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AutoBackend


Bases: nn.Module

Source code in ultralytics/nn/autobackend.py
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class AutoBackend(nn.Module):

    def __init__(self,
                 weights='yolov8n.pt',
                 device=torch.device('cpu'),
                 dnn=False,
                 data=None,
                 fp16=False,
                 fuse=True,
                 verbose=True):
        """
        MultiBackend class for python inference on various platforms using Ultralytics YOLO.

        Args:
            weights (str): The path to the weights file. Default: 'yolov8n.pt'
            device (torch.device): The device to run the model on.
            dnn (bool): Use OpenCV's DNN module for inference if True, defaults to False.
            data (str), (Path): Additional data.yaml file for class names, optional
            fp16 (bool): If True, use half precision. Default: False
            fuse (bool): Whether to fuse the model or not. Default: True
            verbose (bool): Whether to run in verbose mode or not. Default: True

        Supported formats and their naming conventions:
            | Format                | Suffix           |
            |-----------------------|------------------|
            | PyTorch               | *.pt             |
            | TorchScript           | *.torchscript    |
            | ONNX Runtime          | *.onnx           |
            | ONNX OpenCV DNN       | *.onnx dnn=True  |
            | OpenVINO              | *.xml            |
            | CoreML                | *.mlmodel        |
            | TensorRT              | *.engine         |
            | TensorFlow SavedModel | *_saved_model    |
            | TensorFlow GraphDef   | *.pb             |
            | TensorFlow Lite       | *.tflite         |
            | TensorFlow Edge TPU   | *_edgetpu.tflite |
            | PaddlePaddle          | *_paddle_model   |
        """
        super().__init__()
        w = str(weights[0] if isinstance(weights, list) else weights)
        nn_module = isinstance(weights, torch.nn.Module)
        pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
        fp16 &= pt or jit or onnx or engine or nn_module or triton  # FP16
        nhwc = coreml or saved_model or pb or tflite or edgetpu  # BHWC formats (vs torch BCWH)
        stride = 32  # default stride
        model, metadata = None, None
        cuda = torch.cuda.is_available() and device.type != 'cpu'  # use CUDA
        if not (pt or triton or nn_module):
            w = attempt_download_asset(w)  # download if not local

