mexca.video.helper_classes
==========================

.. py:module:: mexca.video.helper_classes

.. autoapi-nested-parse::

   Helper classes for multi-dimensional edge feature-based AU relation graph (MEFARG) learning.

   Implementation of building blocks in the MEFARG model from the paper:

       Luo, C., Song, S., Xie, W., Shen, L., Gunes, H. (2022). Learning multi-dimentionsal edge
       feature-based AU relation graph for facial action unit recognition. *arXiv*.
       `<https://arxiv.org/pdf/2205.01782.pdf>`_

   Code adapted from the `OpenGraphAU <https://github.com/lingjivoo/OpenGraphAU/tree/main>`_ code base
   (licensed under Apache 2.0).



Classes
-------

.. autoapisummary::

   mexca.video.helper_classes.LinearBlock
   mexca.video.helper_classes.AUPredictor


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

.. py:class:: LinearBlock(in_features: int, out_features: Optional[int] = None, drop: float = 0.0)



   Apply transformations of multiple layers including a linear layer.

   :param in_features: Size of each input sample.
   :type in_features: int
   :param out_features: Size of each output sample. If `None`, uses `in_features`.
   :type out_features: int, optional, default=None
   :param drop: Probability of dropping (zeroing out) input features.
   :type drop: float, default=0.0

   .. rubric:: Notes

   Applies four transformations:

   - Linear
   - 1D batch normalization
   - ReLU
   - Drop out regularization

   Linear layer weights are initialized with :math:`N(0, \sqrt{\frac{2}{out\_features}})`.
   Batch norm weights are initialized as 1 and biases as 0.


.. py:class:: AUPredictor(in_features: int, n_main_nodes: int = 27, n_sub_nodes: int = 14)



   Meta class for predicting action unit (AU) activations