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Faster self.meta.episodes[...] 

switch back to set_transform instead of set_format

Add video_files_size_in_mb

pre-commit run --all-files
This commit is contained in:
Remi Cadene 2025-04-22 14:46:33 +00:00
parent 601b5fdbfe
commit d518b036d0
5 changed files with 66 additions and 64 deletions
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83
lerobot/common/datasets/lerobot_dataset.py
View File

@ -56,6 +56,7 @@ from lerobot.common.datasets.utils import (
get_safe_version, get_safe_version,
get_video_duration_in_s, get_video_duration_in_s,
get_video_size_in_mb, get_video_size_in_mb,
hf_transform_to_torch,
is_valid_version, is_valid_version,
load_episodes, load_episodes,
load_info, load_info,
@ -136,14 +137,16 @@ class LeRobotDatasetMetadata:
return packaging.version.parse(self.info["codebase_version"]) return packaging.version.parse(self.info["codebase_version"])
def get_data_file_path(self, ep_index: int) -> Path: def get_data_file_path(self, ep_index: int) -> Path:
chunk_idx = self.episodes["data/chunk_index"][ep_index] ep = self.episodes[ep_index]
file_idx = self.episodes["data/file_index"][ep_index] chunk_idx = ep["data/chunk_index"]
file_idx = ep["data/file_index"]
fpath = self.data_path.format(chunk_index=chunk_idx, file_index=file_idx) fpath = self.data_path.format(chunk_index=chunk_idx, file_index=file_idx)
return Path(fpath) return Path(fpath)
def get_video_file_path(self, ep_index: int, vid_key: str) -> Path: def get_video_file_path(self, ep_index: int, vid_key: str) -> Path:
chunk_idx = self.episodes[f"videos/{vid_key}/chunk_index"][ep_index] ep = self.episodes[ep_index]
file_idx = self.episodes[f"videos/{vid_key}/file_index"][ep_index] chunk_idx = ep[f"videos/{vid_key}/chunk_index"]
file_idx = ep[f"videos/{vid_key}/file_index"]
fpath = self.video_path.format(video_key=vid_key, chunk_index=chunk_idx, file_index=file_idx) fpath = self.video_path.format(video_key=vid_key, chunk_index=chunk_idx, file_index=file_idx)
return Path(fpath) return Path(fpath)
@ -218,9 +221,14 @@ class LeRobotDatasetMetadata:
return self.info["chunks_size"] return self.info["chunks_size"]
@property @property
def files_size_in_mb(self) -> int: def data_files_size_in_mb(self) -> int:
"""Max size of file in mega bytes.""" """Max size of data file in mega bytes."""
return self.info["files_size_in_mb"] return self.info["data_files_size_in_mb"]
@property
def video_files_size_in_mb(self) -> int:
"""Max size of video file in mega bytes."""