        # NOTE: special case: in-memory pytorch model
        if nn_module:
            model = weights.to(device)
            model = model.fuse(verbose=verbose) if fuse else model
            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()
            self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
            pt = True
        elif pt:  # PyTorch
            from ultralytics.nn.tasks import attempt_load_weights
            model = attempt_load_weights(weights if isinstance(weights, list) else w,
                                         device=device,
                                         inplace=True,
                                         fuse=fuse)
            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()
            self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
        elif jit:  # TorchScript
            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()))
        elif dnn:  # ONNX OpenCV DNN
            LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
            check_requirements('opencv-python>=4.5.4')
            net = cv2.dnn.readNetFromONNX(w)
        elif onnx:  # ONNX Runtime
            LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
            check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
            import onnxruntime
            providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
            session = onnxruntime.InferenceSession(w, providers=providers)
            output_names = [x.name for x in session.get_outputs()]
            metadata = session.get_modelmeta().custom_metadata_map  # metadata
        elif xml:  # OpenVINO
            LOGGER.info(f'Loading {w} for OpenVINO inference...')
            check_requirements('openvino')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
            from openvino.runtime import Core, Layout, get_batch  # noqa
            ie = Core()
            w = Path(w)
            if not w.is_file():  # if not *.xml
                w = next(w.glob('*.xml'))  # get *.xml file from *_openvino_model dir
            network = ie.read_model(model=str(w), weights=w.with_suffix('.bin'))
            if network.get_parameters()[0].get_layout().empty:
                network.get_parameters()[0].set_layout(Layout('NCHW'))
            batch_dim = get_batch(network)
            if batch_dim.is_static:
                batch_size = batch_dim.get_length()
            executable_network = ie.compile_model(network, device_name='CPU')  # device_name="MYRIAD" for NCS2
            metadata = w.parent / 'metadata.yaml'
        elif engine:  # TensorRT
            LOGGER.info(f'Loading {w} for TensorRT inference...')
            try:
                import tensorrt as trt  # noqa https://developer.nvidia.com/nvidia-tensorrt-download
            except ImportError:
                if LINUX:
                    check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
                import tensorrt as trt  # noqa
            check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=7.0.0
            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:
                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
                model = runtime.deserialize_cuda_engine(f.read())  # read engine
            context = model.create_execution_context()
            bindings = OrderedDict()
            output_names = []
            fp16 = False  # default updated below
            dynamic = False
            for i in range(model.num_bindings):
                name = model.get_binding_name(i)
                dtype = trt.nptype(model.get_binding_dtype(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)[2]))
                    if dtype == np.float16:
                        fp16 = True
                else:  # output
                    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())
            batch_size = bindings['images'].shape[0]  # if dynamic, this is instead max batch size
        elif coreml:  # CoreML
            LOGGER.info(f'Loading {w} for CoreML inference...')
            import coremltools as ct
            model = ct.models.MLModel(w)
            metadata = dict(model.user_defined_metadata)
        elif saved_model:  # TF SavedModel
            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'
        elif pb:  # GraphDef 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.yolo.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))
        elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_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
                LOGGER.info(f'Loading {w} 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)])
            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
            with contextlib.suppress(zipfile.BadZipFile):
                with zipfile.ZipFile(w, 'r') as model:
                    meta_file = model.namelist()[0]
                    metadata = ast.literal_eval(model.read(meta_file).decode('utf-8'))
        elif tfjs:  # TF.js
            raise NotImplementedError('YOLOv8 TF.js inference is not supported')
        elif paddle:  # PaddlePaddle
            LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
            check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
            import paddle.inference as pdi  # noqa
            w = Path(w)
            if not w.is_file():  # if not *.pdmodel
                w = next(w.rglob('*.pdmodel'))  # get *.pdmodel file from *_paddle_model dir
            config = pdi.Config(str(w), str(w.with_suffix('.pdiparams')))
            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.parents[1] / 'metadata.yaml'
        elif triton:  # NVIDIA Triton Inference Server
            LOGGER.info('Triton Inference Server not supported...')
            '''
            TODO:
            check_requirements('tritonclient[all]')
            from utils.triton import TritonRemoteModel
            model = TritonRemoteModel(url=w)
            nhwc = model.runtime.startswith("tensorflow")
            '''
        else:
            from ultralytics.yolo.engine.exporter import export_formats
            raise TypeError(f"model='{w}' is not a supported model format. "
                            'See https://docs.ultralytics.com/modes/predict for help.'
                            f'\n\n{export_formats()}')

        # Load external metadata YAML
        if isinstance(metadata, (str, Path)) and Path(metadata).exists():
            metadata = yaml_load(metadata)
        if metadata:
            for k, v in metadata.items():
                if k in ('stride', 'batch'):
                    metadata[k] = int(v)
                elif k in ('imgsz', 'names', 'kpt_shape') 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')
        elif not (pt or triton or nn_module):
            LOGGER.warning(f"WARNING ⚠️ Metadata not found for 'model={weights}'")

        # Check names
        if 'names' not in locals():  # names missing
            names = self._apply_default_class_names(data)
        names = check_class_names(names)

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

    def forward(self, im, augment=False, visualize=False):
        """
        Runs inference on the YOLOv8 MultiBackend model.

        Args:
            im (torch.Tensor): The image tensor to perform inference on.
            augment (bool): whether to perform data augmentation during inference, defaults to False
            visualize (bool): whether to visualize the output predictions, defaults to False

        Returns:
            (tuple): Tuple containing the raw output tensor, and processed output for visualization (if visualize=True)
        """
        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)

