ncalab.models.applications.classification
=========================================

.. py:module:: ncalab.models.applications.classification


Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/ncalab/models/applications/classification/classificationNCA/index
   /autoapi/ncalab/models/applications/classification/classificationNCAhead/index


Classes
-------

.. autoapisummary::

   ncalab.models.applications.classification.ClassificationNCAModel
   ncalab.models.applications.classification.ClassificationNCAHead


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

.. py:class:: ClassificationNCAModel(device: torch.device, num_image_channels: int, num_hidden_channels: int, num_classes: int, fire_rate: float = 0.8, hidden_size: int = 128, use_alive_mask: bool = False, pixel_wise_loss: bool = False, num_learned_filters: int = 2, filter_padding: Literal['zero', 'reflect', 'replicate', 'circular'] = 'reflect', use_laplace: bool = False, kernel_size: int = 3, pad_noise: bool = False, use_temporal_encoding: bool = False, use_classifier: bool = True, class_names: Optional[List[str]] = None, avg_pool_size: int = 8, lambda_hidden: float = 0, **kwargs)

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


   Abstract base class for NCA models.

   BasicNCAModel is a composition of an NCA backbone model (called "rule"), and
   an (optional) head module for downstream tasks.

   :param device: Pytorch device descriptor.
   :param num_image_channels: _description_
   :param num_hidden_channels: _description_
   :param num_classes: _description_
   :param fire_rate: Fire rate for stochastic weight update. Defaults to 0.8.
   :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 pixel_wise_loss: Whether a prediction per pixel is desired, like in self-classifying MNIST. Defaults to False.
   :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 pad_noise: Whether to pad input image tensor with noise in hidden / output channels


   .. py:attribute:: _num_classes


   .. py:attribute:: pixel_wise_loss
      :value: False



   .. py:attribute:: use_classifier
      :value: True



   .. py:attribute:: avg_pool_size
      :value: 8



   .. py:attribute:: lambda_hidden
      :value: 0



   .. py:attribute:: metrics


   .. py:attribute:: focal_loss


   .. py:property:: num_classes
      :type: int



   .. py:method:: classify(image: torch.Tensor, steps: int = 100, reduce: bool = False) -> torch.Tensor

      Predict classification for an input image.

      :param image: Input image.
      :param steps: Inference steps. Defaults to 100.
      :param reduce: Return a single softmax probability. Defaults to False.

      :returns: Single class index or vector of logits.



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

      Return the classification loss.

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

      :returns: Dictionary of identifiers mapped to computed losses.



   .. py:method:: post_prediction(prediction: ncalab.prediction.Prediction) -> ncalab.prediction.Prediction


.. py:class:: ClassificationNCAHead(num_hidden_channels: int, num_classes: int, device: torch.device, avg_pool_size: int, hidden_size: int = 32)

   Bases: :py:obj:`ncalab.models.basicNCA.abstractNCAhead.AbstractNCAHead`


   Base class for all neural network modules.

   Your models should also subclass this class.

   Modules can also contain other Modules, allowing them to be nested in
   a tree structure. You can assign the submodules as regular attributes::

       import torch.nn as nn
       import torch.nn.functional as F


       class Model(nn.Module):
           def __init__(self) -> None:
               super().__init__()
               self.conv1 = nn.Conv2d(1, 20, 5)
               self.conv2 = nn.Conv2d(20, 20, 5)

           def forward(self, x):
               x = F.relu(self.conv1(x))
               return F.relu(self.conv2(x))

   Submodules assigned in this way will be registered, and will also have their
   parameters converted when you call :meth:`to`, etc.

   .. note::
       As per the example above, an ``__init__()`` call to the parent class
       must be made before assignment on the child.

   :ivar training: Boolean represents whether this module is in training or
                   evaluation mode.
   :vartype training: bool

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


   .. py:attribute:: num_hidden_channels


   .. py:attribute:: num_classes


   .. py:attribute:: device


   .. py:attribute:: avg_pool_size


   .. py:attribute:: classifier


   .. py:method:: forward(x)

      :param x: Input tensor
      :type x: torch.Tensor

      :returns: NotImplemented, subclasses are required to implement this method.



   .. py:method:: freeze(freeze_last: bool = False)

      Freeze head weights.

      :param freeze_last: Whether to freeze the last layer (if applicable), defaults to True
      :type freeze_last: bool, optional
      :returns: NotImplemented, subclasses are required to implement this method.