ncalab.losses
=============

.. py:module:: ncalab.losses


Classes
-------

.. autoapisummary::

   ncalab.losses.DiceScore
   ncalab.losses.DiceLoss
   ncalab.losses.DiceBCELoss
   ncalab.losses.FocalLoss


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

.. py:class:: DiceScore

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


   Pytorch Module that computes the Dice overlap score between two images.

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


   .. py:method:: forward(x: torch.Tensor, y: torch.Tensor, smooth: float = 1.0) -> torch.Tensor

      :param x: Reference Input
      :type x: torch.Tensor
      :param y: Other Input
      :type y: torch.Tensor
      :param smooth: Smooting factor, defaults to 1.0
      :type smooth: float

      :returns: Dice score
      :rtype: torch.Tensor



.. py:class:: DiceLoss

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


   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:: dicescore


   .. py:method:: forward(x: torch.Tensor, y: torch.Tensor, smooth: float = 1.0) -> torch.Tensor


.. py:class:: DiceBCELoss

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


   Combination of Dice and BCE Loss between two images.

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


   .. py:attribute:: dicescore


   .. py:method:: forward(x: torch.Tensor, y: torch.Tensor, smooth: float = 1.0) -> torch.Tensor

      :param x: Reference Input
      :type x: torch.Tensor
      :param y: Other Input
      :type y: torch.Tensor
      :param smooth: Smooting factor, defaults to 1.0
      :type smooth: float

      :returns: Dice score
      :rtype: torch.Tensor



.. py:class:: FocalLoss(weight=None, gamma=2, device='cpu')

   Bases: :py:obj:`torch.nn.modules.loss._WeightedLoss`


   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:: gamma
      :value: 2



   .. py:attribute:: weight
      :value: None



   .. py:attribute:: device
      :value: 'cpu'



   .. py:attribute:: ce_loss


   .. py:method:: forward(_input, _target)