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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 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 | #!/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.
import logging
import math
from pprint import pformat
import torch
from lerobot.configs import PreTrainedConfig
from lerobot.configs.rewards import RewardModelConfig
from lerobot.configs.train import TrainPipelineConfig
from lerobot.transforms import ImageTransforms
from lerobot.utils.constants import ACTION, IMAGENET_STATS, OBS_PREFIX, REWARD
from .dataset_metadata import LeRobotDatasetMetadata
from .lerobot_dataset import LeRobotDataset
from .multi_dataset import MultiLeRobotDataset
from .streaming_dataset import StreamingLeRobotDataset
def resolve_delta_timestamps(
cfg: PreTrainedConfig | RewardModelConfig, ds_meta: LeRobotDatasetMetadata
) -> dict[str, list] | None:
"""Resolves delta_timestamps by reading from the 'delta_indices' properties of the config.
Args:
cfg (PreTrainedConfig | RewardModelConfig): The config to read delta_indices from. Both
``PreTrainedConfig`` and concrete ``RewardModelConfig`` subclasses expose the
``{observation,action,reward}_delta_indices`` properties used below.
ds_meta (LeRobotDatasetMetadata): The dataset from which features and fps are used to build
delta_timestamps against.
Returns:
dict[str, list] | None: A dictionary of delta_timestamps, e.g.:
{
"observation.state": [-0.04, -0.02, 0]
"observation.action": [-0.02, 0, 0.02]
}
returns `None` if the resulting dict is empty.
"""
delta_timestamps = {}
for key in ds_meta.features:
if key == REWARD and cfg.reward_delta_indices is not None:
delta_timestamps[key] = [i / ds_meta.fps for i in cfg.reward_delta_indices]
if key == ACTION and cfg.action_delta_indices is not None:
delta_timestamps[key] = [i / ds_meta.fps for i in cfg.action_delta_indices]
if key.startswith(OBS_PREFIX) and cfg.observation_delta_indices is not None:
delta_timestamps[key] = [i / ds_meta.fps for i in cfg.observation_delta_indices]
if len(delta_timestamps) == 0:
delta_timestamps = None
return delta_timestamps
def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDataset:
"""Handles the logic of setting up delta timestamps and image transforms before creating a dataset.
Args:
cfg (TrainPipelineConfig): A TrainPipelineConfig config which contains a DatasetConfig and a PreTrainedConfig.
Raises:
NotImplementedError: The MultiLeRobotDataset is currently deactivated.
Returns:
LeRobotDataset | MultiLeRobotDataset
"""
image_transforms = (
ImageTransforms(cfg.dataset.image_transforms) if cfg.dataset.image_transforms.enable else None
)
if isinstance(cfg.dataset.repo_id, str):
ds_meta = LeRobotDatasetMetadata(
cfg.dataset.repo_id, root=cfg.dataset.root, revision=cfg.dataset.revision
)
delta_timestamps = resolve_delta_timestamps(cfg.trainable_config, ds_meta)
if not cfg.dataset.streaming:
dataset = LeRobotDataset(
cfg.dataset.repo_id,
root=cfg.dataset.root,
episodes=cfg.dataset.episodes,
delta_timestamps=delta_timestamps,
image_transforms=image_transforms,
revision=cfg.dataset.revision,
video_backend=cfg.dataset.video_backend,
return_uint8=True,
depth_output_unit=cfg.dataset.depth_output_unit,
tolerance_s=cfg.tolerance_s,
)
else:
dataset = StreamingLeRobotDataset(
cfg.dataset.repo_id,
root=cfg.dataset.root,
episodes=cfg.dataset.episodes,
delta_timestamps=delta_timestamps,
image_transforms=image_transforms,
revision=cfg.dataset.revision,
max_num_shards=cfg.num_workers,
tolerance_s=cfg.tolerance_s,
return_uint8=True,
)
else:
raise NotImplementedError("The MultiLeRobotDataset isn't supported for now.")
dataset = MultiLeRobotDataset(
cfg.dataset.repo_id,
# TODO(aliberts): add proper support for multi dataset
# delta_timestamps=delta_timestamps,
image_transforms=image_transforms,
video_backend=cfg.dataset.video_backend,
)
logging.info(
"Multiple datasets were provided. Applied the following index mapping to the provided datasets: "
f"{pformat(dataset.repo_id_to_index, indent=2)}"
)
if cfg.dataset.use_imagenet_stats:
for key in dataset.meta.camera_keys:
if key in dataset.meta.depth_keys:
continue # Exclude depth keys from ImageNet stats
for stats_type, stats in IMAGENET_STATS.items():
dataset.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
return dataset
def make_train_eval_datasets(
cfg: TrainPipelineConfig,
) -> tuple[LeRobotDataset | MultiLeRobotDataset, LeRobotDataset | None]:
"""Create train and optional eval datasets by splitting episodes based on eval_split.
The last ceil(n_episodes * eval_split) episodes per task are held out for evaluation.
If eval_split == 0.0, returns (full_dataset, None).
"""
full_dataset = make_dataset(cfg)
if cfg.dataset.eval_split == 0.0:
return full_dataset, None
base_episodes = (
full_dataset.episodes if full_dataset.episodes is not None else list(range(full_dataset.num_episodes))
)
episode_tasks = full_dataset.meta.episodes["tasks"]
task_to_episodes: dict[str, list[int]] = {}
for ep_idx in base_episodes:
task_key = episode_tasks[ep_idx][0] if episode_tasks[ep_idx] else ""
task_to_episodes.setdefault(task_key, []).append(ep_idx)
train_episodes, eval_episodes = [], []
for eps in task_to_episodes.values():
n_eval = math.ceil(len(eps) * cfg.dataset.eval_split)
train_episodes.extend(eps[: len(eps) - n_eval])
eval_episodes.extend(eps[len(eps) - n_eval :])
if not train_episodes:
raise ValueError(
f"eval_split={cfg.dataset.eval_split} leaves 0 training episodes from {len(base_episodes)} total."
)
logging.info(
f"Train/eval split: {len(train_episodes)} train, {len(eval_episodes)} eval "
f"(eval_split={cfg.dataset.eval_split}, {len(task_to_episodes)} tasks)"
)
delta_timestamps = resolve_delta_timestamps(cfg.trainable_config, full_dataset.meta)
train_image_transforms = (
ImageTransforms(cfg.dataset.image_transforms) if cfg.dataset.image_transforms.enable else None
)
train_dataset = LeRobotDataset(
cfg.dataset.repo_id,
root=cfg.dataset.root,
episodes=train_episodes,
delta_timestamps=delta_timestamps,
image_transforms=train_image_transforms,
revision=cfg.dataset.revision,
video_backend=cfg.dataset.video_backend,
return_uint8=True,
tolerance_s=cfg.tolerance_s,
)
eval_dataset = LeRobotDataset(
cfg.dataset.repo_id,
root=cfg.dataset.root,
episodes=eval_episodes,
delta_timestamps=delta_timestamps,
image_transforms=None,
revision=cfg.dataset.revision,
video_backend=cfg.dataset.video_backend,
return_uint8=True,
tolerance_s=cfg.tolerance_s,
)
if cfg.dataset.use_imagenet_stats:
for ds in (train_dataset, eval_dataset):
for key in ds.meta.camera_keys:
for stats_type, stats in IMAGENET_STATS.items():
ds.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
return train_dataset, eval_dataset
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