.bind_output(
                        name=output.name,
                        device_type=device.type,
                        device_id=device.index if cuda else 0,
                        element_type=np.float16 if out_fp16 else np.float32,
                        shape=tuple(y_tensor.shape),
                        buffer_ptr=y_tensor.data_ptr(),
                    )
                    bindings.append(y_tensor)

        # OpenVINO
        elif xml:
            LOGGER.info(f"Loading {w} for OpenVINO inference...")
            check_requirements("openvino>=2024.0.0")
            import openvino as ov

            core = ov.Core()
            device_name = "AUTO"
            if isinstance(device, str) and device.startswith("intel"):
                device_name = device.split(":")[1].upper()  # Intel OpenVINO device
                device = torch.device("cpu")
                if device_name not in core.available_devices:
                    LOGGER.warning(f"OpenVINO device '{device_name}' not available. Using 'AUTO' instead.")
                    device_name = "AUTO"
            w = Path(w)
            if not w.is_file():  # if not *.xml
                w = next(w.glob("*.xml"))  # get *.xml file from *_openvino_model dir
            ov_model = core.read_model(model=str(w), weights=w.with_suffix(".bin"))
            if ov_model.get_parameters()[0].get_layout().empty:
                ov_model.get_parameters()[0].set_layout(ov.Layout("NCHW"))

            metadata = w.parent / "metadata.yaml"
            if metadata.exists():
                metadata = YAML.load(metadata)
                batch = metadata["batch"]
                dynamic = metadata.get("args", {}).get("dynamic", dynamic)
            # OpenVINO inference modes are 'LATENCY', 'THROUGHPUT' (not recommended), or 'CUMULATIVE_THROUGHPUT'
            inference_mode = "CUMULATIVE_THROUGHPUT" if batch > 1 and dynamic else "LATENCY"
            ov_compiled_model = core.compile_model(
                ov_model,
                device_name=device_name,
                config={"PERFORMANCE_HINT": inference_mode},
            )
            LOGGER.info(
                f"Using OpenVINO {inference_mode} mode for batch={batch} inference on {', '.join(ov_compiled_model.get_property('EXECUTION_DEVICES'))}..."
            )
            input_name = ov_compiled_model.input().get_any_name()

        # TensorRT
        elif engine:
            LOGGER.info(f"Loading {w} for TensorRT inference...")

            if IS_JETSON and check_version(PYTHON_VERSION, "<=3.8.10"):
                # fix error: `np.bool` was a deprecated alias for the builtin `bool` for JetPack 4 and JetPack 5 with Python <= 3.8.10
                check_requirements("numpy==1.23.5")

            try:  # https://developer.nvidia.com/nvidia-tensorrt-download
                import tensorrt as trt  # noqa
            except ImportError:
                if LINUX:
                    check_requirements("tensorrt>7.0.0,!=10.1.0")
                import tensorrt as trt  # noqa
            check_version(trt.__version__, ">=7.0.0", hard=True)
            check_version(trt.__version__, "!=10.1.0", msg="https://github.com/ultralytics/ultralytics/pull/14239")
            if device.type == "cpu":
                device = torch.device("cuda:0")
            Binding = namedtuple("Binding", ("name", "dtype", "shape", "data", "ptr"))
            logger = trt.Logger(trt.Logger.INFO)
            # Read file
            with open(w, "rb") as f, trt.Runtime(logger) as runtime:
                try:
                    meta_len = int.from_bytes(f.read(4), byteorder="little")  # read metadata length
                    metadata = json.loads(f.read(meta_len).decode("utf-8"))  # read metadata
                    dla = metadata.get("dla", None)
                    if dla is not None:
                        runtime.DLA_core = int(dla)
                except UnicodeDecodeError:
                    f.seek(0)  # engine file may lack embedded Ultralytics metadata
                model = runtime.deserialize_cuda_engine(f.read())  # read engine