        if self.pt or self.nn_module:  # PyTorch
            y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
        elif self.jit:  # TorchScript
            y = self.model(im)
        elif self.dnn:  # ONNX OpenCV DNN
            im = im.cpu().numpy()  # torch to numpy
            self.net.setInput(im)
            y = self.net.forward()
        elif self.onnx:  # ONNX Runtime
            im = im.cpu().numpy()  # torch to numpy
            y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
        elif self.xml:  # OpenVINO
            im = im.cpu().numpy()  # FP32
            y = list(self.executable_network([im]).values())
        elif self.engine:  # TensorRT
            if self.dynamic and im.shape != self.bindings['images'].shape:
                i = self.model.get_binding_index('images')
                self.context.set_binding_shape(i, im.shape)  # reshape if dynamic
                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)]
        elif self.coreml:  # CoreML
            im = im[0].cpu().numpy()
            im_pil = Image.fromarray((im * 255).astype('uint8'))
            # im = im.resize((192, 320), Image.ANTIALIAS)
            y = self.model.predict({'image': im_pil})  # coordinates are xywh normalized
            if 'confidence' in y:
                box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels
                conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
                y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
            elif len(y) == 1:  # classification model
                y = list(y.values())
            elif len(y) == 2:  # segmentation model
                y = list(reversed(y.values()))  # reversed for segmentation models (pred, proto)
        elif self.paddle:  # PaddlePaddle
            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]
        elif self.triton:  # NVIDIA Triton Inference Server
            y = self.model(im)
        else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
            im = im.cpu().numpy()
            if self.saved_model:  # SavedModel
                y = self.model(im, training=False) if self.keras else self.model(im)
                if not isinstance(y, list):
                    y = [y]
            elif self.pb:  # GraphDef
                y = self.frozen_func(x=self.tf.constant(im))
                if len(y) == 2 and len(self.names) == 999:  # segments and names not defined
                    ip, ib = (0, 1) if len(y[0].shape) == 4 else (1, 0)  # index of protos, boxes
                    nc = y[ib].shape[1] - y[ip].shape[3] - 4  # y = (1, 160, 160, 32), (1, 116, 8400)
                    self.names = {i: f'class{i}' for i in range(nc)}
            else:  # Lite or Edge TPU
                input = self.input_details[0]
                int8 = input['dtype'] == np.int8  # is TFLite quantized int8 model
                if int8:
                    scale, zero_point = input['quantization']
                    im = (im / scale + zero_point).astype(np.int8)  # de-scale
                self.interpreter.set_tensor(input['index'], im)
                self.interpreter.invoke()
                y = []
                for output in self.output_details:
                    x = self.interpreter.get_tensor(output['index'])
                    if int8:
                        scale, zero_point = output['quantization']
                        x = (x.astype(np.float32) - zero_point) * scale  # re-scale
                    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)
                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]
            # y[0][..., :4] *= [w, h, w, h]  # xywh normalized to pixels

        # 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)):
            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)

    def from_numpy(self, x):
        """
         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

    def warmup(self, imgsz=(1, 3, 640, 640)):
        """
        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)

        Returns:
            (None): This method runs the forward pass and don't return any value
        """
        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

    @staticmethod
    def _apply_default_class_names(data):
        """Applies default class names to an input YAML file or returns numerical class names."""
        with contextlib.suppress(Exception):
            return yaml_load(check_yaml(data))['names']
        return {i: f'class{i}' for i in range(999)}  # return default if above errors

    @staticmethod
    def _model_type(p='path/to/model.pt'):
        """
        This function takes a path to a model file and returns the model type

        Args:
            p: path to the model file. Defaults to path/to/model.pt
        """
        # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
        # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
        from ultralytics.yolo.engine.exporter import export_formats
        sf = list(export_formats().Suffix)  # export suffixes
        if not is_url(p, check=False) and not isinstance(p, str):
            check_suffix(p, sf)  # checks
        url = urlparse(p)  # if url may be Triton inference server
        types = [s in Path(p).name for s in sf]
        types[8] &= not types[9]  # tflite &= not edgetpu
        triton = not any(types) and all([any(s in url.scheme for s in ['http', 'grpc']), url.netloc])
        return types + [triton]

__init__(weights='yolov8n.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True, verbose=True)

MultiBackend class for python inference on various platforms using Ultralytics YOLO.

Parameters:

Name Type Description Default
weights str

The path to the weights file. Default: 'yolov8n.pt'

'yolov8n.pt'
device torch.device

The device to run the model on.

torch.device('cpu')
dnn bool

Use OpenCV's DNN module for inference if True, defaults to False.

