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#!/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 contextlib
import logging
import shutil
from pathlib import Path
from typing import Callable
import datasets
import numpy as np
import packaging.version
import PIL.Image
import torch
import torch.utils
from datasets import concatenate_datasets, load_dataset
from huggingface_hub import HfApi, snapshot_download
from huggingface_hub.constants import REPOCARD_NAME
from huggingface_hub.errors import RevisionNotFoundError
from lerobot.common.constants import HF_LEROBOT_HOME
from lerobot.common.datasets.compute_stats import aggregate_stats, compute_episode_stats
from lerobot.common.datasets.image_writer import AsyncImageWriter, write_image
from lerobot.common.datasets.utils import (
DEFAULT_FEATURES,
DEFAULT_IMAGE_PATH,
INFO_PATH,
TASKS_PATH,
append_jsonlines,
backward_compatible_episodes_stats,
check_delta_timestamps,
check_timestamps_sync,
check_version_compatibility,
create_empty_dataset_info,
create_lerobot_dataset_card,
embed_images,
get_delta_indices,
get_episode_data_index,
get_features_from_robot,
get_hf_features_from_features,
get_safe_version,
hf_transform_to_torch,
is_valid_version,
load_episodes,
load_episodes_stats,
load_info,
load_stats,
load_tasks,
validate_episode_buffer,
validate_frame,
write_episode,
write_episode_stats,
write_info,
write_json,
)
from lerobot.common.datasets.video_utils import (
VideoFrame,
decode_video_frames,
encode_video_frames,
get_safe_default_codec,
get_video_info,
)
from lerobot.common.robot_devices.robots.utils import Robot
CODEBASE_VERSION = "v2.1"
class LeRobotDatasetMetadata:
def __init__(
self,
repo_id: str,
root: str | Path | None = None,
revision: str | None = None,
force_cache_sync: bool = False,
):
self.repo_id = repo_id
self.revision = revision if revision else CODEBASE_VERSION
self.root = Path(root) if root is not None else HF_LEROBOT_HOME / repo_id
try:
if force_cache_sync:
raise FileNotFoundError
self.load_metadata()
except (FileNotFoundError, NotADirectoryError):
if is_valid_version(self.revision):
self.revision = get_safe_version(self.repo_id, self.revision)
(self.root / "meta").mkdir(exist_ok=True, parents=True)
self.pull_from_repo(allow_patterns="meta/")
self.load_metadata()
def load_metadata(self):
self.info = load_info(self.root)
check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
self.tasks, self.task_to_task_index = load_tasks(self.root)
self.episodes = load_episodes(self.root)
if self._version < packaging.version.parse("v2.1"):
self.stats = load_stats(self.root)
self.episodes_stats = backward_compatible_episodes_stats(self.stats, self.episodes)
else:
self.episodes_stats = load_episodes_stats(self.root)
self.stats = aggregate_stats(list(self.episodes_stats.values()))
def pull_from_repo(
self,
allow_patterns: list[str] | str | None = None,
ignore_patterns: list[str] | str | None = None,
) -> None:
snapshot_download(
self.repo_id,
repo_type="dataset",
revision=self.revision,
local_dir=self.root,
allow_patterns=allow_patterns,
ignore_patterns=ignore_patterns,
)
@property
def _version(self) -> packaging.version.Version:
"""Codebase version used to create this dataset."""
return packaging.version.parse(self.info["codebase_version"])
def get_data_file_path(self, ep_index: int) -> Path:
ep_chunk = self.get_episode_chunk(ep_index)
fpath = self.data_path.format(episode_chunk=ep_chunk, episode_index=ep_index)
return Path(fpath)
def get_video_file_path(self, ep_index: int, vid_key: str) -> Path:
ep_chunk = self.get_episode_chunk(ep_index)
fpath = self.video_path.format(episode_chunk=ep_chunk, video_key=vid_key, episode_index=ep_index)
return Path(fpath)
def get_episode_chunk(self, ep_index: int) -> int:
return ep_index // self.chunks_size
@property
def data_path(self) -> str:
"""Formattable string for the parquet files."""
return self.info["data_path"]
@property
def video_path(self) -> str | None:
"""Formattable string for the video files."""
return self.info["video_path"]
@property
def robot_type(self) -> str | None:
"""Robot type used in recording this dataset."""
return self.info["robot_type"]
@property
def fps(self) -> int:
"""Frames per second used during data collection."""
return self.info["fps"]
@property
def features(self) -> dict[str, dict]:
"""All features contained in the dataset."""
return self.info["features"]
@property
def image_keys(self) -> list[str]:
"""Keys to access visual modalities stored as images."""
return [key for key, ft in self.features.items() if ft["dtype"] == "image"]
@property
def video_keys(self) -> list[str]:
"""Keys to access visual modalities stored as videos."""
return [key for key, ft in self.features.items() if ft["dtype"] == "video"]
@property
def camera_keys(self) -> list[str]:
"""Keys to access visual modalities (regardless of their storage method)."""
return [key for key, ft in self.features.items() if ft["dtype"] in ["video", "image"]]
@property
def names(self) -> dict[str, list | dict]:
"""Names of the various dimensions of vector modalities."""
