vision_unlearning.metrics.image_and_image
=========================================

.. py:module:: vision_unlearning.metrics.image_and_image


Classes
-------

.. autoapisummary::

   vision_unlearning.metrics.image_and_image.MetricImageImage


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

.. py:class:: MetricImageImage(/, **data: Any)

   Bases: :py:obj:`vision_unlearning.metrics.base.Metric`


   !!! abstract "Usage Documentation"
       [Models](../concepts/models.md)

   A base class for creating Pydantic models.

   .. attribute:: __class_vars__

      The names of the class variables defined on the model.

   .. attribute:: __private_attributes__

      Metadata about the private attributes of the model.

   .. attribute:: __signature__

      The synthesized `__init__` [`Signature`][inspect.Signature] of the model.

   .. attribute:: __pydantic_complete__

      Whether model building is completed, or if there are still undefined fields.

   .. attribute:: __pydantic_core_schema__

      The core schema of the model.

   .. attribute:: __pydantic_custom_init__

      Whether the model has a custom `__init__` function.

   .. attribute:: __pydantic_decorators__

      Metadata containing the decorators defined on the model.
      This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.

   .. attribute:: __pydantic_generic_metadata__

      A dictionary containing metadata about generic Pydantic models.
      The `origin` and `args` items map to the [`__origin__`][genericalias.__origin__]
      and [`__args__`][genericalias.__args__] attributes of [generic aliases][types-genericalias],
      and the `parameter` item maps to the `__parameter__` attribute of generic classes.

   .. attribute:: __pydantic_parent_namespace__

      Parent namespace of the model, used for automatic rebuilding of models.

   .. attribute:: __pydantic_post_init__

      The name of the post-init method for the model, if defined.

   .. attribute:: __pydantic_root_model__

      Whether the model is a [`RootModel`][pydantic.root_model.RootModel].

   .. attribute:: __pydantic_serializer__

      The `pydantic-core` `SchemaSerializer` used to dump instances of the model.

   .. attribute:: __pydantic_validator__

      The `pydantic-core` `SchemaValidator` used to validate instances of the model.

   .. attribute:: __pydantic_fields__

      A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects.

   .. attribute:: __pydantic_computed_fields__

      A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.

   .. attribute:: __pydantic_extra__

      A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra]
      is set to `'allow'`.

   .. attribute:: __pydantic_fields_set__

      The names of fields explicitly set during instantiation.

   .. attribute:: __pydantic_private__

      Values of private attributes set on the model instance.


   .. py:attribute:: _loss_alex
      :type:  Optional[lpips.lpips.LPIPS]


   .. py:attribute:: _loss_vgg
      :type:  Optional[lpips.lpips.LPIPS]


   .. py:attribute:: metrics
      :type:  List[Literal['rmse', 'psnr', 'ssim', 'fsim', 'issm', 'sre', 'sam', 'uiq', 'lpips_alex', 'lpips_vgg']]


   .. py:method:: model_post_init(__context: Optional[dict] = None) -> None

      Override this method to perform additional initialization after `__init__` and `model_construct`.
      This is useful if you want to do some validation that requires the entire model to be initialized.



   .. py:method:: _evaluate_lpips(org_img_path: str, pred_img_path: str, loss_fn: lpips.lpips.LPIPS) -> float


   .. py:method:: _score_from_paths(org_img_path: str, pred_img_path: str) -> Dict[str, float]


   .. py:method:: _pil_or_array_to_path(img: Union[str, PIL.Image.Image, numpy.ndarray]) -> tuple[str, bool]
      :staticmethod:


      Return (file_path, is_temp).  Caller must unlink when is_temp is True.



   .. py:method:: score(org_img: Union[str, PIL.Image.Image], pred_img: Union[str, PIL.Image.Image]) -> Dict[str, float]


   .. py:method:: score_batch(org_imgs: List[Union[str, PIL.Image.Image]], pred_imgs: List[Union[str, PIL.Image.Image]]) -> List[Dict[str, float]]

      Warning: this function don't improve performance. The underlying libraries still work serially.
      Returns per-pair results in the same order.