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https://github.com/huggingface/lerobot.git
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8b4a5368b3
- Integrated ToBatchProcessor into various policy processors to handle observation batching. - Updated make functions for act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet processors to include the new batching functionality. - Ensured consistency across all processor implementations for improved data handling.
48 lines
1.7 KiB
Python
48 lines
1.7 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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import torch
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from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
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from lerobot.processor import (
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NormalizerProcessor,
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RobotProcessor,
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ToBatchProcessor,
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UnnormalizerProcessor,
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)
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def make_diffusion_processor(
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config: DiffusionConfig, dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None
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) -> tuple[RobotProcessor, RobotProcessor]:
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input_steps = [
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NormalizerProcessor(
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features=config.input_features, norm_map=config.normalization_mapping, stats=dataset_stats
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),
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NormalizerProcessor(
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features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
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),
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ToBatchProcessor(),
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]
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output_steps = [
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UnnormalizerProcessor(
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features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
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),
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]
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return RobotProcessor(steps=input_steps, name="diffusion_preprocessor"), RobotProcessor(
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steps=output_steps, name="diffusion_postprocessor"
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)
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