return self.info["video_files_size_in_mb"]
def get_task_index(self, task: str) -> int | None: def get_task_index(self, task: str) -> int | None:
""" """
@ -278,23 +286,14 @@ class LeRobotDatasetMetadata:
df["dataset_to_index"] = [len(df)] df["dataset_to_index"] = [len(df)]
else: else:
# Retrieve information from the latest parquet file # Retrieve information from the latest parquet file
latest_ep = self.episodes.with_format( latest_ep = self.episodes[-1]
columns=[ chunk_idx = latest_ep["meta/episodes/chunk_index"]
"meta/episodes/chunk_index", file_idx = latest_ep["meta/episodes/file_index"]
"meta/episodes/file_index",
"dataset_from_index",
"dataset_to_index",
]
)[-1]
chunk_idx, file_idx = (
latest_ep["meta/episodes/chunk_index"],
latest_ep["meta/episodes/file_index"],
)
latest_path = self.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx) latest_path = self.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
latest_size_in_mb = get_parquet_file_size_in_mb(latest_path) latest_size_in_mb = get_parquet_file_size_in_mb(latest_path)
if latest_size_in_mb + ep_size_in_mb >= self.files_size_in_mb: if latest_size_in_mb + ep_size_in_mb >= self.data_files_size_in_mb:
# Size limit is reached, prepare new parquet file # Size limit is reached, prepare new parquet file
chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.chunks_size) chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.chunks_size)
@ -304,7 +303,7 @@ class LeRobotDatasetMetadata:
df["dataset_from_index"] = [latest_ep["dataset_to_index"]] df["dataset_from_index"] = [latest_ep["dataset_to_index"]]
df["dataset_to_index"] = [latest_ep["dataset_to_index"] + len(df)] df["dataset_to_index"] = [latest_ep["dataset_to_index"] + len(df)]
if latest_size_in_mb + ep_size_in_mb < self.files_size_in_mb: if latest_size_in_mb + ep_size_in_mb < self.data_files_size_in_mb:
# Size limit wasnt reached, concatenate latest dataframe with new one # Size limit wasnt reached, concatenate latest dataframe with new one
latest_df = pd.read_parquet(latest_path) latest_df = pd.read_parquet(latest_path)
df = pd.concat([latest_df, df], ignore_index=True) df = pd.concat([latest_df, df], ignore_index=True)
@ -339,6 +338,7 @@ class LeRobotDatasetMetadata:
# Update info # Update info
self.info["total_episodes"] += 1 self.info["total_episodes"] += 1
self.info["total_frames"] += episode_length self.info["total_frames"] += episode_length
self.info["total_tasks"] = len(self.tasks)
self.info["splits"] = {"train": f"0:{self.info['total_episodes']}"} self.info["splits"] = {"train": f"0:{self.info['total_episodes']}"}
if len(self.video_keys) > 0: if len(self.video_keys) > 0:
self.update_video_info() self.update_video_info()
@ -674,14 +674,14 @@ class LeRobotDataset(torch.utils.data.Dataset):
def load_hf_dataset(self) -> datasets.Dataset: def load_hf_dataset(self) -> datasets.Dataset:
"""hf_dataset contains all the observations, states, actions, rewards, etc.""" """hf_dataset contains all the observations, states, actions, rewards, etc."""
hf_dataset = load_nested_dataset(self.root / "data") hf_dataset = load_nested_dataset(self.root / "data")
hf_dataset.set_format("torch") hf_dataset.set_transform(hf_transform_to_torch)
return hf_dataset return hf_dataset
def create_hf_dataset(self) -> datasets.Dataset: def create_hf_dataset(self) -> datasets.Dataset:
features = get_hf_features_from_features(self.features) features = get_hf_features_from_features(self.features)
ft_dict = {col: [] for col in features} ft_dict = {col: [] for col in features}
hf_dataset = datasets.Dataset.from_dict(ft_dict, features=features, split="train") hf_dataset = datasets.Dataset.from_dict(ft_dict, features=features, split="train")
hf_dataset.set_format("torch") hf_dataset.set_transform(hf_transform_to_torch)
return hf_dataset return hf_dataset
@property @property
@ -712,8 +712,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
return get_hf_features_from_features(self.features) return get_hf_features_from_features(self.features)
def _get_query_indices(self, idx: int, ep_idx: int) -> tuple[dict[str, list[int | bool]]]: def _get_query_indices(self, idx: int, ep_idx: int) -> tuple[dict[str, list[int | bool]]]:
ep_start = self.meta.episodes["dataset_from_index"][ep_idx] ep = self.meta.episodes[ep_idx]
ep_end = self.meta.episodes["dataset_to_index"][ep_idx] ep_start = ep["dataset_from_index"]
ep_end = ep["dataset_to_index"]
query_indices = { query_indices = {
key: [max(ep_start, min(ep_end - 1, idx + delta)) for delta in delta_idx] key: [max(ep_start, min(ep_end - 1, idx + delta)) for delta in delta_idx]
for key, delta_idx in self.delta_indices.items() for key, delta_idx in self.delta_indices.items()
@ -754,12 +755,13 @@ class LeRobotDataset(torch.utils.data.Dataset):
Segmentation Fault. This probably happens because a memory reference to the video loader is created in 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. the main process and a subprocess fails to access it.
