ksuit.data.pipeline.multistage
==============================

.. py:module:: ksuit.data.pipeline.multistage


Attributes
----------

.. autoapisummary::

   ksuit.data.pipeline.multistage.SampleProcessorType
   ksuit.data.pipeline.multistage.BatchProcessorType


Classes
-------

.. autoapisummary::

   ksuit.data.pipeline.multistage.MultiStagePipeline


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

.. py:data:: SampleProcessorType

.. py:data:: BatchProcessorType

.. py:class:: MultiStagePipeline(collators = None, sample_processors = None, batch_processors = None)

   Bases: :py:obj:`ksuit.data.pipeline.collator.Collator`


   A Collator that processes the list of samples into a batch in multiple stages:
       - sample_processors: Processing the data before collation on a per-sample level.
       - collators: Conversion from a list of samples into a batch (dict of usually tensors).
       - batch_processors: Processing after collation on a batch-level.
   Most of the work is usually done by the sample_processors. One or two collators, and batch processors are often not needed. However this depends on the use case.
   .. rubric:: Example

   >>> sample_processors = [MySampleProcessor1(), MySampleProcessor2()]
   >>> collators = [MyCollator1(), MyCollator2()]
   >>> batch_processors = [MyBatchProcessor1(), MyBatchProcessor2()]
   >>> multistage_pipeline = MultiStagePipeline(
   >>>     sample_processors=sample_processors,
   >>>     collators=collators,
   >>>     batch_processors=batch_processors
   >>> )
   >>> batch = multistage_pipeline(samples)

   :param sample_processors: A list of callables that will be applied sequentially to pre-process on a per-sample level (e.g., subsample a pointcloud).
   :param collators: A list of callables that will be applied sequentially to convert the list of individual samples into a batched format. If None, the default PyTorch collator will be used.
   :param batch_processors: A list of callables that will be applied sequentially to process on a per-batch level.


   .. py:attribute:: sample_processors
      :value: []



   .. py:attribute:: batch_processors
      :value: []



   .. py:method:: get_sample_processor(predicate)

      Retrieves a sample processor by a predicate function.
      Examples:
      - Search by type (assumes the sample processor type only occurs once in the list of sample processors)
        `pipeline.get_sample_processor(lambda p: isinstance(p, MySampleProcessorType))`
      - Search by type and member
        `pipeline.get_sample_processor(lambda p: isinstance(p, PointSamplingSampleProcessor) and "input_pos" in p.items)`

      :param predicate: A function that is called for each processor and selects if this is the right one.

      :returns: The matching sample processor.
      :rtype: Any

      :raises ValueError: If no matching sample processor are found, multiple matching sample processors are found or if there
          are no sample processors.