helios.model.utils
==================

.. py:module:: helios.model.utils


Attributes
----------

.. autoapisummary::

   helios.model.utils.MODEL_REGISTRY


Functions
---------

.. autoapisummary::

   helios.model.utils.create_model
   helios.model.utils.find_pretrained_file


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

.. py:data:: MODEL_REGISTRY

   Global instance of the registry for models.

   .. rubric:: Example

   .. code-block:: python

       import helios.model as hlm

       # This automatically registers your model.
       @hlm.MODEL_REGISTRY.register
       class MyModel:
           ...

       # Alternatively you can manually register a model like this:
       hlm.MODEL_REGISTRY.register(MyModel)

.. py:function:: create_model(type_name: str, *args: Any, **kwargs: Any) -> helios.model.model.Model

   Create the model for the given type.

   :param type_name: the type of the model to create.
   :param args: positional arguments to pass into the model.
   :param kwargs: keyword arguments to pass into the model.

   :returns: The model.


.. py:function:: find_pretrained_file(root: pathlib.Path, name: str) -> pathlib.Path

   Find the pre-trained file in the given root.

   The assumption is the following:
   Given a root ``/models/cifar`` and a name ``resnet-50``, then the name of the
   pre-trained file will contain ``cifar_resnet-50_`` as a prefix. If no file is
   found, an exception is raised.

   :param root: the root where the file is stored.
   :param net_name: the save name of the file.

   :returns: The path to the file.

   :raises RuntimeError: if no pre-trained network was found.