return {key: ft["names"] for key, ft in self.features.items()}
@property
def shapes(self) -> dict:
"""Shapes for the different features."""
return {key: tuple(ft["shape"]) for key, ft in self.features.items()}
@property
def total_episodes(self) -> int:
"""Total number of episodes available."""
return self.info["total_episodes"]
@property
def total_frames(self) -> int:
"""Total number of frames saved in this dataset."""
return self.info["total_frames"]
@property
def total_tasks(self) -> int:
"""Total number of different tasks performed in this dataset."""
return self.info["total_tasks"]
@property
def total_chunks(self) -> int:
"""Total number of chunks (groups of episodes)."""
return self.info["total_chunks"]
@property
def chunks_size(self) -> int:
"""Max number of episodes per chunk."""
return self.info["chunks_size"]
def get_task_index(self, task: str) -> int | None:
"""
Given a task in natural language, returns its task_index if the task already exists in the dataset,
otherwise return None.
"""
return self.task_to_task_index.get(task, None)
def add_task(self, task: str):
"""
Given a task in natural language, add it to the dictionary of tasks.
"""
if task in self.task_to_task_index:
raise ValueError(f"The task '{task}' already exists and can't be added twice.")
task_index = self.info["total_tasks"]
self.task_to_task_index[task] = task_index
self.tasks[task_index] = task
self.info["total_tasks"] += 1
task_dict = {
"task_index": task_index,
"task": task,
}
append_jsonlines(task_dict, self.root / TASKS_PATH)
def save_episode(
self,
episode_index: int,
episode_length: int,
episode_tasks: list[str],
episode_stats: dict[str, dict],
) -> None:
self.info["total_episodes"] += 1
self.info["total_frames"] += episode_length
chunk = self.get_episode_chunk(episode_index)
if chunk >= self.total_chunks:
self.info["total_chunks"] += 1
self.info["splits"] = {"train": f"0:{self.info['total_episodes']}"}
self.info["total_videos"] += len(self.video_keys)
if len(self.video_keys) > 0:
self.update_video_info()
write_info(self.info, self.root)
episode_dict = {
"episode_index": episode_index,
"tasks": episode_tasks,
"length": episode_length,
}
self.episodes[episode_index] = episode_dict
write_episode(episode_dict, self.root)
self.episodes_stats[episode_index] = episode_stats
self.stats = aggregate_stats([self.stats, episode_stats]) if self.stats else episode_stats
write_episode_stats(episode_index, episode_stats, self.root)
def update_video_info(self) -> None:
"""
Warning: this function writes info from first episode videos, implicitly assuming that all videos have
been encoded the same way. Also, this means it assumes the first episode exists.
"""
for key in self.video_keys:
if not self.features[key].get("info", None):
video_path = self.root / self.get_video_file_path(ep_index=0, vid_key=key)
self.info["features"][key]["info"] = get_video_info(video_path)
def __repr__(self):
feature_keys = list(self.features)
return (
f"{self.__class__.__name__}({{\n"
f" Repository ID: '{self.repo_id}',\n"
f" Total episodes: '{self.total_episodes}',\n"
f" Total frames: '{self.total_frames}',\n"
f" Features: '{feature_keys}',\n"
"})',\n"
)
@classmethod
def create(
cls,
repo_id: str,
fps: int,
root: str | Path | None = None,
robot: Robot | None = None,
robot_type: str | None = None,
features: dict | None = None,
use_videos: bool = True,
) -> "LeRobotDatasetMetadata":
"""Creates metadata for a LeRobotDataset."""
obj = cls.__new__(cls)
obj.repo_id = repo_id
obj.root = Path(root) if root is not None else HF_LEROBOT_HOME / repo_id
obj.root.mkdir(parents=True, exist_ok=False)
if robot is not None:
features = get_features_from_robot(robot, use_videos)
robot_type = robot.robot_type
if not all(cam.fps == fps for cam in robot.cameras.values()):
logging.warning(
f"Some cameras in your {robot.robot_type} robot don't have an fps matching the fps of your dataset."
"In this case, frames from lower fps cameras will be repeated to fill in the blanks."
)
elif features is None:
raise ValueError(
"Dataset features must either come from a Robot or explicitly passed upon creation."
)
else:
# TODO(aliberts, rcadene): implement sanity check for features
features = {**features, **DEFAULT_FEATURES}
# check if none of the features contains a "/" in their names,
# as this would break the dict flattening in the stats computation, which uses '/' as separator
for key in features:
if "/" in key:
raise ValueError(f"Feature names should not contain '/'. Found '/' in feature '{key}'.")
features = {**features, **DEFAULT_FEATURES}
obj.tasks, obj.task_to_task_index = {}, {}
obj.episodes_stats, obj.stats, obj.episodes = {}, {}, {}
obj.info = create_empty_dataset_info(CODEBASE_VERSION, fps, robot_type, features, use_videos)
if len(obj.video_keys) > 0 and not use_videos:
raise ValueError()
write_json(obj.info, obj.root / INFO_PATH)
obj.revision = None
return obj
class LeRobotDataset(torch.utils.data.Dataset):
def __init__(
self,
repo_id: str,
root: str | Path | None = None,
episodes: list[int] | None = None,
image_transforms: Callable | None = None,
delta_timestamps: dict[list[float]] | None = None,
tolerance_s: float = 1e-4,
revision: str | None = None,
force_cache_sync: bool = False,
download_videos: bool = True,
video_backend: str | None = None,
):
"""
2 modes are available for instantiating this class, depending on 2 different use cases:
1. Your dataset already exists:
- On your local disk in the 'root' folder. This is typically the case when you recorded your
dataset locally and you may or may not have pushed it to the hub yet. Instantiating this class
with 'root' will load your dataset directly from disk. This can happen while you're offline (no
internet connection).