            # Model context
            try:
                context = model.create_execution_context()
            except Exception as e:  # model is None
                LOGGER.error(f"TensorRT model exported with a different version than {trt.__version__}\n")
                raise e

            bindings = OrderedDict()
            output_names = []
            fp16 = False  # default updated below
            dynamic = False
            is_trt10 = not hasattr(model, "num_bindings")
            num = range(model.num_io_tensors) if is_trt10 else range(model.num_bindings)
            for i in num:
                if is_trt10:
                    name = model.get_tensor_name(i)
                    dtype = trt.nptype(model.get_tensor_dtype(name))
                    is_input = model.get_tensor_mode(name) == trt.TensorIOMode.INPUT
                    if is_input:
                        if -1 in tuple(model.get_tensor_shape(name)):
                            dynamic = True
                            context.set_input_shape(name, tuple(model.get_tensor_profile_shape(name, 0)[1]))
                        if dtype == np.float16:
                            fp16 = True
                    else:
                        output_names.append(name)
                    shape = tuple(context.get_tensor_shape(name))
                else:  # TensorRT < 10.0
                    name = model.get_binding_name(i)
                    dtype = trt.nptype(model.get_binding_dtype(i))
                    is_input = model.binding_is_input(i)
                    if model.binding_is_input(i):
                        if -1 in tuple(model.get_binding_shape(i)):  # dynamic
                            dynamic = True
                            context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[1]))
                        if dtype == np.float16:
                            fp16 = True
                    else:
                        output_names.append(name)
                    shape = tuple(context.get_binding_shape(i))
                im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
                bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
            binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())

        # CoreML
        elif coreml:
            check_requirements("coremltools>=8.0")
            LOGGER.info(f"Loading {w} for CoreML inference...")
            import coremltools as ct

            model = ct.models.MLModel(w)
            metadata = dict(model.user_defined_metadata)

        # TF SavedModel
        elif saved_model:
            LOGGER.info(f"Loading {w} for TensorFlow SavedModel inference...")
            import tensorflow as tf

            keras = False  # assume TF1 saved_model
            model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
            metadata = Path(w) / "metadata.yaml"

        # TF GraphDef
        elif pb:  # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
            LOGGER.info(f"Loading {w} for TensorFlow GraphDef inference...")
            import tensorflow as tf

            from ultralytics.engine.exporter import gd_outputs

            def wrap_frozen_graph(gd, inputs, outputs):
                """Wrap frozen graphs for deployment."""
                x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), [])  # wrapped
                ge = x.graph.as_graph_element
                return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))

            gd = tf.Graph().as_graph_def()  # TF GraphDef
            with open(w, "rb") as f:
                gd.ParseFromString(f.read())
            frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd))
            try:  # find metadata in SavedModel alongside GraphDef
                metadata = next(Path(w).resolve().parent.rglob(f"{Path(w).stem}_saved_model*/metadata.yaml"))
            except StopIteration:
                pass

        # TFLite or TFLite Edge TPU
        elif tflite or edgetpu:  # https://ai.google.dev/edge/litert/microcontrollers/python
            try:  # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
                from tflite_runtime.interpreter import Interpreter, load_delegate
            except ImportError:
                import tensorflow as tf

                Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate
            if edgetpu:  # TF Edge TPU https://coral.ai/software/#edgetpu-runtime
                device = device[3:] if str(device).startswith("tpu") else ":0"
                LOGGER.info(f"Loading {w} on device {device[1:]} for TensorFlow Lite Edge TPU inference...")
                delegate = {"Linux": "libedgetpu.so.1", "Darwin": "libedgetpu.1.dylib", "Windows": "edgetpu.dll"}[
                    platform.system()
                ]
                interpreter = Interpreter(
                    model_path=w,
                    experimental_delegates=[load_delegate(delegate, options={"device": device})],
                )
                device = "cpu"  # Required, otherwise PyTorch will try to use the wrong device
            else:  # TFLite
                LOGGER.info(f"Loading {w} for TensorFlow Lite inference...")
                interpreter = Interpreter(model_path=w)  # load TFLite model
            interpreter.allocate_tensors()  # allocate
            input_details = interpreter.get_input_details()  # inputs
            output_details = interpreter.get_output_details()  # outputs
            # Load metadata
            try:
                with zipfile.ZipFile(w, "r") as zf:
                    name = zf.namelist()[0]
                    contents = zf.read(name).decode("utf-8")
                    if name == "metadata.json":  # Custom Ultralytics metadata dict for Python>=3.12
                        metadata = json.loads(contents)
                    else:
                        metadata = ast.literal_eval(contents)  # Default tflite-support metadata for Python<=3.11
            except (zipfile.BadZipFile, SyntaxError, ValueError, json.JSONDecodeError):
                pass

        # TF.js
        elif tfjs:
            raise NotImplementedError("Ultralytics TF.js inference is not currently supported.")

        # PaddlePaddle
        elif paddle:
            LOGGER.info(f"Loading {w} for PaddlePaddle inference...")
            check_requirements(
                "paddlepaddle-gpu"
                if torch.cuda.is_available()
                else "paddlepaddle==3.0.0"  # pin 3.0.0 for ARM64
                if ARM64
                else "paddlepaddle>=3.0.0"
            )
            import paddle.inference as pdi  # noqa

            w = Path(w)
            model_file, params_file = None, None
            if w.is_dir():
                model_file = next(w.rglob("*.json"), None)
                params_file = next(w.rglob("*.pdiparams"), None)
            elif w.suffix == ".pdiparams":
                model_file = w.with_name("model.json")
                params_file = w

            if not (model_file and params_file and model_file.is_file() and params_file.is_file()):
                raise FileNotFoundError(f"Paddle model not found in {w}. Both .json and .pdiparams files are required.")

            config = pdi.Config(str(model_file), str(params_file))
            if cuda:
                config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
            predictor = pdi.create_predictor(config)
            input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
            output_names = predictor.get_output_names()
            metadata = w / "metadata.yaml"

        # MNN
        elif mnn:
            LOGGER.info(f"Loading {w} for MNN inference...")
            check_requirements("MNN")  # requires MNN
            import os

            import MNN

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

            def torch_to_mnn(x):
                return MNN.expr.const(x.data_ptr(), x.shape)

            metadata = json.loads(net.get_info()["bizCode"])

        # NCNN
        elif ncnn:
            LOGGER.info(f"Loading {w} for NCNN inference...")
            check_requirements("git+https://github.com/Tencent/ncnn.git" if ARM64 else "ncnn", cmds="--no-deps")
            import ncnn as pyncnn

            net = pyncnn.Net()
            net.opt.use_vulkan_compute = cuda
            w = Path(w)
            if not w.is_file():  # if not *.param
                w = next(w.glob("*.param"))  # get *.param file from *_ncnn_model dir
            net.load_param(str(w))
            net.load_model(str(w.with_suffix(".bin")))
            metadata = w.parent / "metadata.yaml"

        # NVIDIA Triton Inference Server
        elif triton:
            check_requirements("tritonclient[all]")
            from ultralytics.utils.triton import TritonRemoteModel

            model = TritonRemoteModel(w)
            metadata = model.metadata

        # RKNN
        elif rknn:
            if not is_rockchip():
                raise OSError("RKNN inference is only supported on Rockchip devices.")
            LOGGER.info(f"Loading {w} for RKNN inference...")
            check_requirements("rknn-toolkit-lite2")
            from rknnlite.api import RKNNLite

            w = Path(w)
            if not w.is_file():  # if not *.rknn
                w = next(w.rglob("*.rknn"))  # get *.rknn file from *_rknn_model dir
            rknn_model = RKNNLite()
            rknn_model.load_rknn(str(w))
            rknn_model.init_runtime()
            metadata = w.parent / "metadata.yaml"

