mirror of
https://github.com/huggingface/lerobot.git
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Steven Palma
104 lines
3.6 KiB
Python
104 lines
3.6 KiB
Python
#!/usr/bin/env python
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# Copyright 2025 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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AddBatchDimensionProcessorStep,
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DeviceProcessorStep,
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NewLineTaskProcessorStep,
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NormalizerProcessorStep,
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PolicyAction,
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PolicyProcessorPipeline,
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RenameObservationsProcessorStep,
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TokenizerProcessorStep,
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UnnormalizerProcessorStep,
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policy_action_to_transition,
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transition_to_policy_action,
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)
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from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME
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from .configuration_smolvla import SmolVLAConfig
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def make_smolvla_pre_post_processors(
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config: SmolVLAConfig,
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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 the SmolVLA policy.
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The pre-processing pipeline prepares input data for the model by:
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1. Renaming features to match pretrained configurations.
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2. Normalizing input and output features based on dataset statistics.
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3. Adding a batch dimension.
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4. Ensuring the language task description ends with a newline character.
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5. Tokenizing the language task description.
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6. Moving all 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 data to the CPU.
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2. Unnormalizing the output actions to their original scale.
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Args:
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config: The configuration object for the SmolVLA policy.
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dataset_stats: A dictionary of statistics for normalization.
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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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input_steps = [
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RenameObservationsProcessorStep(rename_map={}), # To mimic the same processor as pretrained one
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AddBatchDimensionProcessorStep(),
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NewLineTaskProcessorStep(),
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TokenizerProcessorStep(
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tokenizer_name=config.vlm_model_name,
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padding=config.pad_language_to,
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padding_side="right",
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max_length=config.tokenizer_max_length,
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),
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DeviceProcessorStep(device=config.device),
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NormalizerProcessorStep(
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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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]
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output_steps = [
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UnnormalizerProcessorStep(
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features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
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),
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DeviceProcessorStep(device="cpu"),
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]
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return (
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PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
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steps=input_steps,
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name=POLICY_PREPROCESSOR_DEFAULT_NAME,
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),
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PolicyProcessorPipeline[PolicyAction, PolicyAction](
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steps=output_steps,
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name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
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to_transition=policy_action_to_transition,
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to_output=transition_to_policy_action,
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),
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)
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