emmi_inference.models.abupt
===========================

.. py:module:: emmi_inference.models.abupt


Classes
-------

.. autoapisummary::

   emmi_inference.models.abupt.AnchoredBranchedUPT


Functions
---------

.. autoapisummary::

   emmi_inference.models.abupt.main


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

.. py:class:: AnchoredBranchedUPT(ndim = 3, input_dim = 3, output_dim_surface = 4, output_dim_volume = 7, dim = 192, geometry_depth = 1, num_heads = 3, blocks = 'pscscs', num_volume_blocks = 6, num_surface_blocks = 6, radius = 0.25, **kwargs)

   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 to nest them 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):
               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 have their
   parameters converted too 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:: rope


   .. py:attribute:: encoder


   .. py:attribute:: geometry_blocks


   .. py:attribute:: pos_embed


   .. py:attribute:: surface_bias


   .. py:attribute:: volume_bias


   .. py:attribute:: blocks


   .. py:attribute:: surface_blocks


   .. py:attribute:: volume_blocks


   .. py:attribute:: surface_decoder


   .. py:attribute:: volume_decoder


   .. py:method:: forward(geometry_position, geometry_supernode_idx, geometry_batch_idx, surface_anchor_position, volume_anchor_position, surface_query_position = None, volume_query_position = None)


.. py:function:: main()