        # Any other format (unsupported)
        else:
            from ultralytics.engine.exporter import export_formats

            raise TypeError(
                f"model='{w}' is not a supported model format. Ultralytics supports: {export_formats()['Format']}\n"
                f"See https://docs.ultralytics.com/modes/predict for help."
            )

        # Load external metadata YAML
        if isinstance(metadata, (str, Path)) and Path(metadata).exists():
            metadata = YAML.load(metadata)
        if metadata and isinstance(metadata, dict):
            for k, v in metadata.items():
                if k in {"stride", "batch", "channels"}:
                    metadata[k] = int(v)
                elif k in {"imgsz", "names", "kpt_shape", "args"} and isinstance(v, str):
                    metadata[k] = eval(v)
            stride = metadata["stride"]
            task = metadata["task"]
            batch = metadata["batch"]
            imgsz = metadata["imgsz"]
            names = metadata["names"]
            kpt_shape = metadata.get("kpt_shape")
            end2end = metadata.get("args", {}).get("nms", False)
            dynamic = metadata.get("args", {}).get("dynamic", dynamic)
            ch = metadata.get("channels", 3)
        elif not (pt or triton or nn_module):
            LOGGER.warning(f"Metadata not found for 'model={w}'")

        # Check names
        if "names" not in locals():  # names missing
            names = default_class_names(data)
        names = check_class_names(names)

        self.__dict__.update(locals())  # assign all variables to self

### `` forward

    forward(
        im: Tensor,
        augment: bool = False,
        visualize: bool = False,
        embed: list | None = None,
        **kwargs: Any
    ) -> torch.Tensor | list[torch.Tensor]

Run inference on an AutoBackend model.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`im` | `Tensor` | The image tensor to perform inference on. | required
`augment` | `bool` | Whether to perform data augmentation during inference. | `False`
`visualize` | `bool` | Whether to visualize the output predictions. | `False`
`embed` | `list` | A list of feature vectors/embeddings to return. | `None`
`**kwargs` | `Any` | Additional keyword arguments for model configuration. | `{}`

Returns:

Type | Description
--- | ---
`Tensor | list[Tensor]` | The raw output tensor(s) from the model.

Source code in `ultralytics/nn/autobackend.py`

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    def forward(
        self,
        im: torch.Tensor,
        augment: bool = False,
        visualize: bool = False,
        embed: list | None = None,
        **kwargs: Any,
    ) -> torch.Tensor | list[torch.Tensor]:
        """
        Run inference on an AutoBackend model.

        Args:
            im (torch.Tensor): The image tensor to perform inference on.
            augment (bool): Whether to perform data augmentation during inference.
            visualize (bool): Whether to visualize the output predictions.
            embed (list, optional): A list of feature vectors/embeddings to return.
            **kwargs (Any): Additional keyword arguments for model configuration.

        Returns:
            (torch.Tensor | list[torch.Tensor]): The raw output tensor(s) from the model.
        """
        b, ch, h, w = im.shape  # batch, channel, height, width
        if self.fp16 and im.dtype != torch.float16:
            im = im.half()  # to FP16
        if self.nhwc:
            im = im.permute(0, 2, 3, 1)  # torch BCHW to numpy BHWC shape(1,320,192,3)

        # PyTorch
        if self.pt or self.nn_module:
            y = self.model(im, augment=augment, visualize=visualize, embed=embed, **kwargs)

        # TorchScript
        elif self.jit:
            y = self.model(im)

        # ONNX OpenCV DNN
        elif self.dnn:
            im = im.cpu().numpy()  # torch to numpy
            self.net.setInput(im)
            y = self.net.forward()