- On the Hugging Face Hub at the address https://huggingface.co/datasets/{repo_id} and not on
your local disk in the 'root' folder. Instantiating this class with this 'repo_id' will download
the dataset from that address and load it, pending your dataset is compliant with
codebase_version v2.0. If your dataset has been created before this new format, you will be
prompted to convert it using our conversion script from v1.6 to v2.0, which you can find at
lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py.
2. Your dataset doesn't already exists (either on local disk or on the Hub): you can create an empty
LeRobotDataset with the 'create' classmethod. This can be used for recording a dataset or port an
existing dataset to the LeRobotDataset format.
In terms of files, LeRobotDataset encapsulates 3 main things:
- metadata:
- info contains various information about the dataset like shapes, keys, fps etc.
- stats stores the dataset statistics of the different modalities for normalization
- tasks contains the prompts for each task of the dataset, which can be used for
task-conditioned training.
- hf_dataset (from datasets.Dataset), which will read any values from parquet files.
- videos (optional) from which frames are loaded to be synchronous with data from parquet files.
A typical LeRobotDataset looks like this from its root path:
.
├── data
│ ├── chunk-000
│ │ ├── episode_000000.parquet
│ │ ├── episode_000001.parquet
│ │ ├── episode_000002.parquet
│ │ └── ...
│ ├── chunk-001
│ │ ├── episode_001000.parquet
│ │ ├── episode_001001.parquet
│ │ ├── episode_001002.parquet
│ │ └── ...
│ └── ...
├── meta
│ ├── episodes.jsonl
│ ├── info.json
│ ├── stats.json
│ └── tasks.jsonl
└── videos
├── chunk-000
│ ├── observation.images.laptop
│ │ ├── episode_000000.mp4
│ │ ├── episode_000001.mp4
│ │ ├── episode_000002.mp4
│ │ └── ...
│ ├── observation.images.phone
│ │ ├── episode_000000.mp4
│ │ ├── episode_000001.mp4
│ │ ├── episode_000002.mp4
│ │ └── ...
├── chunk-001
└── ...
Note that this file-based structure is designed to be as versatile as possible. The files are split by
episodes which allows a more granular control over which episodes one wants to use and download. The
structure of the dataset is entirely described in the info.json file, which can be easily downloaded
or viewed directly on the hub before downloading any actual data. The type of files used are very
simple and do not need complex tools to be read, it only uses .parquet, .json and .mp4 files (and .md
for the README).
Args:
repo_id (str): This is the repo id that will be used to fetch the dataset. Locally, the dataset
will be stored under root/repo_id.
root (Path | None, optional): Local directory to use for downloading/writing files. You can also
set the LEROBOT_HOME environment variable to point to a different location. Defaults to
'~/.cache/huggingface/lerobot'.
episodes (list[int] | None, optional): If specified, this will only load episodes specified by
their episode_index in this list. Defaults to None.
image_transforms (Callable | None, optional): You can pass standard v2 image transforms from
torchvision.transforms.v2 here which will be applied to visual modalities (whether they come
from videos or images). Defaults to None.
delta_timestamps (dict[list[float]] | None, optional): _description_. Defaults to None.
tolerance_s (float, optional): Tolerance in seconds used to ensure data timestamps are actually in
sync with the fps value. It is used at the init of the dataset to make sure that each
timestamps is separated to the next by 1/fps +/- tolerance_s. This also applies to frames
decoded from video files. It is also used to check that `delta_timestamps` (when provided) are
multiples of 1/fps. Defaults to 1e-4.
revision (str, optional): An optional Git revision id which can be a branch name, a tag, or a
commit hash. Defaults to current codebase version tag.
sync_cache_first (bool, optional): Flag to sync and refresh local files first. If True and files
are already present in the local cache, this will be faster. However, files loaded might not
be in sync with the version on the hub, especially if you specified 'revision'. Defaults to
False.
download_videos (bool, optional): Flag to download the videos. Note that when set to True but the
video files are already present on local disk, they won't be downloaded again. Defaults to
True.
video_backend (str | None, optional): Video backend to use for decoding videos. Defaults to torchcodec when available int the platform; otherwise, defaults to 'pyav'.
You can also use the 'pyav' decoder used by Torchvision, which used to be the default option, or 'video_reader' which is another decoder of Torchvision.
