ncalab.models.applications.depth
================================

.. py:module:: ncalab.models.applications.depth


Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/ncalab/models/applications/depth/depthNCA/index


Classes
-------

.. autoapisummary::

   ncalab.models.applications.depth.DepthNCAModel


Package Contents
----------------

.. py:class:: DepthNCAModel(device: torch.device, num_image_channels: int = 3, num_hidden_channels: int = 18, fire_rate: float = 0.8, hidden_size: int = 128, num_learned_filters: int = 2, pad_noise: bool = False, **kwargs)

   Bases: :py:obj:`ncalab.models.basicNCA.BasicNCAModel`


   NCA model for monocular depth estimation.

   :param device: Pytorch device descriptor.
   :param num_image_channels: Number of channels reserved for input image.
   :param num_hidden_channels: Number of hidden channels (communication channels).
   :param num_output_channels: Number of output channels.
   :param fire_rate: Fire rate for stochastic weight update. Defaults to 0.5.
   :param hidden_size: Number of neurons in hidden layer. Defaults to 128.
   :param use_alive_mask: Whether to use alive masking (channel 3) during training. Defaults to False.
   :param immutable_image_channels: If image channels should be fixed during inference, which is the case for most segmentation or classification problems. Defaults to True.
   :param num_learned_filters: Number of learned filters. If zero, use two sobel filters instead. Defaults to 2.
   :param filter_padding: Padding type to use. Might affect reliance on spatial cues. Defaults to "circular".
   :param use_laplace: Whether to use Laplace filter (only if num_learned_filters == 0)
   :param kernel_size: Filter kernel size (only for learned filters)
   :param pad_noise: Whether to pad input image tensor with noise in hidden / output channels


   .. py:attribute:: vignette
      :value: None



   .. py:method:: loss(pred: ncalab.prediction.Prediction, label: torch.Tensor) -> Dict[str, torch.Tensor]

      :param image: Input image, BCWH.
      :param label: Ground truth.

      :returns: Dictionary of identifiers mapped to computed losses.



   .. py:method:: metrics(pred: ncalab.prediction.Prediction, label: torch.Tensor) -> Dict[str, float]

      Return dict of standard evaluation metrics.
      Needs to include special item 'prediction', containing the predicted image (all channels).

      :param pred:
      :param label: Ground truth label.

      :returns [Dict]: Dict of metrics, mapped by their names.