:py:mod:`anomalib.models.stfpm.anomaly_map`
===========================================

.. py:module:: anomalib.models.stfpm.anomaly_map

.. autoapi-nested-parse::

   Anomaly Map Generator for the STFPM model implementation.



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

Classes
~~~~~~~

.. autoapisummary::

   anomalib.models.stfpm.anomaly_map.AnomalyMapGenerator




.. py:class:: AnomalyMapGenerator(image_size: Union[omegaconf.ListConfig, Tuple])

   Bases: :py:obj:`torch.nn.Module`

   Generate Anomaly Heatmap.

   .. py:method:: compute_layer_map(teacher_features: torch.Tensor, student_features: torch.Tensor) -> torch.Tensor

      Compute the layer map based on cosine similarity.

      :param teacher_features: Teacher features
      :type teacher_features: Tensor
      :param student_features: Student features
      :type student_features: Tensor

      :returns: Anomaly score based on cosine similarity.


   .. py:method:: compute_anomaly_map(teacher_features: Dict[str, torch.Tensor], student_features: Dict[str, torch.Tensor]) -> torch.Tensor

      Compute the overall anomaly map via element-wise production the interpolated anomaly maps.

      :param teacher_features: Teacher features
      :type teacher_features: Dict[str, Tensor]
      :param student_features: Student features
      :type student_features: Dict[str, Tensor]

      :returns: Final anomaly map


   .. py:method:: forward(**kwargs: Dict[str, torch.Tensor]) -> torch.Tensor

      Returns anomaly map.

      Expects `teach_features` and `student_features` keywords to be passed explicitly.

      .. rubric:: Example

      >>> anomaly_map_generator = AnomalyMapGenerator(image_size=tuple(hparams.model.input_size))
      >>> output = self.anomaly_map_generator(
              teacher_features=teacher_features,
              student_features=student_features
          )

      :raises ValueError: `teach_features` and `student_features` keys are not found

      :returns: anomaly map
      :rtype: torch.Tensor