dacapo.store.weights_store
==========================

.. py:module:: dacapo.store.weights_store


Classes
-------

.. autoapisummary::

   dacapo.store.weights_store.Weights
   dacapo.store.weights_store.WeightsStore


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

.. py:class:: Weights(model_state_dict, optimizer_state_dict)

   A class representing the weights of a model and optimizer.

   .. attribute:: optimizer

      The optimizer's state dictionary.

      :type: OrderedDict[str, torch.Tensor]

   .. attribute:: model

      The model's state dictionary.

      :type: OrderedDict[str, torch.Tensor]

   .. method:: __init__(model_state_dict, optimizer_state_dict)

      Initializes the Weights object with the given model and optimizer state dictionaries.
      


   .. py:attribute:: optimizer
      :type:  collections.OrderedDict[str, torch.Tensor]


   .. py:attribute:: model
      :type:  collections.OrderedDict[str, torch.Tensor]


.. py:class:: WeightsStore



   Base class for network weight stores.

   .. method:: load_weights(run, iteration)

      Load the weights of the given iteration into the given run.

   .. method:: load_best(run, dataset, criterion)

      Load the best weights for the given run, dataset, and criterion into the given run.

   .. method:: latest_iteration(run)

      Return the latest iteration for which weights are available for the given run.

   .. method:: store_weights(run, iteration)

      Store the network weights of the given run.

   .. method:: retrieve_weights(run, iteration)

      Retrieve the network weights of the given run.

   .. method:: remove(run, iteration)

      Delete the weights associated with a specific run/iteration.

   .. method:: retrieve_best(run, dataset, criterion)

      Retrieve the best weights for the given run, dataset, and criterion.
      


   .. py:method:: load_weights(run: dacapo.experiments.run.Run, iteration: int) -> None

      Load this iterations weights into the given run.
      :param run: The run to load the weights into.
      :type run: Run
      :param iteration: The iteration to load the weights from.
      :type iteration: int

      :raises ValueError: If the iteration is not available.

      .. rubric:: Examples

      >>> store = WeightsStore()
      >>> run = Run()
      >>> iteration = 0
      >>> store.load_weights(run, iteration)



   .. py:method:: load_best(run: dacapo.experiments.run.Run, dataset: str, criterion: str) -> None

      Load the best weights for this Run,dataset,criterion into Run.model

      :param run: The run to load the weights into.
      :type run: Run
      :param dataset: The dataset to load the weights from.
      :type dataset: str
      :param criterion: The criterion to load the weights from.
      :type criterion: str

      :raises ValueError: If the best iteration is not available.

      .. rubric:: Examples

      >>> store = WeightsStore()
      >>> run = Run()
      >>> dataset = 'mnist'
      >>> criterion = 'accuracy'
      >>> store.load_best(run, dataset, criterion)



   .. py:method:: latest_iteration(run: str) -> Optional[int]
      :abstractmethod:


      Return the latest iteration for which weights are available for the
      given run.

      :param run: The name of the run.
      :type run: str

      :returns: The latest iteration for which weights are available.
      :rtype: int

      :raises ValueError: If no weights are available for the given run.

      .. rubric:: Examples

      >>> store = WeightsStore()
      >>> run = 'run_0'
      >>> store.latest_iteration(run)



   .. py:method:: store_weights(run: dacapo.experiments.run.Run, iteration: int) -> None
      :abstractmethod:


      Store the network weights of the given run.

      :param run: The run to store the weights of.
      :type run: Run
      :param iteration: The iteration to store the weights for.
      :type iteration: int

      :raises ValueError: If the iteration is already stored.

      .. rubric:: Examples

      >>> store = WeightsStore()
      >>> run = Run()
      >>> iteration = 0
      >>> store.store_weights(run, iteration)



   .. py:method:: retrieve_weights(run: str, iteration: int) -> Weights
      :abstractmethod:


      Retrieve the network weights of the given run.

      :param run: The name of the run.
      :type run: str
      :param iteration: The iteration to retrieve the weights for.
      :type iteration: int

      :returns: The weights of the given run and iteration.
      :rtype: Weights

      :raises ValueError: If the weights are not available.

      .. rubric:: Examples

      >>> store = WeightsStore()
      >>> run = 'run_0'
      >>> iteration = 0
      >>> store.retrieve_weights(run, iteration)



   .. py:method:: remove(run: str, iteration: int) -> None
      :abstractmethod:


      Delete the weights associated with a specific run/iteration

      :param run: The name of the run.
      :type run: str
      :param iteration: The iteration to delete the weights for.
      :type iteration: int

      :raises ValueError: If the weights are not available.

      .. rubric:: Examples

      >>> store = WeightsStore()
      >>> run = 'run_0'
      >>> iteration = 0
      >>> store.remove(run, iteration)



   .. py:method:: retrieve_best(run: str, dataset: str, criterion: str) -> int
      :abstractmethod:


      Retrieve the best weights for this run/dataset/criterion

      :param run: The name of the run.
      :type run: str
      :param dataset: The dataset to retrieve the best weights for.
      :type dataset: str
      :param criterion: The criterion to retrieve the best weights for.
      :type criterion: str

      :returns: The iteration of the best weights.
      :rtype: int

      :raises ValueError: If the best weights are not available.

      .. rubric:: Examples

      >>> store = WeightsStore()
      >>> run = 'run_0'
      >>> dataset = 'mnist'
      >>> criterion = 'accuracy'
      >>> store.retrieve_best(run, dataset, criterion)