:py:mod:`neural_compressor.utils.create_obj_from_config`
========================================================

.. py:module:: neural_compressor.utils.create_obj_from_config

.. autoapi-nested-parse::

   Utility methods to create corresponding objects from configuration.



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


Functions
~~~~~~~~~

.. autoapisummary::

   neural_compressor.utils.create_obj_from_config.get_func_from_config
   neural_compressor.utils.create_obj_from_config.get_preprocess
   neural_compressor.utils.create_obj_from_config.get_metrics
   neural_compressor.utils.create_obj_from_config.get_postprocess
   neural_compressor.utils.create_obj_from_config.get_algorithm
   neural_compressor.utils.create_obj_from_config.create_dataset
   neural_compressor.utils.create_obj_from_config.create_dataloader
   neural_compressor.utils.create_obj_from_config.create_eval_func
   neural_compressor.utils.create_obj_from_config.create_train_func



.. py:function:: get_func_from_config(func_dict, cfg, compose=True)

   Get the function or the composed function from configuration.


.. py:function:: get_preprocess(preprocesses, cfg, compose=True)

   Get the preprocess function from configuration.


.. py:function:: get_metrics(metrics, cfg, compose=True)

   Get the metrics function from configuration.


.. py:function:: get_postprocess(postprocesses, cfg, compose=True)

   Get the postprocess function from configuration.


.. py:function:: get_algorithm(algorithms, cfg, compose=False)

   Get the algorithms from configuration.

   :param algorithms: the algorithm management.
   :param cfg: a dict contain the algo name and use it or not.
   :param compose: compose all algo or not. Defaults to False.

   :returns: All open algos.


.. py:function:: create_dataset(framework, data_source, cfg_preprocess, cfg_filter)

   Create the dataset from the data source.


.. py:function:: create_dataloader(framework, dataloader_cfg)

   Create the dataloader according to the framework.


.. py:function:: create_eval_func(framework, dataloader, adaptor, metric, postprocess_cfg=None, iteration=-1, tensorboard=False, fp32_baseline=False)

   The interface to create evaluate function from config.

   :param framework: The string of framework.
   :type framework: str
   :param dataloader: The object of common.DataLoader.
   :type dataloader: common.DataLoader
   :param adaptor: The object of adaptor.
   :type adaptor: obj
   :param metric: The evaluation metric.
   :param postprocess_cfg: The postprocess configuration.
   :param iteration: The number of iterations to evaluate.
   :param tensorboard: Whether to use tensorboard.
   :param fp32_baseline: The fp32 baseline score.

   :returns: The constructed evaluation function


.. py:function:: create_train_func(framework, dataloader, adaptor, train_cfg, hooks=None, callbacks=None)

   The interface to create train function from config.

   :param framework: The string of framework.
   :type framework: str
   :param dataloader: The object of common.DataLoader.
   :type dataloader: common.DataLoader
   :param adaptor: The object of adaptor.
   :type adaptor: obj
   :param train_cfg: The dict of training related config.
   :type train_cfg: dict
   :param hooks: The dict of training hooks, supported keys are:
                 on_epoch_begin, on_epoch_end, on_step_begin, on_step_end.
                 Their values are functions to be executed in adaptor layer.
   :type hooks: dict

   :returns: The constructed train function