ncalab.models.applications.segmentation
=======================================

.. py:module:: ncalab.models.applications.segmentation


Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/ncalab/models/applications/segmentation/segmentationNCA/index


Classes
-------

.. autoapisummary::

   ncalab.models.applications.segmentation.SegmentationNCAModel


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

.. py:class:: SegmentationNCAModel(device: torch.device, num_image_channels: int = 3, num_hidden_channels: int = 16, num_classes: int = 1, fire_rate: float = 0.8, hidden_size: int = 128, num_learned_filters: int = 2, pad_noise: bool = True, filter_padding: str = 'reflect', **kwargs)

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


   Model used for image segmentation.

   Uses Dice score as the default validation metric. Currently, only binary segmentation
   masks are supported.

   :param device [torch.device]: Compute device.
   :param num_image_channels [int]: Number of image channels. Defaults to 3.
   :param num_hidden_channels [int]: Number of hidden channels. Defaults to 16.
   :param num_classes [int]: Number of classes. Defaults to 1.
   :param fire_rate [float]: NCA fire rate. Defaults to 0.8.
   :param hidden_size [int]: Number of neurons in hidden layer. Defaults to 128.
   :param learned_filters [int]: Number of learned filters. If 0, use sobel. Defaults to 2.
   :param pad_noise [bool]: Whether to pad input images with noise. Defaults to True.
   :param filter_padding [str]: Padding type to use. Might affect reliance on spatial cues. Defaults to "circular".


   .. py:attribute:: num_classes
      :value: 1



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

      Compute Dice + BCE loss.

      :param pred: Prediction.
      :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.

      :param pred [torch.Tensor]: Predicted image.
      :param label [torch.Tensor]: Ground truth label.