credit.models.debugger_model
============================

.. py:module:: credit.models.debugger_model


Attributes
----------

.. autoapisummary::

   credit.models.debugger_model.logger
   credit.models.debugger_model.image_height


Classes
-------

.. autoapisummary::

   credit.models.debugger_model.DebuggerModel


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

.. py:data:: logger

.. py:class:: DebuggerModel(image_height: int = 640, image_width: int = 1280, frames: int = 2, channels: int = 4, surface_channels: int = 7, input_only_channels: int = 3, output_only_channels: int = 0, levels: int = 15, post_conf: dict = None, **kwargs)

   Bases: :py:obj:`credit.models.base_model.BaseModel`


   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


   .. py:attribute:: image_height
      :value: 640



   .. py:attribute:: image_width
      :value: 1280



   .. py:attribute:: frames
      :value: 2



   .. py:attribute:: channels
      :value: 4



   .. py:attribute:: surface_channels
      :value: 7



   .. py:attribute:: levels
      :value: 15



   .. py:attribute:: input_only_channels
      :value: 3



   .. py:attribute:: output_only_channels
      :value: 0



   .. py:attribute:: linear


   .. py:attribute:: use_post_block


   .. py:method:: forward(x)

      forward that multiplies self.coef to the input
      used to test postblock and other model parts



.. py:data:: image_height
   :value: 640