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35c5d43255
- Introduced `PREPROCESSOR_DEFAULT_NAME` and `POSTPROCESSOR_DEFAULT_NAME` constants for consistent naming across various processor implementations. - Updated processor creation in multiple policy files to utilize these constants, enhancing code readability and maintainability. - Modified the training script to load and save the preprocessor and postprocessor using the new constants.
54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 Seungjae Lee and Yibin Wang and Haritheja Etukuru
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# and H. Jin Kim and Nur Muhammad Mahi Shafiullah and Lerrel Pinto
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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.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
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from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig
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from lerobot.processor import (
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DeviceProcessor,
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NormalizerProcessor,
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RenameProcessor,
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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_vqbet_processor(
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config: VQBeTConfig, 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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RenameProcessor(rename_map={}), # Let the possibility to the user to rename the keys
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NormalizerProcessor(
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features={**config.input_features, **config.output_features},
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norm_map=config.normalization_mapping,
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stats=dataset_stats,
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),
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ToBatchProcessor(),
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DeviceProcessor(device=config.device),
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]
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output_steps = [
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DeviceProcessor(device="cpu"),
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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=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
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steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
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)
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