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chore(docs): update doctrines pipeline files (#1872)

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* docs(processor): update docstrings batch_processor

* docs(processor): update docstrings device_processor

* docs(processor): update docstrings tokenizer_processor

* update docstrings processor_act

* update docstrings for pipeline_features

* update docstrings for utils

* update docstring for processor_diffusion

* update docstrings factory

* add docstrings to pi0 processor

* add docstring to pi0fast processor

* add docstring classifier processor

* add docstring to sac processor

* add docstring smolvla processor

* add docstring to tdmpc processor

* add docstring to vqbet processor

* add docstrings to converters

* add docstrings for delta_action_processor

* add docstring to gym action processor

* update hil processor

* add docstring to joint obs processor

* add docstring to migrate_normalize_processor

* update docstrings normalize processor

* update docstring normalize processor

* update docstrings observation processor

* update docstrings rename_processor

* add docstrings robot_kinematic_processor

* cleanup rl comments

* add docstring to train.py

* add docstring to teleoperate.py

* add docstrings to phone_processor.py

* add docstrings to teleop_phone.py

* add docstrings to control_utils.py

* add docstrings to visualization_utils.py

---------

Co-authored-by: Pepijn <pepijn@huggingface.co>
This commit is contained in:
Steven Palma
2025-09-08 18:44:15 +02:00
committed by GitHub
parent d32006440c
commit af9ddcf9a2
33 changed files with 2325 additions and 519 deletions
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20
src/lerobot/policies/sac/reward_model/processor_classifier.py
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@@ -31,6 +31,26 @@ def make_classifier_processor(
preprocessor_kwargs: ProcessorKwargs | None = None,
postprocessor_kwargs: ProcessorKwargs | None = None,
) -> tuple[PolicyProcessorPipeline, PolicyProcessorPipeline]:
"""
Constructs pre-processor and post-processor pipelines for the reward classifier.
The pre-processing pipeline prepares input data for the classifier by:
1. Normalizing both input and output features based on dataset statistics.
2. Moving the data to the specified device.
The post-processing pipeline handles the classifier's output by:
1. Moving the data to the CPU.
2. Applying an identity step, as no unnormalization is needed for the output logits.
Args:
config: The configuration object for the RewardClassifier.
dataset_stats: A dictionary of statistics for normalization.
preprocessor_kwargs: Additional arguments for the pre-processor pipeline.
postprocessor_kwargs: Additional arguments for the post-processor pipeline.
Returns:
A tuple containing the configured pre-processor and post-processor pipelines.
"""
if preprocessor_kwargs is None:
preprocessor_kwargs = {}
if postprocessor_kwargs is None: