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https://github.com/huggingface/lerobot.git
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2c4e888c7f
* Add feature contract to pipelinestep and pipeline * Add tests * Add processor tests * PR feedback * encorperate pr feedback * type in doc * oops
64 lines
2.3 KiB
Python
64 lines
2.3 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 dataclasses import dataclass, field
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from typing import Any
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import torch
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from lerobot.configs.types import PolicyFeature
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from lerobot.processor.pipeline import EnvTransition, ProcessorStepRegistry, TransitionKey
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@dataclass
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@ProcessorStepRegistry.register(name="rename_processor")
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class RenameProcessor:
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"""Rename processor that renames keys in the observation."""
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rename_map: dict[str, str] = field(default_factory=dict)
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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observation = transition.get(TransitionKey.OBSERVATION)
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if observation is None:
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return transition
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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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# Create a new transition with the renamed observation
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new_transition = transition.copy()
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new_transition[TransitionKey.OBSERVATION] = processed_obs
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return new_transition
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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 state_dict(self) -> dict[str, torch.Tensor]:
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return {}
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def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
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pass
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def feature_contract(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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