:py:mod:`anomalib.utils.metrics.aupro`
======================================

.. py:module:: anomalib.utils.metrics.aupro

.. autoapi-nested-parse::

   Implementation of AUPRO score based on TorchMetrics.



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

Classes
~~~~~~~

.. autoapisummary::

   anomalib.utils.metrics.aupro.AUPRO




.. py:class:: AUPRO(compute_on_step: bool = True, dist_sync_on_step: bool = False, process_group: Optional[Any] = None, dist_sync_fn: Callable = None, fpr_limit: float = 0.3)

   Bases: :py:obj:`torchmetrics.Metric`

   Area under per region overlap (AUPRO) Metric.

   .. py:attribute:: is_differentiable
      :annotation: :bool = False

      

   .. py:attribute:: higher_is_better
      :annotation: :Optional[bool]

      

   .. py:attribute:: full_state_update
      :annotation: :bool = False

      

   .. py:attribute:: preds
      :annotation: :List[torch.Tensor]

      

   .. py:attribute:: target
      :annotation: :List[torch.Tensor]

      

   .. py:method:: update(preds: torch.Tensor, target: torch.Tensor) -> None

      Update state with new values.

      :param preds: predictions of the model
      :type preds: Tensor
      :param target: ground truth targets
      :type target: Tensor


   .. py:method:: _compute() -> Tuple[torch.Tensor, torch.Tensor]

      Compute the pro/fpr value-pairs until the fpr specified by self.fpr_limit.

      It leverages the fact that the overlap corresponds to the tpr, and thus computes the overall
      PRO curve by aggregating per-region tpr/fpr values produced by ROC-construction.

      :raises ValueError: ValueError is raised if self.target doesn't conform with requirements imposed by kornia for
          connected component analysis.

      :returns: tuple containing final fpr and tpr values.
      :rtype: Tuple[Tensor, Tensor]


   .. py:method:: compute() -> torch.Tensor

      Fist compute PRO curve, then compute and scale area under the curve.

      :returns: Value of the AUPRO metric
      :rtype: Tensor


   .. py:method:: generate_figure() -> Tuple[matplotlib.figure.Figure, str]

      Generate a figure containing the PRO curve and the AUPRO.

      :returns: Tuple containing both the figure and the figure title to be used for logging
      :rtype: Tuple[Figure, str]


   .. py:method:: interp1d(old_x: torch.Tensor, old_y: torch.Tensor, new_x: torch.Tensor) -> torch.Tensor
      :staticmethod:

      Function to interpolate a 1D signal linearly to new sampling points.

      :param old_x: original 1-D x values (same size as y)
      :type old_x: Tensor
      :param old_y: original 1-D y values (same size as x)
      :type old_y: Tensor
      :param new_x: x-values where y should be interpolated at
      :type new_x: Tensor

      :returns: y-values at corresponding new_x values.
      :rtype: Tensor