:py:mod:`neural_compressor.compression.callbacks`
=================================================

.. py:module:: neural_compressor.compression.callbacks

.. autoapi-nested-parse::

   This is a module for Component class.

   The Component class will be inherited by the class 'QuantizationAwareTrainingCallbacks',
   'PruningCallbacks' and 'DistillationCallbacks'.



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

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.compression.callbacks.BaseCallbacks
   neural_compressor.compression.callbacks.QuantizationAwareTrainingCallbacks
   neural_compressor.compression.callbacks.PruningCallbacks
   neural_compressor.compression.callbacks.DistillationCallbacks




.. py:class:: BaseCallbacks(conf=None, model=None)




   This is base class of Neural Compressor Callbacks.

   This class will be inherited by the class 'QuantizationAwareTrainingCallbacks',
   'PruningCallbacks' and 'DistillationCallbacks'.
   This design is mainly for pruning/distillation/quantization-aware training.
   In this class will apply all hooks for 'Quantization', 'Pruning' and 'Distillation'.


.. py:class:: QuantizationAwareTrainingCallbacks(conf=None, model=None, adaptor=None)




   This is the class for callbacks of quantization aware training.

   This design is mainly for Quantization-Aware Training.
   In this class will apply all hooks for Quantization-Aware Training.


.. py:class:: PruningCallbacks(conf=None, model=None)




   This is the class for callbacks of pruning object.

   In this class will apply all hooks for Pruning.


.. py:class:: DistillationCallbacks(conf=None, model=None)




   Distillation class derived from Component class.

   Distillation class abstracted the pipeline of knowledge distillation,
   transfer the knowledge of the teacher model to the student model.

   :param conf: Distillation_Conf containing teacher model, distillation criterion etc.
   :param model: Student model. It should be neural compressor model.

   .. attribute:: _epoch_ran

      A integer indicating how much epochs ran.

   .. attribute:: eval_frequency

      The frequency for doing evaluation of the student model
      in terms of epoch.

   .. attribute:: best_score

      The best metric of the student model in the training.

   .. attribute:: best_model

      The best student model found in the training.