False
data str), (Path

Additional data.yaml file for class names, optional

None
fp16 bool

If True, use half precision. Default: False

False
fuse bool

Whether to fuse the model or not. Default: True

True
verbose bool

Whether to run in verbose mode or not. Default: True

True
Supported formats and their naming conventions
Format Suffix
PyTorch *.pt
TorchScript *.torchscript
ONNX Runtime *.onnx
ONNX OpenCV DNN *.onnx dnn=True
OpenVINO *.xml
CoreML *.mlmodel
TensorRT *.engine
TensorFlow SavedModel *_saved_model
TensorFlow GraphDef *.pb
TensorFlow Lite *.tflite
TensorFlow Edge TPU *_edgetpu.tflite
PaddlePaddle *_paddle_model
Source code in ultralytics/nn/autobackend.py
def __init__(self,
             weights='yolov8n.pt',
             device=torch.device('cpu'),
             dnn=False,
             data=None,
             fp16=False,
             fuse=True,
             verbose=True):
    """
    MultiBackend class for python inference on various platforms using Ultralytics YOLO.

    Args:
        weights (str): The path to the weights file. Default: 'yolov8n.pt'
        device (torch.device): The device to run the model on.
        dnn (bool): Use OpenCV's DNN module for inference if True, defaults to False.
        data (str), (Path): Additional data.yaml file for class names, optional
        fp16 (bool): If True, use half precision. Default: False
        fuse (bool): Whether to fuse the model or not. Default: True
        verbose (bool): Whether to run in verbose mode or not. Default: True

    Supported formats and their naming conventions:
        | Format                | Suffix           |
        |-----------------------|------------------|
        | PyTorch               | *.pt             |
        | TorchScript           | *.torchscript    |
        | ONNX Runtime          | *.onnx           |
        | ONNX OpenCV DNN       | *.onnx dnn=True  |
        | OpenVINO              | *.xml            |
        | CoreML                | *.mlmodel        |
        | TensorRT              | *.engine         |
        | TensorFlow SavedModel | *_saved_model    |
        | TensorFlow GraphDef   | *.pb             |
        | TensorFlow Lite       | *.tflite         |
        | TensorFlow Edge TPU   | *_edgetpu.tflite |
        | PaddlePaddle          | *_paddle_model   |
    """
    super().__init__()
    w = str(weights[0] if isinstance(weights, list) else weights)
    nn_module = isinstance(weights, torch.nn.Module)
    pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
    fp16 &= pt or jit or onnx or engine or nn_module or triton  # FP16
    nhwc = coreml or saved_model or pb or tflite or edgetpu  # BHWC formats (vs torch BCWH)
    stride = 32  # default stride
    model, metadata = None, None
    cuda = torch.cuda.is_available() and device.type != 'cpu'  # use CUDA
    if not (pt or triton or nn_module):
        w = attempt_download_asset(w)  # download if not local