"""
super().__init__()
self.repo_id = repo_id
self.root = Path(root) if root else HF_LEROBOT_HOME / repo_id
self.image_transforms = image_transforms
self.delta_timestamps = delta_timestamps
self.episodes = episodes
self.tolerance_s = tolerance_s
self.revision = revision if revision else CODEBASE_VERSION
self.video_backend = video_backend if video_backend else get_safe_default_codec()
self.delta_indices = None
# Unused attributes
self.image_writer = None
self.episode_buffer = None
self.root.mkdir(exist_ok=True, parents=True)
# Load metadata
self.meta = LeRobotDatasetMetadata(
self.repo_id, self.root, self.revision, force_cache_sync=force_cache_sync
)
if self.episodes is not None and self.meta._version >= packaging.version.parse("v2.1"):
episodes_stats = [self.meta.episodes_stats[ep_idx] for ep_idx in self.episodes]
self.stats = aggregate_stats(episodes_stats)
# Load actual data
try:
if force_cache_sync:
raise FileNotFoundError
assert all((self.root / fpath).is_file() for fpath in self.get_episodes_file_paths())
self.hf_dataset = self.load_hf_dataset()
except (AssertionError, FileNotFoundError, NotADirectoryError):
self.revision = get_safe_version(self.repo_id, self.revision)
self.download_episodes(download_videos)
self.hf_dataset = self.load_hf_dataset()
self.episode_data_index = get_episode_data_index(self.meta.episodes, self.episodes)
# Check timestamps
timestamps = torch.stack(self.hf_dataset["timestamp"]).numpy()
episode_indices = torch.stack(self.hf_dataset["episode_index"]).numpy()
ep_data_index_np = {k: t.numpy() for k, t in self.episode_data_index.items()}
check_timestamps_sync(timestamps, episode_indices, ep_data_index_np, self.fps, self.tolerance_s)
# Setup delta_indices
if self.delta_timestamps is not None:
check_delta_timestamps(self.delta_timestamps, self.fps, self.tolerance_s)
self.delta_indices = get_delta_indices(self.delta_timestamps, self.fps)
def push_to_hub(
self,
branch: str | None = None,
tags: list | None = None,
license: str | None = "apache-2.0",
tag_version: bool = True,
push_videos: bool = True,
private: bool = False,
allow_patterns: list[str] | str | None = None,
upload_large_folder: bool = False,
**card_kwargs,
) -> None:
ignore_patterns = ["images/"]
if not push_videos:
ignore_patterns.append("videos/")
hub_api = HfApi()
hub_api.create_repo(
repo_id=self.repo_id,
private=private,
repo_type="dataset",
exist_ok=True,
)
if branch:
hub_api.create_branch(
repo_id=self.repo_id,
branch=branch,
revision=self.revision,
repo_type="dataset",
exist_ok=True,
)
upload_kwargs = {
"repo_id": self.repo_id,
"folder_path": self.root,
"repo_type": "dataset",
"revision": branch,
"allow_patterns": allow_patterns,
"ignore_patterns": ignore_patterns,
}
if upload_large_folder:
hub_api.upload_large_folder(**upload_kwargs)
else:
hub_api.upload_folder(**upload_kwargs)
if not hub_api.file_exists(self.repo_id, REPOCARD_NAME, repo_type="dataset", revision=branch):
card = create_lerobot_dataset_card(
tags=tags, dataset_info=self.meta.info, license=license, **card_kwargs
)
card.push_to_hub(repo_id=self.repo_id, repo_type="dataset", revision=branch)
if tag_version:
with contextlib.suppress(RevisionNotFoundError):
hub_api.delete_tag(self.repo_id, tag=CODEBASE_VERSION, repo_type="dataset")
hub_api.create_tag(self.repo_id, tag=CODEBASE_VERSION, revision=branch, repo_type="dataset")
def pull_from_repo(
self,
allow_patterns: list[str] | str | None = None,
ignore_patterns: list[str] | str | None = None,
) -> None:
snapshot_download(
self.repo_id,
repo_type="dataset",
revision=self.revision,
local_dir=self.root,
allow_patterns=allow_patterns,
ignore_patterns=ignore_patterns,
)
def download_episodes(self, download_videos: bool = True) -> None:
"""Downloads the dataset from the given 'repo_id' at the provided version. If 'episodes' is given, this
will only download those episodes (selected by their episode_index). If 'episodes' is None, the whole
dataset will be downloaded. Thanks to the behavior of snapshot_download, if the files are already present
in 'local_dir', they won't be downloaded again.
"""
# TODO(rcadene, aliberts): implement faster transfer
# https://huggingface.co/docs/huggingface_hub/en/guides/download#faster-downloads
files = None
ignore_patterns = None if download_videos else "videos/"
if self.episodes is not None:
files = self.get_episodes_file_paths()
self.pull_from_repo(allow_patterns=files, ignore_patterns=ignore_patterns)
def get_episodes_file_paths(self) -> list[Path]:
episodes = self.episodes if self.episodes is not None else list(range(self.meta.total_episodes))
fpaths = [str(self.meta.get_data_file_path(ep_idx)) for ep_idx in episodes]
if len(self.meta.video_keys) > 0:
video_files = [
str(self.meta.get_video_file_path(ep_idx, vid_key))
for vid_key in self.meta.video_keys
for ep_idx in episodes
]
fpaths += video_files
return fpaths
def load_hf_dataset(self) -> datasets.Dataset:
"""hf_dataset contains all the observations, states, actions, rewards, etc."""
