:py:mod:`anomalib.deploy.inferencers.openvino_inferencer`
=========================================================

.. py:module:: anomalib.deploy.inferencers.openvino_inferencer

.. autoapi-nested-parse::

   This module contains inference-related abstract class and its Torch and OpenVINO implementations.



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   anomalib.deploy.inferencers.openvino_inferencer.OpenVINOInferencer




.. py:class:: OpenVINOInferencer(config: Union[str, pathlib.Path, omegaconf.DictConfig, omegaconf.ListConfig], path: Union[str, pathlib.Path, Tuple[bytes, bytes]], meta_data_path: Union[str, pathlib.Path] = None)

   Bases: :py:obj:`anomalib.deploy.inferencers.base_inferencer.Inferencer`

   OpenVINO implementation for the inference.

   :param config: Configurable parameters that are used
                  during the training stage.
   :type config: Union[str, Path, DictConfig, ListConfig]
   :param path: Path to the openvino onnx, xml or bin file.
   :type path: Union[str, Path]
   :param meta_data_path: Path to metadata file. Defaults to None.
   :type meta_data_path: Union[str, Path], optional

   .. py:method:: load_model(path: Union[str, pathlib.Path, Tuple[bytes, bytes]])

      Load the OpenVINO model.

      :param path: Path to the onnx or xml and bin files
                   or tuple of .xml and .bin data as bytes.
      :type path: Union[str, Path, Tuple[bytes, bytes]]

      :returns:

                Input and Output blob names
                    together with the Executable network.
      :rtype: [Tuple[str, str, ExecutableNetwork]]


   .. py:method:: pre_process(image: numpy.ndarray) -> numpy.ndarray

      Pre process the input image by applying transformations.

      :param image: Input image.
      :type image: np.ndarray

      :returns: pre-processed image.
      :rtype: np.ndarray


   .. py:method:: forward(image: numpy.ndarray) -> numpy.ndarray

      Forward-Pass input tensor to the model.

      :param image: Input tensor.
      :type image: np.ndarray

      :returns: Output predictions.
      :rtype: np.ndarray


   .. py:method:: post_process(predictions: numpy.ndarray, meta_data: Optional[Union[Dict, omegaconf.DictConfig]] = None) -> Dict[str, Any]

      Post process the output predictions.

      :param predictions: Raw output predicted by the model.
      :type predictions: np.ndarray
      :param meta_data: Meta data. Post-processing step sometimes requires
                        additional meta data such as image shape. This variable comprises such info.
                        Defaults to None.
      :type meta_data: Dict, optional

      :returns: Post processed prediction results.
      :rtype: Dict[str, Any]