File size: 3,101 Bytes
f4a62da | 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 | #!/usr/bin/env python
# Copyright 2024 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 dataclasses import dataclass, field
from lerobot.datasets.transforms import ImageTransformsConfig
from lerobot.datasets.video_utils import get_safe_default_codec
@dataclass
class DatasetConfig:
# You may provide a list of datasets here. `train.py` creates them all and concatenates them. Note: only data
# keys common between the datasets are kept. Each dataset gets and additional transform that inserts the
# "dataset_index" into the returned item. The index mapping is made according to the order in which the
# datasets are provided.
repo_id: str
# Root directory where the dataset will be stored (e.g. 'dataset/path').
root: str | None = None
episodes: list[int] | None = None
image_transforms: ImageTransformsConfig = field(default_factory=ImageTransformsConfig)
revision: str | None = None
use_imagenet_stats: bool = True
video_backend: str = field(default_factory=get_safe_default_codec)
streaming: bool = False
@dataclass
class WandBConfig:
enable: bool = False
# Set to true to disable saving an artifact despite training.save_checkpoint=True
disable_artifact: bool = False
project: str = "lerobot"
entity: str | None = None
notes: str | None = None
run_id: str | None = None
mode: str | None = None # Allowed values: 'online', 'offline' 'disabled'. Defaults to 'online'
@dataclass
class EvalConfig:
n_episodes: int = 50
# `batch_size` specifies the number of environments to use in a gym.vector.VectorEnv.
batch_size: int = 50
# `use_async_envs` specifies whether to use asynchronous environments (multiprocessing).
use_async_envs: bool = False
def __post_init__(self) -> None:
if self.batch_size > self.n_episodes:
raise ValueError(
"The eval batch size is greater than the number of eval episodes "
f"({self.batch_size} > {self.n_episodes}). As a result, {self.batch_size} "
f"eval environments will be instantiated, but only {self.n_episodes} will be used. "
"This might significantly slow down evaluation. To fix this, you should update your command "
f"to increase the number of episodes to match the batch size (e.g. `eval.n_episodes={self.batch_size}`), "
f"or lower the batch size (e.g. `eval.batch_size={self.n_episodes}`)."
)
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