if self.episodes is None:
path = str(self.root / "data")
hf_dataset = load_dataset("parquet", data_dir=path, split="train")
else:
files = [str(self.root / self.meta.get_data_file_path(ep_idx)) for ep_idx in self.episodes]
hf_dataset = load_dataset("parquet", data_files=files, split="train")
# TODO(aliberts): hf_dataset.set_format("torch")
hf_dataset.set_transform(hf_transform_to_torch)
return hf_dataset
def create_hf_dataset(self) -> datasets.Dataset:
features = get_hf_features_from_features(self.features)
ft_dict = {col: [] for col in features}
hf_dataset = datasets.Dataset.from_dict(ft_dict, features=features, split="train")
# TODO(aliberts): hf_dataset.set_format("torch")
hf_dataset.set_transform(hf_transform_to_torch)
return hf_dataset
@property
def fps(self) -> int:
"""Frames per second used during data collection."""
return self.meta.fps
@property
def num_frames(self) -> int:
"""Number of frames in selected episodes."""
return len(self.hf_dataset) if self.hf_dataset is not None else self.meta.total_frames
@property
def num_episodes(self) -> int:
"""Number of episodes selected."""
return len(self.episodes) if self.episodes is not None else self.meta.total_episodes
@property
def features(self) -> dict[str, dict]:
return self.meta.features
@property
def hf_features(self) -> datasets.Features:
"""Features of the hf_dataset."""
if self.hf_dataset is not None:
return self.hf_dataset.features
else:
return get_hf_features_from_features(self.features)
def _get_query_indices(self, idx: int, ep_idx: int) -> tuple[dict[str, list[int | bool]]]:
ep_start = self.episode_data_index["from"][ep_idx]
ep_end = self.episode_data_index["to"][ep_idx]
query_indices = {
key: [max(ep_start.item(), min(ep_end.item() - 1, idx + delta)) for delta in delta_idx]
for key, delta_idx in self.delta_indices.items()
}
padding = { # Pad values outside of current episode range
f"{key}_is_pad": torch.BoolTensor(
[(idx + delta < ep_start.item()) | (idx + delta >= ep_end.item()) for delta in delta_idx]
)
for key, delta_idx in self.delta_indices.items()
}
return query_indices, padding
def _get_query_timestamps(
self,
current_ts: float,
query_indices: dict[str, list[int]] | None = None,
) -> dict[str, list[float]]:
query_timestamps = {}
for key in self.meta.video_keys:
if query_indices is not None and key in query_indices:
timestamps = self.hf_dataset.select(query_indices[key])["timestamp"]
query_timestamps[key] = torch.stack(timestamps).tolist()
else:
query_timestamps[key] = [current_ts]
return query_timestamps
def _query_hf_dataset(self, query_indices: dict[str, list[int]]) -> dict:
return {
key: torch.stack(self.hf_dataset.select(q_idx)[key])
for key, q_idx in query_indices.items()
if key not in self.meta.video_keys
}
def _query_videos(self, query_timestamps: dict[str, list[float]], ep_idx: int) -> dict[str, torch.Tensor]:
"""Note: When using data workers (e.g. DataLoader with num_workers>0), do not call this function
in the main process (e.g. by using a second Dataloader with num_workers=0). It will result in a
Segmentation Fault. This probably happens because a memory reference to the video loader is created in
the main process and a subprocess fails to access it.
"""
item = {}
for vid_key, query_ts in query_timestamps.items():
video_path = self.root / self.meta.get_video_file_path(ep_idx, vid_key)
frames = decode_video_frames(video_path, query_ts, self.tolerance_s, self.video_backend)
item[vid_key] = frames.squeeze(0)
return item
def _add_padding_keys(self, item: dict, padding: dict[str, list[bool]]) -> dict:
for key, val in padding.items():
item[key] = torch.BoolTensor(val)
return item
def __len__(self):
return self.num_frames
def __getitem__(self, idx) -> dict:
item = self.hf_dataset[idx]
ep_idx = item["episode_index"].item()
query_indices = None
if self.delta_indices is not None:
query_indices, padding = self._get_query_indices(idx, ep_idx)
query_result = self._query_hf_dataset(query_indices)
item = {**item, **padding}
for key, val in query_result.items():
item[key] = val
if len(self.meta.video_keys) > 0:
current_ts = item["timestamp"].item()
query_timestamps = self._get_query_timestamps(current_ts, query_indices)
video_frames = self._query_videos(query_timestamps, ep_idx)
item = {**video_frames, **item}
if self.image_transforms is not None:
image_keys = self.meta.camera_keys
for cam in image_keys:
item[cam] = self.image_transforms(item[cam])
# Add task as a string
task_idx = item["task_index"].item()
item["task"] = self.meta.tasks[task_idx]
return item
def __repr__(self):
feature_keys = list(self.features)
return (
f"{self.__class__.__name__}({{\n"
f" Repository ID: '{self.repo_id}',\n"
f" Number of selected episodes: '{self.num_episodes}',\n"
f" Number of selected samples: '{self.num_frames}',\n"
f" Features: '{feature_keys}',\n"
"})',\n"
)
def create_episode_buffer(self, episode_index: int | None = None) -> dict:
current_ep_idx = self.meta.total_episodes if episode_index is None else episode_index
ep_buffer = {}
# size and task are special cases that are not in self.features
ep_buffer["size"] = 0
ep_buffer["task"] = []
for key in self.features:
ep_buffer[key] = current_ep_idx if key == "episode_index" else []
return ep_buffer
def _get_image_file_path(self, episode_index: int, image_key: str, frame_index: int) -> Path:
fpath = DEFAULT_IMAGE_PATH.format(
image_key=image_key, episode_index=episode_index, frame_index=frame_index
)
return self.root / fpath
def _save_image(self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path) -> None:
if self.image_writer is None:
if isinstance(image, torch.Tensor):
image = image.cpu().numpy()
write_image(image, fpath)
else:
self.image_writer.save_image(image=image, fpath=fpath)
def add_frame(self, frame: dict) -> None:
"""
This function only adds the frame to the episode_buffer. Apart from images — which are written in a
temporary directory — nothing is written to disk. To save those frames, the 'save_episode()' method
then needs to be called.
"""
# Convert torch to numpy if needed
for name in frame:
if isinstance(frame[name], torch.Tensor):
frame[name] = frame[name].numpy()
validate_frame(frame, self.features)
if self.episode_buffer is None:
self.episode_buffer = self.create_episode_buffer()
# Automatically add frame_index and timestamp to episode buffer
frame_index = self.episode_buffer["size"]
timestamp = frame.pop("timestamp") if "timestamp" in frame else frame_index / self.fps
self.episode_buffer["frame_index"].append(frame_index)
self.episode_buffer["timestamp"].append(timestamp)
# Add frame features to episode_buffer
for key in frame:
if key == "task":
# Note: we associate the task in natural language to its task index during `save_episode`
self.episode_buffer["task"].append(frame["task"])
continue
if key not in self.features:
raise ValueError(
f"An element of the frame is not in the features. '{key}' not in '{self.features.keys()}'."
)
if self.features[key]["dtype"] in ["image", "video"]:
img_path = self._get_image_file_path(
episode_index=self.episode_buffer["episode_index"], image_key=key, frame_index=frame_index
)
if frame_index == 0:
img_path.parent.mkdir(parents=True, exist_ok=True)
self._save_image(frame[key], img_path)
self.episode_buffer[key].append(str(img_path))
else:
self.episode_buffer[key].append(frame[key])
self.episode_buffer["size"] += 1
def save_episode(self, episode_data: dict | None = None) -> None:
"""
This will save to disk the current episode in self.episode_buffer.
Args:
episode_data (dict | None, optional): Dict containing the episode data to save. If None, this will
save the current episode in self.episode_buffer, which is filled with 'add_frame'. Defaults to
None.
"""
if not episode_data:
episode_buffer = self.episode_buffer
validate_episode_buffer(episode_buffer, self.meta.total_episodes, self.features)
# size and task are special cases that won't be added to hf_dataset
episode_length = episode_buffer.pop("size")
tasks = episode_buffer.pop("task")
episode_tasks = list(set(tasks))
episode_index = episode_buffer["episode_index"]
episode_buffer["index"] = np.arange(self.meta.total_frames, self.meta.total_frames + episode_length)
episode_buffer["episode_index"] = np.full((episode_length,), episode_index)
# Add new tasks to the tasks dictionary
for task in episode_tasks:
task_index = self.meta.get_task_index(task)
if task_index is None:
self.meta.add_task(task)
# Given tasks in natural language, find their corresponding task indices
episode_buffer["task_index"] = np.array([self.meta.get_task_index(task) for task in tasks])
for key, ft in self.features.items():
# index, episode_index, task_index are already processed above, and image and video
# are processed separately by storing image path and frame info as meta data
if key in ["index", "episode_index", "task_index"] or ft["dtype"] in ["image", "video"]:
continue
episode_buffer[key] = np.stack(episode_buffer[key])
self._wait_image_writer()
self._save_episode_table(episode_buffer, episode_index)
ep_stats = compute_episode_stats(episode_buffer, self.features)
if len(self.meta.video_keys) > 0:
video_paths = self.encode_episode_videos(episode_index)
for key in self.meta.video_keys:
episode_buffer[key] = video_paths[key]
# `meta.save_episode` be executed after encoding the videos
self.meta.save_episode(episode_index, episode_length, episode_tasks, ep_stats)
ep_data_index = get_episode_data_index(self.meta.episodes, [episode_index])
ep_data_index_np = {k: t.numpy() for k, t in ep_data_index.items()}
check_timestamps_sync(
episode_buffer["timestamp"],
episode_buffer["episode_index"],
ep_data_index_np,
self.fps,
self.tolerance_s,
)
video_files = list(self.root.rglob("*.mp4"))
assert len(video_files) == self.num_episodes * len(self.meta.video_keys)
parquet_files = list(self.root.rglob("*.parquet"))
assert len(parquet_files) == self.num_episodes
# delete images
img_dir = self.root / "images"
if img_dir.is_dir():
shutil.rmtree(self.root / "images")
if not episode_data: # Reset the buffer
self.episode_buffer = self.create_episode_buffer()
def _save_episode_table(self, episode_buffer: dict, episode_index: int) -> None:
episode_dict = {key: episode_buffer[key] for key in self.hf_features}
ep_dataset = datasets.Dataset.from_dict(episode_dict, features=self.hf_features, split="train")
ep_dataset = embed_images(ep_dataset)
self.hf_dataset = concatenate_datasets([self.hf_dataset, ep_dataset])
self.hf_dataset.set_transform(hf_transform_to_torch)
ep_data_path = self.root / self.meta.get_data_file_path(ep_index=episode_index)
ep_data_path.parent.mkdir(parents=True, exist_ok=True)
ep_dataset.to_parquet(ep_data_path)
def clear_episode_buffer(self) -> None:
episode_index = self.episode_buffer["episode_index"]
if self.image_writer is not None:
for cam_key in self.meta.camera_keys:
img_dir = self._get_image_file_path(
episode_index=episode_index, image_key=cam_key, frame_index=0
).parent
if img_dir.is_dir():
shutil.rmtree(img_dir)
# Reset the buffer
self.episode_buffer = self.create_episode_buffer()
def start_image_writer(self, num_processes: int = 0, num_threads: int = 4) -> None:
if isinstance(self.image_writer, AsyncImageWriter):
logging.warning(
"You are starting a new AsyncImageWriter that is replacing an already existing one in the dataset."
