abupt
=====

.. py:module:: abupt


Classes
-------

.. autoapisummary::

   abupt.AbuptCollator


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

.. py:class:: AbuptCollator(num_geometry_points, num_surface_anchor_points, num_volume_anchor_points, num_geometry_supernodes, dataset_statistics, use_query_positions = False, seed = None)

   Bases: :py:obj:`ksuit.data.pipeline.MultiStagePipeline`


   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:method:: preprocess_inputs_only(samples)