        # ONNX Runtime
        elif self.onnx or self.imx:
            if self.dynamic:
                im = im.cpu().numpy()  # torch to numpy
                y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
            else:
                if not self.cuda:
                    im = im.cpu()
                self.io.bind_input(
                    name="images",
                    device_type=im.device.type,
                    device_id=im.device.index if im.device.type == "cuda" else 0,
                    element_type=np.float16 if self.fp16 else np.float32,
                    shape=tuple(im.shape),
                    buffer_ptr=im.data_ptr(),
                )
                self.session.run_with_iobinding(self.io)
                y = self.bindings
            if self.imx:
                if self.task == "detect":
                    # boxes, conf, cls
                    y = np.concatenate([y[0], y[1][:, :, None], y[2][:, :, None]], axis=-1)
                elif self.task == "pose":
                    # boxes, conf, kpts
                    y = np.concatenate([y[0], y[1][:, :, None], y[2][:, :, None], y[3]], axis=-1)

        # OpenVINO
        elif self.xml:
            im = im.cpu().numpy()  # FP32

            if self.inference_mode in {"THROUGHPUT", "CUMULATIVE_THROUGHPUT"}:  # optimized for larger batch-sizes
                n = im.shape[0]  # number of images in batch
                results = [None] * n  # preallocate list with None to match the number of images

                def callback(request, userdata):
                    """Place result in preallocated list using userdata index."""
                    results[userdata] = request.results

                # Create AsyncInferQueue, set the callback and start asynchronous inference for each input image
                async_queue = self.ov.AsyncInferQueue(self.ov_compiled_model)
                async_queue.set_callback(callback)
                for i in range(n):
                    # Start async inference with userdata=i to specify the position in results list
                    async_queue.start_async(inputs={self.input_name: im[i : i + 1]}, userdata=i)  # keep image as BCHW
                async_queue.wait_all()  # wait for all inference requests to complete
                y = [list(r.values()) for r in results]
                y = [np.concatenate(x) for x in zip(*y)]
            else:  # inference_mode = "LATENCY", optimized for fastest first result at batch-size 1
                y = list(self.ov_compiled_model(im).values())

        # TensorRT
        elif self.engine:
            if self.dynamic and im.shape != self.bindings["images"].shape:
                if self.is_trt10:
                    self.context.set_input_shape("images", im.shape)
                    self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape)
                    for name in self.output_names:
                        self.bindings[name].data.resize_(tuple(self.context.get_tensor_shape(name)))
                else:
                    i = self.model.get_binding_index("images")
                    self.context.set_binding_shape(i, im.shape)
                    self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape)
                    for name in self.output_names:
                        i = self.model.get_binding_index(name)
                        self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))

            s = self.bindings["images"].shape
            assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
            self.binding_addrs["images"] = int(im.data_ptr())
            self.context.execute_v2(list(self.binding_addrs.values()))
            y = [self.bindings[x].data for x in sorted(self.output_names)]

        # CoreML
        elif self.coreml:
            im = im[0].cpu().numpy()
            im_pil = Image.fromarray((im * 255).astype("uint8"))
            # im = im.resize((192, 320), Image.BILINEAR)
            y = self.model.predict({"image": im_pil})  # coordinates are xywh normalized
            if "confidence" in y:  # NMS included
                from ultralytics.utils.ops import xywh2xyxy

                box = xywh2xyxy(y["coordinates"] * [[w, h, w, h]])  # xyxy pixels
                cls = y["confidence"].argmax(1, keepdims=True)
                y = np.concatenate((box, np.take_along_axis(y["confidence"], cls, axis=1), cls), 1)[None]
            else:
                y = list(y.values())
            if len(y) == 2 and len(y[1].shape) != 4:  # segmentation model
                y = list(reversed(y))  # reversed for segmentation models (pred, proto)

        # PaddlePaddle
        elif self.paddle:
            im = im.cpu().numpy().astype(np.float32)
            self.input_handle.copy_from_cpu(im)
            self.predictor.run()
            y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]

