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0d80452 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | # Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any
import torch
from lerobot.processor import (
DeviceProcessorStep,
IdentityProcessorStep,
NormalizerProcessorStep,
PolicyAction,
PolicyProcessorPipeline,
policy_action_to_transition,
transition_to_policy_action,
)
from .configuration_classifier import RewardClassifierConfig
def make_classifier_processor(
config: RewardClassifierConfig,
dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
) -> tuple[
PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
PolicyProcessorPipeline[PolicyAction, PolicyAction],
]:
"""
Constructs pre-processor and post-processor pipelines for the reward classifier.
The pre-processing pipeline prepares input data for the classifier by:
1. Normalizing both input and output features based on dataset statistics.
2. Moving the data to the specified device.
The post-processing pipeline handles the classifier's output by:
1. Moving the data to the CPU.
2. Applying an identity step, as no unnormalization is needed for the output logits.
Args:
config: The configuration object for the RewardClassifier.
dataset_stats: A dictionary of statistics for normalization.
Returns:
A tuple containing the configured pre-processor and post-processor pipelines.
"""
input_steps = [
NormalizerProcessorStep(
features=config.input_features, norm_map=config.normalization_mapping, stats=dataset_stats
),
NormalizerProcessorStep(
features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
),
DeviceProcessorStep(device=config.device),
]
output_steps = [DeviceProcessorStep(device="cpu"), IdentityProcessorStep()]
return (
PolicyProcessorPipeline(
steps=input_steps,
name="classifier_preprocessor",
),
PolicyProcessorPipeline(
steps=output_steps,
name="classifier_postprocessor",
to_transition=policy_action_to_transition,
to_output=transition_to_policy_action,
),
)
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