    # NOTE: special case: in-memory pytorch model
    if nn_module:
        model = weights.to(device)
        model = model.fuse(verbose=verbose) if fuse else model
        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()
        self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
        pt = True
    elif pt:  # PyTorch
        from ultralytics.nn.tasks import attempt_load_weights
        model = attempt_load_weights(weights if isinstance(weights, list) else w,
                                     device=device,
                                     inplace=True,
                                     fuse=fuse)
        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()
        self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
    elif jit:  # TorchScript
        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()))
    elif dnn:  # ONNX OpenCV DNN
        LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
        check_requirements('opencv-python>=4.5.4')
        net = cv2.dnn.readNetFromONNX(w)
    elif onnx:  # ONNX Runtime
        LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
        check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
        import onnxruntime
        providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
        session = onnxruntime.InferenceSession(w, providers=providers)
        output_names = [x.name for x in session.get_outputs()]
        metadata = session.get_modelmeta().custom_metadata_map  # metadata
    elif xml:  # OpenVINO
        LOGGER.info(f'Loading {w} for OpenVINO inference...')
        check_requirements('openvino')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
        from openvino.runtime import Core, Layout, get_batch  # noqa
        ie = Core()
        w = Path(w)
        if not w.is_file():  # if not *.xml
            w = next(w.glob('*.xml'))  # get *.xml file from *_openvino_model dir
        network = ie.read_model(model=str(w), weights=w.with_suffix('.bin'))
        if network.get_parameters()[0].get_layout().empty:
            network.get_parameters()[0].set_layout(Layout('NCHW'))
        batch_dim = get_batch(network)
        if batch_dim.is_static:
            batch_size = batch_dim.get_length()
        executable_network = ie.compile_model(network, device_name='CPU')  # device_name="MYRIAD" for NCS2
        metadata = w.parent / 'metadata.yaml'
    elif engine:  # TensorRT
        LOGGER.info(f'Loading {w} for TensorRT inference...')
        try:
            import tensorrt as trt  # noqa https://developer.nvidia.com/nvidia-tensorrt-download
        except ImportError:
            if LINUX:
                check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
            import tensorrt as trt  # noqa
        check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=7.0.0
        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:
            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
            model = runtime.deserialize_cuda_engine(f.read())  # read engine
        context = model.create_execution_context()
        bindings = OrderedDict()
        output_names = []
        fp16 = False  # default updated below
        dynamic = False
        for i in range(model.num_bindings):
            name = model.get_binding_name(i)
            dtype = trt.nptype(model.get_binding_dtype(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)[2]))
                if dtype == np.float16:
                    fp16 = True
            else:  # output
                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())
        batch_size = bindings['images'].shape[0]  # if dynamic, this is instead max batch size
    elif coreml:  # CoreML
        LOGGER.info(f'Loading {w} for CoreML inference...')
        import coremltools as ct
        model = ct.models.MLModel(w)
        metadata = dict(model.user_defined_metadata)
    elif saved_model:  # TF SavedModel
        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'
    elif pb:  # GraphDef 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.yolo.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))
    elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_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
            LOGGER.info(f'Loading {w} 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)])
        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
        with contextlib.suppress(zipfile.BadZipFile):
            with zipfile.ZipFile(w, 'r') as model:
                meta_file = model.namelist()[0]
                metadata = ast.literal_eval(model.read(meta_file).decode('utf-8'))
    elif tfjs:  # TF.js
        raise NotImplementedError('YOLOv8 TF.js inference is not supported')
    elif paddle:  # PaddlePaddle
        LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
        check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
        import paddle.inference as pdi  # noqa
        w = Path(w)
        if not w.is_file():  # if not *.pdmodel
            w = next(w.rglob('*.pdmodel'))  # get *.pdmodel file from *_paddle_model dir
        config = pdi.Config(str(w), str(w.with_suffix('.pdiparams')))
        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.parents[1] / 'metadata.yaml'
    elif triton:  # NVIDIA Triton Inference Server
        LOGGER.info('Triton Inference Server not supported...')
        '''
        TODO:
        check_requirements('tritonclient[all]')
        from utils.triton import TritonRemoteModel
        model = TritonRemoteModel(url=w)
        nhwc = model.runtime.startswith("tensorflow")
        '''
    else:
        from ultralytics.yolo.engine.exporter import export_formats
        raise TypeError(f"model='{w}' is not a supported model format. "
                        'See https://docs.ultralytics.com/modes/predict for help.'
                        f'\n\n{export_formats()}')

    # Load external metadata YAML
    if isinstance(metadata, (str, Path)) and Path(metadata).exists():
        metadata = yaml_load(metadata)
    if metadata:
        for k, v in metadata.items():
            if k in ('stride', 'batch'):
                metadata[k] = int(v)
            elif k in ('imgsz', 'names', 'kpt_shape') 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')
    elif not (pt or triton or nn_module):
        LOGGER.warning(f"WARNING ⚠️ Metadata not found for 'model={weights}'")

    # Check names
    if 'names' not in locals():  # names missing
        names = self._apply_default_class_names(data)
    names = check_class_names(names)

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

forward(im, augment=False, visualize=False)

Runs inference on the YOLOv8 MultiBackend model.

Parameters:

Name Type Description Default
im torch.Tensor

The image tensor to perform inference on.

required
augment bool

whether to perform data augmentation during inference, defaults to False

False
visualize bool

whether to visualize the output predictions, defaults to False

False

Returns:

Type Description
tuple

Tuple containing the raw output tensor, and processed output for visualization (if visualize=True)

Source code in ultralytics/nn/autobackend.py
def forward(self, im, augment=False, visualize=False):
    """
    Runs inference on the YOLOv8 MultiBackend model.

