hyrax.models.image_dcae
=======================

.. py:module:: hyrax.models.image_dcae


Classes
-------

.. autoapisummary::

   hyrax.models.image_dcae.ArcsinhActivation
   hyrax.models.image_dcae.ImageDCAE


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

.. py:class:: ArcsinhActivation(*args: Any, **kwargs: Any)

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


   Helper module for ImageDCAE to use the arcsinh function

   Initialize internal Module state, shared by both nn.Module and ScriptModule.


   .. py:method:: forward(x)


.. py:class:: ImageDCAE(config, data_sample=None)

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


   This is an autoencoder with skipconnections that should work with
   arbitarily sized images with arbitrary number of channels.

   Initialize internal Module state, shared by both nn.Module and ScriptModule.


   .. py:attribute:: input_shape


   .. py:attribute:: config


   .. py:attribute:: latent_dim


   .. py:attribute:: base_channel_size


   .. py:attribute:: conv_output_size


   .. py:attribute:: encoder1


   .. py:attribute:: encoder2


   .. py:attribute:: encoder3


   .. py:attribute:: encoder4


   .. py:attribute:: pool


   .. py:attribute:: latent_encoder


   .. py:attribute:: latent_decoder


   .. py:attribute:: decoder4


   .. py:attribute:: decoder3


   .. py:attribute:: decoder2


   .. py:attribute:: decoder1


   .. py:attribute:: activation


   .. py:method:: _calculate_conv_output_size()

      Calculate the output size after all convolutional layers for the linear bottleneck.



   .. py:method:: encode(x)

      Encode input to latent space with skip connections.



   .. py:method:: decode(latent, skip_connections, encoded_shape)

      Decode from latent space to image with skip connections.



   .. py:method:: forward(x)

      Forward pass - returns latent representation for anomaly detection.



   .. py:method:: reconstruct(x)

      Full reconstruction for evaluation and anomaly detection.



   .. py:method:: train_batch(batch)

      This function contains the logic for a single training step.

      :param batch: A tuple containing the input data for the current batch, possibly
                    with labels that are ignored.
      :type batch: tuple

      :returns: **Current loss value** -- Dictionary containing the loss value for the current batch.
      :rtype: dict



   .. py:method:: validate_batch(batch)

      This function contains the logic for a single validation step that will
      process a single batch of data.

      :param batch: A tuple containing the input data for the current batch, possibly
                    with labels that are ignored.
      :type batch: tuple

      :returns: **Current loss value** -- Dictionary containing the loss value for the current batch.
      :rtype: dict



   .. py:method:: test_batch(batch)

      This function contains the logic for a single testing step that will
      process a single batch of data. In this case, it is identical to `validate_batch`.

      :param batch: A tuple containing the input data for the current batch, possibly
                    with labels that are ignored.
      :type batch: tuple

      :returns: **Current loss value** -- Dictionary containing the loss value for the current batch.
      :rtype: dict



   .. py:method:: infer_batch(batch)

      This function contains the logic for a single inference step.

      :param batch: A tuple containing the input data for the current batch, possibly
                    with labels that are ignored.
      :type batch: tuple

      :returns: **Reconstructed images** -- Tensor containing the reconstructed images for the current batch.
      :rtype: torch.Tensor



   .. py:method:: prepare_inputs(data_dict)
      :staticmethod:


      Extract the image array from the batch dictionary.

      This static method is the interface between the data pipeline and the
      model. Override it on the model class to reshape or select fields from
      the collated batch to match the inputs your model expects.

      Hyrax will convert the returned array to a PyTorch tensor and move it
      to the appropriate device automatically.

      :param data_dict: The collated batch dictionary produced by the data pipeline.
                        Expected to contain a ``"data"`` key with an ``"image"`` field.
      :type data_dict: dict

      :returns: **image** -- The image array extracted from the batch.
      :rtype: numpy.ndarray