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https://github.com/huggingface/lerobot.git
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af9ddcf9a2
* 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>
88 lines
3.3 KiB
Python
88 lines
3.3 KiB
Python
#!/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from copy import deepcopy
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from dataclasses import dataclass, field
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from typing import Any
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from lerobot.configs.types import PolicyFeature
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from .pipeline import ObservationProcessorStep, ProcessorStepRegistry
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@dataclass
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@ProcessorStepRegistry.register(name="rename_processor")
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class RenameProcessorStep(ObservationProcessorStep):
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"""
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A processor step that renames keys in an observation dictionary.
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This step is useful for creating a standardized data interface by mapping keys
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from an environment's format to the format expected by a LeRobot policy or
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other downstream components.
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Attributes:
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rename_map: A dictionary mapping from old key names to new key names.
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Keys present in an observation that are not in this map will
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be kept with their original names.
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"""
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rename_map: dict[str, str] = field(default_factory=dict)
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def observation(self, observation):
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processed_obs = {}
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for key, value in observation.items():
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if key in self.rename_map:
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processed_obs[self.rename_map[key]] = value
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else:
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processed_obs[key] = value
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return processed_obs
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def get_config(self) -> dict[str, Any]:
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return {"rename_map": self.rename_map}
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def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
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"""Transforms:
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- Each key in the observation that appears in `rename_map` is renamed to its value.
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- Keys not in `rename_map` remain unchanged.
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"""
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return {self.rename_map.get(k, k): v for k, v in features.items()}
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def rename_stats(stats: dict[str, dict[str, Any]], rename_map: dict[str, str]) -> dict[str, dict[str, Any]]:
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"""
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Renames the top-level keys in a statistics dictionary using a provided mapping.
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This is a helper function typically used to keep normalization statistics
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consistent with renamed observation or action features. It performs a defensive
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deep copy to avoid modifying the original `stats` dictionary.
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Args:
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stats: A nested dictionary of statistics, where top-level keys are
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feature names (e.g., `{"observation.state": {"mean": 0.5}}`).
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rename_map: A dictionary mapping old feature names to new feature names.
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Returns:
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A new statistics dictionary with its top-level keys renamed. Returns an
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empty dictionary if the input `stats` is empty.
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"""
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if not stats:
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return {}
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renamed: dict[str, dict[str, Any]] = {}
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for old_key, sub_stats in stats.items():
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new_key = rename_map.get(old_key, old_key)
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renamed[new_key] = deepcopy(sub_stats) if sub_stats is not None else {}
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return renamed
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