        # MNN
        elif self.mnn:
            input_var = self.torch_to_mnn(im)
            output_var = self.net.onForward([input_var])
            y = [x.read() for x in output_var]

        # NCNN
        elif self.ncnn:
            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)
                # WARNING: 'output_names' sorted as a temporary fix for https://github.com/pnnx/pnnx/issues/130
                y = [np.array(ex.extract(x)[1])[None] for x in sorted(self.net.output_names())]

        # NVIDIA Triton Inference Server
        elif self.triton:
            im = im.cpu().numpy()  # torch to numpy
            y = self.model(im)

        # RKNN
        elif self.rknn:
            im = (im.cpu().numpy() * 255).astype("uint8")
            im = im if isinstance(im, (list, tuple)) else [im]
            y = self.rknn_model.inference(inputs=im)

        # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
        else:
            im = im.cpu().numpy()
            if self.saved_model:  # SavedModel
                y = self.model(im, training=False) if self.keras else self.model.serving_default(im)
                if not isinstance(y, list):
                    y = [y]
            elif self.pb:  # GraphDef
                y = self.frozen_func(x=self.tf.constant(im))
            else:  # Lite or Edge TPU
                details = self.input_details[0]
                is_int = details["dtype"] in {np.int8, np.int16}  # is TFLite quantized int8 or int16 model
                if is_int:
                    scale, zero_point = details["quantization"]
                    im = (im / scale + zero_point).astype(details["dtype"])  # de-scale
                self.interpreter.set_tensor(details["index"], im)
                self.interpreter.invoke()
                y = []
                for output in self.output_details:
                    x = self.interpreter.get_tensor(output["index"])
                    if is_int:
                        scale, zero_point = output["quantization"]
                        x = (x.astype(np.float32) - zero_point) * scale  # re-scale
                    if x.ndim == 3:  # if task is not classification, excluding masks (ndim=4) as well
                        # Denormalize xywh by image size. See https://github.com/ultralytics/ultralytics/pull/1695
                        # xywh are normalized in TFLite/EdgeTPU to mitigate quantization error of integer models
                        if x.shape[-1] == 6 or self.end2end:  # end-to-end model
                            x[:, :, [0, 2]] *= w
                            x[:, :, [1, 3]] *= h
                            if self.task == "pose":
                                x[:, :, 6::3] *= w
                                x[:, :, 7::3] *= h
                        else:
                            x[:, [0, 2]] *= w
                            x[:, [1, 3]] *= h
                            if self.task == "pose":
                                x[:, 5::3] *= w
                                x[:, 6::3] *= h
                    y.append(x)
            # TF segment fixes: export is reversed vs ONNX export and protos are transposed
            if len(y) == 2:  # segment with (det, proto) output order reversed
                if len(y[1].shape) != 4:
                    y = list(reversed(y))  # should be y = (1, 116, 8400), (1, 160, 160, 32)
                if y[1].shape[-1] == 6:  # end-to-end model
                    y = [y[1]]
                else:
                    y[1] = np.transpose(y[1], (0, 3, 1, 2))  # should be y = (1, 116, 8400), (1, 32, 160, 160)
            y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]

        # for x in y:
        #     print(type(x), len(x)) if isinstance(x, (list, tuple)) else print(type(x), x.shape)  # debug shapes
        if isinstance(y, (list, tuple)):
            if len(self.names) == 999 and (self.task == "segment" or len(y) == 2):  # segments and names not defined
                nc = y[0].shape[1] - y[1].shape[1] - 4  # y = (1, 32, 160, 160), (1, 116, 8400)
                self.names = {i: f"class{i}" for i in range(nc)}
            return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]
        else:
            return self.from_numpy(y)

### `` from_numpy

    from_numpy(x: ndarray) -> torch.Tensor

Convert a numpy array to a tensor.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`x` | `ndarray` | The array to be converted. | required

Returns:

Type | Description
--- | ---
`Tensor` | The converted tensor

Source code in `ultralytics/nn/autobackend.py`

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    def from_numpy(self, x: np.ndarray) -> torch.Tensor:
        """
        Convert a numpy array to a tensor.