)
self.image_writer = AsyncImageWriter(
num_processes=num_processes,
num_threads=num_threads,
)
def stop_image_writer(self) -> None:
"""
Whenever wrapping this dataset inside a parallelized DataLoader, this needs to be called first to
remove the image_writer in order for the LeRobotDataset object to be pickleable and parallelized.
"""
if self.image_writer is not None:
self.image_writer.stop()
self.image_writer = None
def _wait_image_writer(self) -> None:
"""Wait for asynchronous image writer to finish."""
if self.image_writer is not None:
self.image_writer.wait_until_done()
def encode_videos(self) -> None:
"""
Use ffmpeg to convert frames stored as png into mp4 videos.
Note: `encode_video_frames` is a blocking call. Making it asynchronous shouldn't speedup encoding,
since video encoding with ffmpeg is already using multithreading.
"""
for ep_idx in range(self.meta.total_episodes):
self.encode_episode_videos(ep_idx)
def encode_episode_videos(self, episode_index: int) -> dict:
"""
Use ffmpeg to convert frames stored as png into mp4 videos.
Note: `encode_video_frames` is a blocking call. Making it asynchronous shouldn't speedup encoding,
since video encoding with ffmpeg is already using multithreading.
"""
video_paths = {}
for key in self.meta.video_keys:
video_path = self.root / self.meta.get_video_file_path(episode_index, key)
video_paths[key] = str(video_path)
if video_path.is_file():
# Skip if video is already encoded. Could be the case when resuming data recording.
continue
img_dir = self._get_image_file_path(
episode_index=episode_index, image_key=key, frame_index=0
).parent
encode_video_frames(img_dir, video_path, self.fps, overwrite=True)
return video_paths
@classmethod
def create(
cls,
repo_id: str,
fps: int,
root: str | Path | None = None,
robot: Robot | None = None,
robot_type: str | None = None,
features: dict | None = None,
use_videos: bool = True,
tolerance_s: float = 1e-4,
image_writer_processes: int = 0,
image_writer_threads: int = 0,
video_backend: str | None = None,
) -> "LeRobotDataset":
"""Create a LeRobot Dataset from scratch in order to record data."""
obj = cls.__new__(cls)
obj.meta = LeRobotDatasetMetadata.create(
repo_id=repo_id,
fps=fps,
root=root,
robot=robot,
robot_type=robot_type,
features=features,
use_videos=use_videos,
)
obj.repo_id = obj.meta.repo_id
obj.root = obj.meta.root
obj.revision = None
obj.tolerance_s = tolerance_s
obj.image_writer = None
if image_writer_processes or image_writer_threads:
obj.start_image_writer(image_writer_processes, image_writer_threads)
# TODO(aliberts, rcadene, alexander-soare): Merge this with OnlineBuffer/DataBuffer
obj.episode_buffer = obj.create_episode_buffer()
obj.episodes = None
obj.hf_dataset = obj.create_hf_dataset()
obj.image_transforms = None
obj.delta_timestamps = None
obj.delta_indices = None
obj.episode_data_index = None
obj.video_backend = video_backend if video_backend is not None else get_safe_default_codec()
return obj
class MultiLeRobotDataset(torch.utils.data.Dataset):
"""A dataset consisting of multiple underlying `LeRobotDataset`s.
The underlying `LeRobotDataset`s are effectively concatenated, and this class adopts much of the API
structure of `LeRobotDataset`.
