minerva.models.ssl.simsiam
==========================

.. py:module:: minerva.models.ssl.simsiam


Classes
-------

.. autoapisummary::

   minerva.models.ssl.simsiam.SimSiam


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

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

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


   SimSiam implementation using PyTorch Lightning.

   This class implements the SimSiam self-supervised learning framework, which is designed
   to learn useful representations without using negative samples. It employs a backbone
   encoder, a projection head, and a prediction head to train the backbone.

   Initialize the SimSiam module.

   Parameters
   ----------
   backbone : nn.Module
       The feature extractor network (e.g., a ResNet encoder).
   projection_head : nn.Module, optional
       The network that maps backbone outputs to the projection space.
       If None, a default 3-layer MLP designed to work with ResNet50 is used.
   prediction_head : nn.Module, optional
       The network that maps projection vectors to the prediction space.
       If None, a default 2-layer MLP is used.
   loss_fn : Callable, optional
       Loss function used for training. Default is cosine similarity loss.
   learning_rate : float, optional
       Learning rate for the optimizer. Default is 0.0001.
   weight_decay : float, optional
       Weight decay for the optimizer. Default is 1e-6.


   .. py:method:: _single_step(batch)

      Compute the loss for a single batch.

      Parameters
      ----------
      batch : Tuple[Tuple[Tensor, 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
          The optimizer used for training.



   .. py:method:: forward(x)

      Forward pass through the backbone, projection, and prediction heads.

      Parameters
      ----------
      x : torch.Tensor
          Input tensor of shape (batch_size, channels, height, width).

      Returns
      -------
      Tuple[torch.Tensor, torch.Tensor]
          The detached projection vector `z` and prediction vector `p`.



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



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

      Defines one training step.

      Parameters
      ----------
      batch : Tuple[Tuple[Tensor, 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[Tuple[Tensor, 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