:py:mod:`neural_compressor.experimental.pruning_v2`
===================================================

.. py:module:: neural_compressor.experimental.pruning_v2

.. autoapi-nested-parse::

   pruning module.



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

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.experimental.pruning_v2.Pruning
   neural_compressor.experimental.pruning_v2.TfPruningCallback




.. py:class:: Pruning(conf_fname_or_obj=None)

   Bases: :py:obj:`neural_compressor.experimental.component.Component`

   This is base class of pruning object.

      Since DL use cases vary in the accuracy metrics (Top-1, MAP, ROC etc.), loss criteria
      (<1% or <0.1% etc.) and pruning objectives (performance, memory footprint etc.).
      Pruning class provides a flexible configuration interface via YAML for users to specify
      these parameters.

   :param conf_fname_or_obj: The path to the YAML configuration file or
                             PruningConf class containing accuracy goal, pruning objective and related
                             dataloaders etc.
   :type conf_fname_or_obj: string or obj

   .. attribute:: conf

      A config dict object. Contains pruning setting parameters.

   .. attribute:: pruners

      A list of Pruner object.

   .. py:property:: pruning_func

      Not support get pruning_func.

   .. py:property:: evaluation_distributed

      Getter to know whether need distributed evaluation dataloader.

   .. py:property:: train_distributed

      Getter to know whether need distributed training dataloader.

   .. py:method:: update_config(*args, **kwargs)

      Add user-defined arguments to the original configurations.

      The original config of pruning is read from a file.
      However, users can still modify configurations by passing key-value arguments in this function.
      Please note that the key-value arguments' keys are analysable in current configuration.


   .. py:method:: get_sparsity_ratio()

      Calculate sparsity ratio of a module/layer.

      :returns: Three floats.
                elementwise_over_matmul_gemm_conv refers to zero elements' ratio in pruning layers.
                elementwise_over_all refers to zero elements' ratio in all layers in the model.
                blockwise_over_matmul_gemm_conv refers to all-zero blocks' ratio in pruning layers.


   .. py:method:: prepare()

      Functions prepare for generate_hooks, generate_pruners.


   .. py:method:: pre_process()

      Functions called before pruning begins, usually set up pruners.


   .. py:method:: execute()

      Functions that execute the pruning process.

      Follow the working flow: evaluate the dense model -> train/prune the model, evaluate the sparse model.


   .. py:method:: generate_hooks()

      Register hooks for pruning.



.. py:class:: TfPruningCallback(nc_model, input_model, hooks)

   Bases: :py:obj:`object`

   Class that contains callback functions.

   :param nc_model: A neural compression model object.
   :param hooks: A dict. Contains pure-defined hooks.

   .. py:method:: on_train_begin(logs=None, dataloader=None)

      Call the same-name function from hooks.


   .. py:method:: on_train_end(logs=None)

      Call the same-name function from hooks.


   .. py:method:: pre_epoch_begin(logs=None, dataloader=None)

      Call the same-name function from hooks.


   .. py:method:: post_epoch_end(logs=None)

      Call the same-name function from hooks.


   .. py:method:: on_epoch_begin(epoch, logs=None)

      Call the same-name function from hooks.


   .. py:method:: on_epoch_end(logs=None)

      Call the same-name function from hooks.


   .. py:method:: on_step_begin(batch, logs=None)

      Call the same-name function from hooks.


   .. py:method:: on_batch_begin(batch, logs=None)

      Call the same-name function from hooks.


   .. py:method:: on_after_compute_loss(input, s_outputs, s_loss, t_outputs=None)

      Call the same-name function from hooks.


   .. py:method:: on_step_end(logs=None)

      Call the same-name function from hooks.


   .. py:method:: on_batch_end(logs=None)

      Call the same-name function from hooks.