minerva.models.ssl.barlowtwins
==============================

.. py:module:: minerva.models.ssl.barlowtwins


Classes
-------

.. autoapisummary::

   minerva.models.ssl.barlowtwins.BarlowTwins


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

.. py:class:: BarlowTwins(backbone, projection_head, loss_fn = None, learning_rate = 0.0001, weight_decay = 1e-06)

   Bases: :py:obj:`lightning.LightningModule`


   PyTorch Lightning module implementing the Barlow Twins self-supervised learning framework.

   It accepts a backbone and projection head for feature encoding and embedding,
   uses a contrastive loss (defaulting to BarlowTwinsLoss if none is provided),
   supports standard training and validation loops in PyTorch Lightning,
   and optimizes using the Adam optimizer.

   Initialize the BarlowTwins module.

   Parameters
   ----------
   backbone : nn.Module
       Neural network used to extract features from input images.
   projection_head : nn.Module
       Network that maps backbone outputs to a latent space.
   loss_fn : nn.Module, optional
       Custom loss function. Defaults to BarlowTwinsLoss.
   learning_rate : float, optional
       Learning rate for the optimizer. Default is 0.0001.
   weight_decay : float, optional
       Weight decay (L2 regularization). Default is 1e-6.


   .. py:method:: _single_step(batch)

      Compute the loss for a single batch.

      Parameters
      ----------
      batch : tuple of ((torch.Tensor, torch.Tensor), Any)
          A tuple containing a pair of augmented views (x0, x1) and labels (unused).

      Returns
      -------
      torch.Tensor
          The computed loss for the batch.



   .. py:attribute:: backbone


   .. py:method:: configure_optimizers()

      Configures the Adam optimizer with provided learning rate and weight decay.

      Returns
      -------
      torch.optim.Optimizer
          Configured Adam optimizer.



   .. py:method:: forward(x)

      Forward pass through the model.

      Parameters
      ----------
      x : torch.Tensor
          Input tensor (e.g., batch of images).

      Returns
      -------
      torch.Tensor
          Projected features in the embedding space.



   .. py:attribute:: learning_rate
      :value: 0.0001



   .. py:attribute:: projection_head


   .. py:method:: training_step(batch, batch_idx)

      Defines one training step.

      Parameters
      ----------
      batch : tuple of ((torch.Tensor, torch.Tensor), Any)
          Batch containing two augmented views and labels (unused).
      batch_idx : int
          Index of the batch.

      Returns
      -------
      torch.Tensor
          Training loss for the batch.



   .. py:method:: validation_step(batch, batch_idx)

      Defines one validation step.

      Parameters
      ----------
      batch : tuple of ((torch.Tensor, torch.Tensor), Any)
          Batch containing two augmented views and labels (unused).
      batch_idx : int
          Index of the batch.

      Returns
      -------
      torch.Tensor
          Validation loss for the batch.



   .. py:attribute:: weight_decay
      :value: 1e-06