d8079587a2
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: Eugene Mironov <helper2424@gmail.com> Co-authored-by: s1lent4gnt <kmeftah.khalil@gmail.com> Co-authored-by: Ke Wang <superwk1017@gmail.com> Co-authored-by: Yoel Chornton <yoel.chornton@gmail.com> Co-authored-by: imstevenpmwork <steven.palma@huggingface.co> Co-authored-by: Simon Alibert <simon.alibert@huggingface.co>
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
# !/usr/bin/env python
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# Copyright 2025 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 dataclasses import dataclass, field
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from lerobot.common.optim.optimizers import AdamWConfig, OptimizerConfig
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from lerobot.common.optim.schedulers import LRSchedulerConfig
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from lerobot.configs.policies import PreTrainedConfig
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from lerobot.configs.types import NormalizationMode
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@PreTrainedConfig.register_subclass(name="reward_classifier")
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@dataclass
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class RewardClassifierConfig(PreTrainedConfig):
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"""Configuration for the Reward Classifier model."""
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name: str = "reward_classifier"
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num_classes: int = 2
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hidden_dim: int = 256
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latent_dim: int = 256
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image_embedding_pooling_dim: int = 8
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dropout_rate: float = 0.1
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model_name: str = "helper2424/resnet10"
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device: str = "cpu"
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model_type: str = "cnn" # "transformer" or "cnn"
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num_cameras: int = 2
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learning_rate: float = 1e-4
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weight_decay: float = 0.01
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grad_clip_norm: float = 1.0
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normalization_mapping: dict[str, NormalizationMode] = field(
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default_factory=lambda: {
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"VISUAL": NormalizationMode.MEAN_STD,
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}
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)
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@property
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def observation_delta_indices(self) -> list | None:
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return None
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@property
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def action_delta_indices(self) -> list | None:
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return None
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@property
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def reward_delta_indices(self) -> list | None:
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return None
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def get_optimizer_preset(self) -> OptimizerConfig:
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return AdamWConfig(
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lr=self.learning_rate,
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weight_decay=self.weight_decay,
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grad_clip_norm=self.grad_clip_norm,
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)
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def get_scheduler_preset(self) -> LRSchedulerConfig | None:
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return None
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def validate_features(self) -> None:
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"""Validate feature configurations."""
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has_image = any(key.startswith("observation.image") for key in self.input_features)
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if not has_image:
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raise ValueError(
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"You must provide an image observation (key starting with 'observation.image') in the input features"
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)
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