:py:mod:`neural_compressor.experimental.common`
===============================================

.. py:module:: neural_compressor.experimental.common

.. autoapi-nested-parse::

   Intel® Neural Compressor: An open-source Python library supporting common model.



Submodules
----------
.. toctree::
   :titlesonly:
   :maxdepth: 1

   criterion/index.rst
   dataloader/index.rst
   metric/index.rst
   model/index.rst
   optimizer/index.rst
   postprocess/index.rst
   torch_utils/index.rst


Package Contents
----------------

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.experimental.common.Model
   neural_compressor.experimental.common.DataLoader
   neural_compressor.experimental.common.Postprocess
   neural_compressor.experimental.common.Metric



Functions
~~~~~~~~~

.. autoapisummary::

   neural_compressor.experimental.common._generate_common_dataloader



.. py:class:: Model

   Bases: :py:obj:`object`

   A wrapper of the information needed to construct a Model.


.. py:class:: DataLoader(dataset, batch_size=1, collate_fn=None, last_batch='rollover', sampler=None, batch_sampler=None, num_workers=0, pin_memory=False, shuffle=False, distributed=False)

   Bases: :py:obj:`object`

   A wrapper of the information needed to construct a dataloader.

   This class can't yield batched data and only in this Quantization/Benchmark
   object's setter method a 'real' calib_dataloader will be created, the reason
   is we have to know the framework info and only after the Quantization/Benchmark
   object created then framework infomation can be known. Future we will support
   creating iterable dataloader from neural_compressor.experimental.common.DataLoader



.. py:class:: Postprocess(postprocess_cls, name='user_postprocess', **kwargs)

   Bases: :py:obj:`object`

   Just collect the infos to construct a Postprocess.


.. py:class:: Metric(metric_cls, name='user_metric', **kwargs)

   Bases: :py:obj:`object`

   A wrapper of the information needed to construct a Metric.

   The metric class should take the outputs of the model as the metric's inputs,
   neural_compressor built-in metric always take (predictions, labels) as inputs, it's
   recommended to design metric_cls to take (predictions, labels) as inputs.