        Args:
            x (np.ndarray): The array to be converted.

        Returns:
            (torch.Tensor): The converted tensor
        """
        return torch.tensor(x).to(self.device) if isinstance(x, np.ndarray) else x

### `` warmup

    warmup(imgsz: tuple[int, int, int, int] = (1, 3, 640, 640)) -> None

Warm up the model by running one forward pass with a dummy input.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`imgsz` | `tuple` | The shape of the dummy input tensor in the format (batch_size, channels, height, width) | `(1, 3, 640, 640)`

Source code in `ultralytics/nn/autobackend.py`

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    def warmup(self, imgsz: tuple[int, int, int, int] = (1, 3, 640, 640)) -> None:
        """
        Warm up the model by running one forward pass with a dummy input.

        Args:
            imgsz (tuple): The shape of the dummy input tensor in the format (batch_size, channels, height, width)
        """
        warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton, self.nn_module
        if any(warmup_types) and (self.device.type != "cpu" or self.triton):
            im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device)  # input
            for _ in range(2 if self.jit else 1):
                self.forward(im)  # warmup

## `` ultralytics.nn.autobackend.check_class_names

    check_class_names(names: list | dict) -> dict[int, str]

Check class names and convert to dict format if needed.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`names` | `list | dict` | Class names as list or dict format. | required

Returns:

Type | Description
--- | ---
`dict` | Class names in dict format with integer keys and string values.

Raises:

Type | Description
--- | ---
`KeyError` | If class indices are invalid for the dataset size.

Source code in `ultralytics/nn/autobackend.py`

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    def check_class_names(names: list | dict) -> dict[int, str]:
        """
        Check class names and convert to dict format if needed.

        Args:
            names (list | dict): Class names as list or dict format.

        Returns:
            (dict): Class names in dict format with integer keys and string values.

        Raises:
            KeyError: If class indices are invalid for the dataset size.
        """
        if isinstance(names, list):  # names is a list
            names = dict(enumerate(names))  # convert to dict
        if isinstance(names, dict):
            # Convert 1) string keys to int, i.e. '0' to 0, and non-string values to strings, i.e. True to 'True'
            names = {int(k): str(v) for k, v in names.items()}
            n = len(names)
            if max(names.keys()) >= n:
                raise KeyError(
                    f"{n}-class dataset requires class indices 0-{n - 1}, but you have invalid class indices "
                    f"{min(names.keys())}-{max(names.keys())} defined in your dataset YAML."
                )
            if isinstance(names[0], str) and names[0].startswith("n0"):  # imagenet class codes, i.e. 'n01440764'
                names_map = YAML.load(ROOT / "cfg/datasets/ImageNet.yaml")["map"]  # human-readable names
                names = {k: names_map[v] for k, v in names.items()}
        return names

## `` ultralytics.nn.autobackend.default_class_names

    default_class_names(data: str | Path | None = None) -> dict[int, str]

Apply default class names to an input YAML file or return numerical class names.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`data` | `str | Path` | Path to YAML file containing class names. | `None`

Returns:

Type | Description
--- | ---
`dict` | Dictionary mapping class indices to class names.

Source code in `ultralytics/nn/autobackend.py`

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    def default_class_names(data: str | Path | None = None) -> dict[int, str]:
        """
        Apply default class names to an input YAML file or return numerical class names.

        Args:
            data (str | Path, optional): Path to YAML file containing class names.

        Returns:
            (dict): Dictionary mapping class indices to class names.
        """
        if data:
            try:
                return YAML.load(check_yaml(data))["names"]
            except Exception:
                pass
        return {i: f"class{i}" for i in range(999)}  # return default if above errors

📅 Created 1 year ago ✏️ Updated 1 year ago