    Args:
        im (torch.Tensor): The image tensor to perform inference on.
        augment (bool): whether to perform data augmentation during inference, defaults to False
        visualize (bool): whether to visualize the output predictions, defaults to False

    Returns:
        (tuple): Tuple containing the raw output tensor, and processed output for visualization (if visualize=True)
    """
    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)

    if self.pt or self.nn_module:  # PyTorch
        y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
    elif self.jit:  # TorchScript
        y = self.model(im)
    elif self.dnn:  # ONNX OpenCV DNN
        im = im.cpu().numpy()  # torch to numpy
        self.net.setInput(im)
        y = self.net.forward()
    elif self.onnx:  # ONNX Runtime
        im = im.cpu().numpy()  # torch to numpy
        y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
    elif self.xml:  # OpenVINO
        im = im.cpu().numpy()  # FP32
        y = list(self.executable_network([im]).values())
    elif self.engine:  # TensorRT
        if self.dynamic and im.shape != self.bindings['images'].shape:
            i = self.model.get_binding_index('images')
            self.context.set_binding_shape(i, im.shape)  # reshape if dynamic
            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)]
    elif self.coreml:  # CoreML
        im = im[0].cpu().numpy()
        im_pil = Image.fromarray((im * 255).astype('uint8'))
        # im = im.resize((192, 320), Image.ANTIALIAS)
        y = self.model.predict({'image': im_pil})  # coordinates are xywh normalized
        if 'confidence' in y:
            box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels
            conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
            y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
        elif len(y) == 1:  # classification model
            y = list(y.values())
        elif len(y) == 2:  # segmentation model
            y = list(reversed(y.values()))  # reversed for segmentation models (pred, proto)
    elif self.paddle:  # PaddlePaddle
        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]
    elif self.triton:  # NVIDIA Triton Inference Server
        y = self.model(im)
    else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
        im = im.cpu().numpy()
        if self.saved_model:  # SavedModel
            y = self.model(im, training=False) if self.keras else self.model(im)
            if not isinstance(y, list):
                y = [y]
        elif self.pb:  # GraphDef
            y = self.frozen_func(x=self.tf.constant(im))
            if len(y) == 2 and len(self.names) == 999:  # segments and names not defined
                ip, ib = (0, 1) if len(y[0].shape) == 4 else (1, 0)  # index of protos, boxes
                nc = y[ib].shape[1] - y[ip].shape[3] - 4  # y = (1, 160, 160, 32), (1, 116, 8400)
                self.names = {i: f'class{i}' for i in range(nc)}
        else:  # Lite or Edge TPU
            input = self.input_details[0]
            int8 = input['dtype'] == np.int8  # is TFLite quantized int8 model
            if int8:
                scale, zero_point = input['quantization']
                im = (im / scale + zero_point).astype(np.int8)  # de-scale
            self.interpreter.set_tensor(input['index'], im)
            self.interpreter.invoke()
            y = []
            for output in self.output_details:
                x = self.interpreter.get_tensor(output['index'])
                if int8:
                    scale, zero_point = output['quantization']
                    x = (x.astype(np.float32) - zero_point) * scale  # re-scale
                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)
            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]
        # y[0][..., :4] *= [w, h, w, h]  # xywh normalized to pixels

    # 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)):
        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(x)

Convert a numpy array to a tensor.

Parameters:

Name Type Description Default
x np.ndarray

The array to be converted.

required

Returns:

Type Description
torch.Tensor

The converted tensor

Source code in ultralytics/nn/autobackend.py
def from_numpy(self, x):
    """
     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(imgsz=(1, 3, 640, 640))

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)

Returns:

Type Description
None

This method runs the forward pass and don't return any value

Source code in ultralytics/nn/autobackend.py
def warmup(self, imgsz=(1, 3, 640, 640)):
    """
    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)

    Returns:
        (None): This method runs the forward pass and don't return any value
    """
    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



check_class_names


Check class names. Map imagenet class codes to human-readable names if required. Convert lists to dicts.

Source code in ultralytics/nn/autobackend.py
def check_class_names(names):
    """Check class names. Map imagenet class codes to human-readable names if required. Convert lists to dicts."""
    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'
            map = yaml_load(ROOT / 'datasets/ImageNet.yaml')['map']  # human-readable names
            names = {k: map[v] for k, v in names.items()}
    return names




Created 2023-04-16, Updated 2023-05-17
Authors: Glenn Jocher (3)