ksuit.callbacks.base.periodic_iterator_callback
===============================================

.. py:module:: ksuit.callbacks.base.periodic_iterator_callback


Classes
-------

.. autoapisummary::

   ksuit.callbacks.base.periodic_iterator_callback.PeriodicIteratorCallback


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

.. py:class:: PeriodicIteratorCallback(callback_config, trainer, model, data_container, tracker, log_writer, checkpoint_writer, metric_property_provider, name = None)

   Bases: :py:obj:`ksuit.callbacks.base.periodic_callback.PeriodicCallback`


   A base class for callbacks that perform periodic iterations over a dataset.

   Periodic callbacks are typically used to calculate a metric on, e.g., a test dataset. Therefore,
   this class provides according functionality to integrate into the dataloading pipeline via the
   `_register_sampler_configs` and `_iterate_over_dataset` methods.

   A basic example for a callback that calculates the test accuracy of a classification model:

   ```
   class AccuracyCallback(PeriodicIteratorCallback):
       def _register_sampler_config(self, trainer) -> None:
           return self._sampler_config_from_key(key=self.dataset_key)

       def _forward(self, batch, *, trainer_model):
           y_hat = trainer_model(batch["x"].to(trainer_model.device))
           return y_hat, batch["class"].clone()

       def _process_results(self, results, *, interval_type, update_counter, **_):
           y_hat, y = results
           accuracy = (y_hat.argmax(dim=1) == y).float().mean()
           ...
   ```

   Initializes the `PeriodicCallback`.

   :param callback_config: Configuration of the `PeriodicCallback`. Implements the `CallBackBaseConfig` schema.
   :param trainer: Trainer of the current run, subclass of `SgdTrainer`.
   :param model: Model of the current run.
   :param data_container: DataContainer instance that provides access to all datasets.
   :param tracker: Tracker instance to log metrics to stdout/disk/online platform.
   :param log_writer: LogWriter instance to log metrics.
   :param checkpoint_writer: CheckpointWriter instance to save checkpoints.
   :param metric_property_provider: MetricPropertyProvider instance to access properties of metrics.
   :param name: Name of the callback.


   .. py:attribute:: total_data_time
      :value: 0.0



   .. py:method:: register_sampler_config()

      Registers the datasets that are used for this callback into the dataloading pipeline.

      :param trainer: Trainer of the current run.

      :returns: The registered sampler_config