:py:mod:`neural_compressor.objective`
=====================================

.. py:module:: neural_compressor.objective

.. autoapi-nested-parse::

   The objectives supported by neural_compressor, which is driven by accuracy.

   To support new objective, developers just need implement a new subclass in this file.



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

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.objective.Objective
   neural_compressor.objective.Accuracy
   neural_compressor.objective.Performance
   neural_compressor.objective.Footprint
   neural_compressor.objective.ModelSize
   neural_compressor.objective.MultiObjective



Functions
~~~~~~~~~

.. autoapisummary::

   neural_compressor.objective.objective_registry
   neural_compressor.objective.objective_custom_registry



.. py:function:: objective_registry(cls)

   The class decorator used to register all Objective subclasses.

   :param cls: The class of register.
   :type cls: object

   :returns: The class of register.
   :rtype: cls (object)


.. py:function:: objective_custom_registry(name, obj_cls)

   Register a customized objective.

   Example::

       from eural_compressor.objective import objective_custom_registry

       objective_cls = type(user_objective)
       name = user_objective.__class__.__name__
       objective_cfg = name if deep_get(self.conf.usr_cfg, "tuning.objective") else [name]
       deep_set(self.conf.usr_cfg, user_obj_cfg, objective_cfg)
       self.conf.usr_cfg = DotDict(self.conf.usr_cfg)
       objective_custom_registry(name, objective_cls)


.. py:class:: Objective




   The base class for precise benchmark supported by neural_compressor.

   With built-in objectives, users can compress models with different objectives easily.
   In special cases, users can also register their own objective classes.


.. py:class:: Accuracy




   Configuration Accuracy class.

   Evaluate the accuracy.


.. py:class:: Performance




   Configuration Performance class.

   Evaluate the inference time.


.. py:class:: Footprint




   Configuration Footprint class.

   Evaluate the peak size of memory blocks during inference.


.. py:class:: ModelSize




   Configuration ModelSize class.

   Evaluate the model size.


.. py:class:: MultiObjective(objectives, accuracy_criterion, metric_criterion=[True], metric_weight=None, obj_criterion=None, obj_weight=None, is_measure=False)


   The base class for multiple benchmarks supported by neural_compressor.

   Example::

       from neural_compressor.objective import MultiObjective
       obj = MultiObjective(
           objectives=['accuracy', 'modelsize', 'performance'],
           accuracy_criterion={'relative': 0.1},
           obj_criterion=[True, False, False],
           obj_weight=[0.7, 0.2, 0.1])