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https://github.com/huggingface/lerobot.git
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Steven Palma
c5371d0691
* refactor(processors): share policy pipeline builders * Apply suggestions from code review Co-authored-by: Martino Russi <77496684+nepyope@users.noreply.github.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> * fix(processor): solve style after commit suggestions --------- Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Martino Russi <77496684+nepyope@users.noreply.github.com>
60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 Columbia Artificial Intelligence, Robotics Lab,
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# and 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 typing import Any
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import torch
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from lerobot.processor import (
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PolicyAction,
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PolicyProcessorPipeline,
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make_default_pre_post_processors,
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)
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from .configuration_diffusion import DiffusionConfig
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def make_diffusion_pre_post_processors(
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config: DiffusionConfig,
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dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
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) -> tuple[
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PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
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PolicyProcessorPipeline[PolicyAction, PolicyAction],
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]:
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"""
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Constructs pre-processor and post-processor pipelines for a diffusion policy.
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The pre-processing pipeline prepares the input data for the model by:
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1. Renaming features.
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2. Normalizing the input and output features based on dataset statistics.
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3. Adding a batch dimension.
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4. Moving the data to the specified device.
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The post-processing pipeline handles the model's output by:
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1. Moving the data to the CPU.
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2. Unnormalizing the output features to their original scale.
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Args:
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config: The configuration object for the diffusion policy,
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containing feature definitions, normalization mappings, and device information.
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dataset_stats: A dictionary of statistics used for normalization.
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Defaults to None.
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Returns:
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A tuple containing the configured pre-processor and post-processor pipelines.
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"""
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return make_default_pre_post_processors(config, dataset_stats)
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