dacapo.experiments.starts
=========================

.. py:module:: dacapo.experiments.starts


Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/dacapo/experiments/starts/cosem_start/index
   /autoapi/dacapo/experiments/starts/cosem_start_config/index
   /autoapi/dacapo/experiments/starts/start/index
   /autoapi/dacapo/experiments/starts/start_config/index


Classes
-------

.. autoapisummary::

   dacapo.experiments.starts.Start
   dacapo.experiments.starts.StartConfig
   dacapo.experiments.starts.CosemStart
   dacapo.experiments.starts.CosemStartConfig


Package Contents
----------------

.. py:class:: Start(start_config)



   This class interfaces with the dacapo store to retrieve and load the
   weights of the starter model used for finetuning.

   .. attribute:: run

      str
      The specified run to retrieve weights for the model.

   .. attribute:: criterion

      str
      The policy that was used to decide when to store the weights.

   .. attribute:: channels

      int
      The number of channels in the input data.

   .. method:: __init__(start_config)

      
      Initializes the Start class with specified config to run the
      initialization of weights for a model associated with a specific
      criterion.

   .. method:: initialize_weights(model, new_head=None)

      
      Retrieves the weights from the dacapo store and load them into
      the model.

   .. rubric:: Notes

   This class is used to retrieve and load the weights of the starter
   model used for finetuning from the dacapo store.


   .. py:attribute:: channels
      :value: None



   .. py:method:: initialize_weights(model, new_head=None)

      Retrieves the weights from the dacapo store and load them into
      the model.

      :param model: obj
                    The model to which the weights are to be loaded.
      :param new_head: list
                       The labels of the new head.

      :returns:

                obj
                    The model with the weights loaded from the dacapo store.
      :rtype: model

      :raises RuntimeError: If weights of a non-existing or mismatched layer are being
          loaded, a RuntimeError exception is thrown which is logged
          and handled by loading only the common layers from weights.

      .. rubric:: Examples

      >>> model = start.initialize_weights(model, new_head)

      .. rubric:: Notes

      This function is called by the Start class to retrieve the weights
      from the dacapo store and load them into the model.



.. py:class:: StartConfig

   A class to represent the configuration for running tasks. This class
   interfaces with the dacapo store to retrieve and load the weights of the
   starter model used for finetuning.

   .. attribute:: run

      str
      The run to be used as a starting point for tasks.

   .. attribute:: criterion

      str
      The criterion to be used for choosing weights from run.

   .. method:: __init__(start_config)

      
      Initializes the StartConfig class with specified config to run the
      initialization of weights for a model associated with a specific
      criterion.

   .. rubric:: Notes

   This class is used to represent the configuration for running tasks.


   .. py:attribute:: start_type


   .. py:attribute:: run
      :type:  str


   .. py:attribute:: criterion
      :type:  str


.. py:class:: CosemStart(start_config)



   A class to represent the starting point for tasks. This class inherits
   from the Start class and is used to load the weights of the starter model
   used for finetuning. The weights are loaded from the dacapo store for the
   specified run and criterion.

   .. attribute:: run

      str
      The run to be used as a starting point for tasks.

   .. attribute:: criterion

      str
      The criterion to be used for choosing weights from run.

   .. attribute:: name

      str
      The name of the run and criterion.

   .. attribute:: channels

      list
      The classes_channels of the model.

   .. method:: __init__(start_config)

      
      Initializes the CosemStart class with specified config to run the
      initialization of weights for a model associated with a specific
      criterion.

   .. method:: check()

      
      Checks if the checkpoint for the specified run and criterion exists.

   .. method:: initialize_weights(model, new_head=None)

      
      Retrieves the weights from the dacapo store and load them into
      the model.

   .. rubric:: Notes

   This class is used to represent the starting point for tasks. The weights
   of the starter model used for finetuning are loaded from the dacapo store.


   .. py:attribute:: run


   .. py:attribute:: criterion


   .. py:attribute:: name


   .. py:attribute:: channels


   .. py:method:: check()

      Checks if the checkpoint for the specified run and criterion exists.

      :raises Exception: If the checkpoint does not exist, an Exception is thrown which
          is logged and handled by training the model without head matching.

      .. rubric:: Examples

      >>> check()

      .. rubric:: Notes

      This function is called by the CosemStart class to check if the
      checkpoint for the specified run and criterion exists.



   .. py:method:: initialize_weights(model, new_head=None)

      Retrieves the weights from the dacapo store and load them into
      the model.

      :param model: obj
                    The model to which the weights are to be loaded.
      :param new_head: list
                       The labels of the new head.

      :returns:

                obj
                    The model with the weights loaded from the dacapo store.
      :rtype: model

      :raises RuntimeError: If weights of a non-existing or mismatched layer are being
          loaded, a RuntimeError exception is thrown which is logged
          and handled by loading only the common layers from weights.

      .. rubric:: Examples

      >>> model = initialize_weights(model, new_head)

      .. rubric:: Notes

      This function is called by the CosemStart class to retrieve the weights
      from the dacapo store and load them into the model.



.. py:class:: CosemStartConfig



   Starter for COSEM pretained models. This is a subclass of `StartConfig` and
   should be used to initialize the model with pretrained weights from a previous
   run.

   The weights are loaded from the dacapo store for the specified run. The
   configuration is used to initialize the weights for the model associated with
   a specific criterion.

   .. attribute:: run

      str
      The run to be used as a starting point for tasks.

   .. attribute:: criterion

      str
      The criterion to be used for choosing weights from run.

   .. method:: __init__(start_config)

      
      Initializes the CosemStartConfig class with specified config to run the
      initialization of weights for a model associated with a specific
      criterion.

   .. rubric:: Examples

   >>> start_config = CosemStartConfig(run="run_1", criterion="best")

   .. rubric:: Notes

   This class is used to represent the configuration for running tasks.


   .. py:attribute:: start_type