dacapo.experiments.tasks.losses.affinities_loss
===============================================

.. py:module:: dacapo.experiments.tasks.losses.affinities_loss


Classes
-------

.. autoapisummary::

   dacapo.experiments.tasks.losses.affinities_loss.AffinitiesLoss


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

.. py:class:: AffinitiesLoss(num_affinities: int, lsds_to_affs_weight_ratio: float)



   A class representing a loss function that calculates the loss between affinities and local shape descriptors (LSDs).

   .. attribute:: num_affinities

      int
      the number of affinities

   .. attribute:: lsds_to_affs_weight_ratio

      float
      the ratio of the weight of the loss between affinities and LSDs

   .. method:: compute(prediction, target, weight=None)

      
      Calculate the total loss between prediction and target.

   .. note:: The AffinitiesLoss class is used to calculate the loss between affinities and local shape descriptors (LSDs).


   .. py:attribute:: num_affinities


   .. py:attribute:: lsds_to_affs_weight_ratio


   .. py:method:: compute(prediction, target, weight)

      Method to calculate the total loss between affinities and LSDs.

      :param prediction: torch.Tensor
                         the model's prediction
      :param target: torch.Tensor
                     the target values
      :param weight: torch.Tensor
                     the weight to apply to the loss

      :returns:

                torch.Tensor
                    the total loss between affinities and LSDs

      :raises ValueError: if the number of affinities in the prediction and target does not match

      .. rubric:: Examples

      >>> affinities_loss = AffinitiesLoss(3, 0.5)
      >>> prediction = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]])
      >>> target = torch.tensor([[9, 10, 11, 12], [13, 14, 15, 16]])
      >>> weight = torch.tensor([[1, 1, 1, 1], [1, 1, 1, 1]])
      >>> affinities_loss.compute(prediction, target, weight)
      tensor(0.5)

      .. note:: The AffinitiesLoss class is used to calculate the loss between affinities and local shape descriptors (LSDs).