mirror of
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117 lines
5.3 KiB
Python
117 lines
5.3 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 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.datasets.transforms import ImageTransformsConfig
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from lerobot.datasets.video_utils import get_safe_default_codec
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@dataclass
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class DatasetConfig:
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# You may provide a list of datasets here. `train.py` creates them all and concatenates them. Note: only data
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# keys common between the datasets are kept. Each dataset gets and additional transform that inserts the
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# "dataset_index" into the returned item. The index mapping is made according to the order in which the
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# datasets are provided.
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repo_id: str
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# Root directory for a concrete local dataset tree (e.g. 'dataset/path'). If None, local datasets are
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# looked up under $HF_LEROBOT_HOME/repo_id and Hub downloads use a revision-safe cache under $HF_LEROBOT_HOME/hub.
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root: str | None = None
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episodes: list[int] | None = None
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image_transforms: ImageTransformsConfig = field(default_factory=ImageTransformsConfig)
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revision: str | None = None
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use_imagenet_stats: bool = False
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video_backend: str = field(default_factory=get_safe_default_codec)
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streaming: bool = True
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def __post_init__(self) -> None:
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if self.episodes is not None:
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if any(ep < 0 for ep in self.episodes):
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raise ValueError(
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f"Episode indices must be non-negative, got: {[ep for ep in self.episodes if ep < 0]}"
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)
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if len(self.episodes) != len(set(self.episodes)):
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duplicates = sorted({ep for ep in self.episodes if self.episodes.count(ep) > 1})
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raise ValueError(f"Episode indices contain duplicates: {duplicates}")
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@dataclass
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class WandBConfig:
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enable: bool = False
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# Set to true to disable saving an artifact despite training.save_checkpoint=True
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disable_artifact: bool = False
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project: str = "lerobot"
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entity: str | None = None
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notes: str | None = None
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run_id: str | None = None
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mode: str | None = None # Allowed values: 'online', 'offline' 'disabled'. Defaults to 'online'
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add_tags: bool = True # If True, save configuration as tags in the WandB run.
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@dataclass
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class EvalConfig:
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n_episodes: int = 50
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# `batch_size` specifies the number of environments to use in a gym.vector.VectorEnv.
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# Set to 0 for auto-tuning based on available CPU cores and n_episodes.
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batch_size: int = 0
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# `use_async_envs` specifies whether to use asynchronous environments (multiprocessing).
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# Defaults to True; automatically downgraded to SyncVectorEnv when batch_size=1.
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use_async_envs: bool = True
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def __post_init__(self) -> None:
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if self.batch_size == 0:
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self.batch_size = self._auto_batch_size()
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if self.batch_size > self.n_episodes:
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self.batch_size = self.n_episodes
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def _auto_batch_size(self) -> int:
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"""Pick batch_size based on CPU cores, capped by n_episodes."""
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import math
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import os
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cpu_cores = os.cpu_count() or 4
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# Each async env worker needs ~1 core; leave headroom for main process + inference.
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by_cpu = max(1, math.floor(cpu_cores * 0.7))
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return min(by_cpu, self.n_episodes, 64)
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@dataclass
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class PeftConfig:
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# PEFT offers many fine-tuning methods, layer adapters being the most common and currently also the most
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# effective methods so we'll focus on those in this high-level config interface.
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# Either a string (module name suffix or 'all-linear'), a list of module name suffixes or a regular expression
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# describing module names to target with the configured PEFT method. Some policies have a default value for this
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# so that you don't *have* to choose which layers to adapt but it might still be worthwhile depending on your case.
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target_modules: list[str] | str | None = None
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# Names/suffixes of modules to fully fine-tune and store alongside adapter weights. Useful for layers that are
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# not part of a pre-trained model (e.g., action state projections). Depending on the policy this defaults to layers
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# that are newly created in pre-trained policies. If you're fine-tuning an already trained policy you might want
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# to set this to `[]`. Corresponds to PEFT's `modules_to_save`.
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full_training_modules: list[str] | None = None
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# The PEFT (adapter) method to apply to the policy. Needs to be a valid PEFT type.
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method_type: str = "LORA"
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# Adapter initialization method. Look at the specific PEFT adapter documentation for defaults.
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init_type: str | None = None
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# We expect that all PEFT adapters are in some way doing rank-decomposition therefore this parameter specifies
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# the rank used for the adapter. In general a higher rank means more trainable parameters and closer to full
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# fine-tuning.
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r: int = 16
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