"""
def __init__(
self,
repo_ids: list[str],
root: str | Path | None = None,
episodes: dict | None = None,
image_transforms: Callable | None = None,
delta_timestamps: dict[list[float]] | None = None,
tolerances_s: dict | None = None,
download_videos: bool = True,
video_backend: str | None = None,
):
super().__init__()
self.repo_ids = repo_ids
self.root = Path(root) if root else HF_LEROBOT_HOME
self.tolerances_s = tolerances_s if tolerances_s else dict.fromkeys(repo_ids, 0.0001)
# Construct the underlying datasets passing everything but `transform` and `delta_timestamps` which
# are handled by this class.
self._datasets = [
LeRobotDataset(
repo_id,
root=self.root / repo_id,
episodes=episodes[repo_id] if episodes else None,
image_transforms=image_transforms,
delta_timestamps=delta_timestamps,
tolerance_s=self.tolerances_s[repo_id],
download_videos=download_videos,
video_backend=video_backend,
)
for repo_id in repo_ids
]
# Disable any data keys that are not common across all of the datasets. Note: we may relax this
# restriction in future iterations of this class. For now, this is necessary at least for being able
# to use PyTorch's default DataLoader collate function.
self.disabled_features = set()
intersection_features = set(self._datasets[0].features)
for ds in self._datasets:
intersection_features.intersection_update(ds.features)
if len(intersection_features) == 0:
raise RuntimeError(
"Multiple datasets were provided but they had no keys common to all of them. "
"The multi-dataset functionality currently only keeps common keys."
)
for repo_id, ds in zip(self.repo_ids, self._datasets, strict=True):
extra_keys = set(ds.features).difference(intersection_features)
logging.warning(
f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
"other datasets."
)
self.disabled_features.update(extra_keys)
self.image_transforms = image_transforms
self.delta_timestamps = delta_timestamps
# TODO(rcadene, aliberts): We should not perform this aggregation for datasets
# with multiple robots of different ranges. Instead we should have one normalization
# per robot.
self.stats = aggregate_stats([dataset.meta.stats for dataset in self._datasets])
@property
def repo_id_to_index(self):
"""Return a mapping from dataset repo_id to a dataset index automatically created by this class.
This index is incorporated as a data key in the dictionary returned by `__getitem__`.
"""
return {repo_id: i for i, repo_id in enumerate(self.repo_ids)}
@property
def repo_index_to_id(self):
"""Return the inverse mapping if repo_id_to_index."""
return {v: k for k, v in self.repo_id_to_index}
@property
def fps(self) -> int:
"""Frames per second used during data collection.
NOTE: Fow now, this relies on a check in __init__ to make sure all sub-datasets have the same info.
"""
return self._datasets[0].meta.info["fps"]
@property
def video(self) -> bool:
"""Returns True if this dataset loads video frames from mp4 files.
Returns False if it only loads images from png files.
NOTE: Fow now, this relies on a check in __init__ to make sure all sub-datasets have the same info.
"""
return self._datasets[0].meta.info.get("video", False)
@property
def features(self) -> datasets.Features:
features = {}
for dataset in self._datasets:
features.update({k: v for k, v in dataset.hf_features.items() if k not in self.disabled_features})
return features
@property
def camera_keys(self) -> list[str]:
"""Keys to access image and video stream from cameras."""
keys = []
for key, feats in self.features.items():
if isinstance(feats, (datasets.Image, VideoFrame)):
keys.append(key)
return keys
@property
def video_frame_keys(self) -> list[str]:
"""Keys to access video frames that requires to be decoded into images.
Note: It is empty if the dataset contains images only,
or equal to `self.cameras` if the dataset contains videos only,
or can even be a subset of `self.cameras` in a case of a mixed image/video dataset.
"""
video_frame_keys = []
for key, feats in self.features.items():
if isinstance(feats, VideoFrame):
video_frame_keys.append(key)
return video_frame_keys
@property
def num_frames(self) -> int:
"""Number of samples/frames."""
return sum(d.num_frames for d in self._datasets)
@property
def num_episodes(self) -> int:
"""Number of episodes."""
return sum(d.num_episodes for d in self._datasets)
@property
def tolerance_s(self) -> float:
"""Tolerance in seconds used to discard loaded frames when their timestamps
are not close enough from the requested frames. It is only used when `delta_timestamps`
is provided or when loading video frames from mp4 files.
"""
# 1e-4 to account for possible numerical error
return 1 / self.fps - 1e-4
def __len__(self):
return self.num_frames
def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
if idx >= len(self):
raise IndexError(f"Index {idx} out of bounds.")
# Determine which dataset to get an item from based on the index.
start_idx = 0
dataset_idx = 0
for dataset in self._datasets:
if idx >= start_idx + dataset.num_frames:
start_idx += dataset.num_frames
dataset_idx += 1
continue
break
else:
raise AssertionError("We expect the loop to break out as long as the index is within bounds.")
item = self._datasets[dataset_idx][idx - start_idx]
item["dataset_index"] = torch.tensor(dataset_idx)
for data_key in self.disabled_features:
if data_key in item:
del item[data_key]
return item
def __repr__(self):
return (
f"{self.__class__.__name__}(\n"
f" Repository IDs: '{self.repo_ids}',\n"
f" Number of Samples: {self.num_frames},\n"
f" Number of Episodes: {self.num_episodes},\n"
f" Type: {'video (.mp4)' if self.video else 'image (.png)'},\n"
f" Recorded Frames per Second: {self.fps},\n"
f" Camera Keys: {self.camera_keys},\n"
f" Video Frame Keys: {self.video_frame_keys if self.video else 'N/A'},\n"
f" Transformations: {self.image_transforms},\n"
f")"
)
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