minerva.models.nets.image.deeplabv3
===================================

.. py:module:: minerva.models.nets.image.deeplabv3


Classes
-------

.. autoapisummary::

   minerva.models.nets.image.deeplabv3.DeepLabV3
   minerva.models.nets.image.deeplabv3.DeepLabV3Backbone
   minerva.models.nets.image.deeplabv3.DeepLabV3PredictionHead


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

.. py:class:: DeepLabV3(backbone = None, pred_head = None, loss_fn = None, learning_rate = 0.001, num_classes = 6, train_metrics = None, val_metrics = None, test_metrics = None, optimizer = torch.optim.Adam, optimizer_kwargs = None, lr_scheduler = None, lr_scheduler_kwargs = None, output_shape = None)

   Bases: :py:obj:`minerva.models.nets.base.SimpleSupervisedModel`


   A DeeplabV3 with a ResNet50 backbone

   References
   ----------
   Liang-Chieh Chen, George Papandreou, Florian Schroff, Hartwig Adam.
   "Rethinking Atrous Convolution for Semantic Image Segmentation", 2017

   Initializes a DeepLabV3 model.

   Parameters
   ----------
   backbone: Optional[nn.Module]
       The backbone network. Defaults to None, which will use a ResNet50
       backbone.
   pred_head: Optional[nn.Module]
       The prediction head network. Defaults to None, which will use a
       DeepLabV3PredictionHead with specified number of classes.
   loss_fn: Optional[nn.Module]
       The loss function. Defaults to None, which will use a
       CrossEntropyLoss.
   learning_rate: float
       The learning rate for the optimizer. Defaults to 0.001.
   num_classes: int
       The number of classes for prediction. Defaults to 6.
   train_metrics: Optional[Dict[str, Metric]]
       The metrics to be computed during training. Defaults to None.
   val_metrics: Optional[Dict[str, Metric]]
       The metrics to be computed during validation. Defaults to None.
   test_metrics: Optional[Dict[str, Metric]]
       The metrics to be computed during testing. Defaults to None.
   optimizer: type
       Optimizer class to be instantiated. By default, it is set to
       `torch.optim.Adam`. Should be a subclass of
       `torch.optim.Optimizer` (e.g., `torch.optim.SGD`).
   optimizer_kwargs : dict, optional
       Additional kwargs passed to the optimizer constructor.
   lr_scheduler : type, optional
       Learning rate scheduler class to be instantiated. By default, it is
       set to None, which means no scheduler will be used. Should be a
       subclass of `torch.optim.lr_scheduler.LRScheduler` (e.g.,
       `torch.optim.lr_scheduler.StepLR`).
   lr_scheduler_kwargs : dict, optional
       Additional kwargs passed to the scheduler constructor.
   output_shape: Optional[Tuple[int, ...]]
       The output shape of the model. If None, the output shape will be
       the same as the input shape. Defaults to None. This is useful for
       models that require a specific output shape, that is different from
       the input shape.


   .. py:method:: _loss_func(y_hat, y)

      Calculate the loss between the output and the input data.

      Parameters
      ----------
      y_hat : torch.Tensor
          The output data from the forward pass.
      y : torch.Tensor
          The input data/label.

      Returns
      -------
      torch.Tensor
          The loss value.



   .. py:method:: forward(x)

      Perform a forward pass with the input data on the backbone model.

      Parameters
      ----------
      x : torch.Tensor
          The input data.

      Returns
      -------
      torch.Tensor
          The output data from the forward pass.



   .. py:attribute:: output_shape
      :value: None



.. py:class:: DeepLabV3Backbone(num_classes = 6)

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


   A ResNet50 backbone for DeepLabV3

   Initializes the DeepLabV3 model.

   Parameters
   ----------
   num_classes: int
       The number of classes for classification. Default is 6.


   .. py:attribute:: RN50model


   .. py:method:: forward(x)


   .. py:method:: freeze_weights()


   .. py:method:: unfreeze_weights()


.. py:class:: DeepLabV3PredictionHead(in_channels = 2048, num_classes = 6, atrous_rates = (12, 24, 36))

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


   The prediction head for DeepLabV3

   Initializes the DeepLabV3 model.

   Parameters
   ----------
   in_channels: int
       Number of input channels. Defaults to 2048.
   num_classes: int
       Number of output classes. Defaults to 6.
   atrous_rates: Sequence[int]
       A sequence of atrous rates for the ASPP module. Defaults to (12, 24, 36).


   .. py:method:: forward(input)