""" """
ep = self.meta.episodes[ep_idx]
item = {} item = {}
for vid_key, query_ts in query_timestamps.items(): for vid_key, query_ts in query_timestamps.items():
# Episodes are stored sequentially on a single mp4 to reduce the number of files. # Episodes are stored sequentially on a single mp4 to reduce the number of files.
# Thus we load the start timestamp of the episode on this mp4 and # Thus we load the start timestamp of the episode on this mp4 and
# shift the query timestamp accordingly. # shift the query timestamp accordingly.
from_timestamp = self.meta.episodes[f"videos/{vid_key}/from_timestamp"][ep_idx] from_timestamp = ep[f"videos/{vid_key}/from_timestamp"]
shifted_query_ts = [from_timestamp + ts for ts in query_ts] shifted_query_ts = [from_timestamp + ts for ts in query_ts]
video_path = self.root / self.meta.get_video_file_path(ep_idx, vid_key) video_path = self.root / self.meta.get_video_file_path(ep_idx, vid_key)
@ -984,15 +986,16 @@ class LeRobotDataset(torch.utils.data.Dataset):
latest_num_frames = 0 latest_num_frames = 0
else: else:
# Retrieve information from the latest parquet file # Retrieve information from the latest parquet file
latest_ep = self.meta.episodes.with_format(columns=["data/chunk_index", "data/file_index"])[-1] latest_ep = self.meta.episodes[-1]
chunk_idx, file_idx = latest_ep["data/chunk_index"], latest_ep["data/file_index"] chunk_idx = latest_ep["data/chunk_index"]
file_idx = latest_ep["data/file_index"]
latest_path = self.root / self.meta.data_path.format(chunk_index=chunk_idx, file_index=file_idx) latest_path = self.root / self.meta.data_path.format(chunk_index=chunk_idx, file_index=file_idx)
latest_size_in_mb = get_parquet_file_size_in_mb(latest_path) latest_size_in_mb = get_parquet_file_size_in_mb(latest_path)
latest_num_frames = get_parquet_num_frames(latest_path) latest_num_frames = get_parquet_num_frames(latest_path)
# Determine if a new parquet file is needed # Determine if a new parquet file is needed
if latest_size_in_mb + ep_size_in_mb >= self.meta.files_size_in_mb: if latest_size_in_mb + ep_size_in_mb >= self.meta.data_files_size_in_mb:
# Size limit is reached, prepare new parquet file # Size limit is reached, prepare new parquet file
chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.meta.chunks_size) chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.meta.chunks_size)
latest_num_frames = 0 latest_num_frames = 0
@ -1039,13 +1042,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
shutil.move(str(ep_path), str(new_path)) shutil.move(str(ep_path), str(new_path))
else: else:
# Retrieve information from the latest video file # Retrieve information from the latest video file
latest_ep = self.meta.episodes.with_format( latest_ep = self.meta.episodes[-1]
columns=[f"videos/{video_key}/chunk_index", f"videos/{video_key}/file_index"] chunk_idx = latest_ep[f"videos/{video_key}/chunk_index"]
)[-1] file_idx = latest_ep[f"videos/{video_key}/file_index"]
chunk_idx, file_idx = (
latest_ep[f"videos/{video_key}/chunk_index"],
latest_ep[f"videos/{video_key}/file_index"],
)
latest_path = self.root / self.meta.video_path.format( latest_path = self.root / self.meta.video_path.format(
video_key=video_key, chunk_index=chunk_idx, file_index=file_idx video_key=video_key, chunk_index=chunk_idx, file_index=file_idx
@ -1053,7 +1052,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
latest_size_in_mb = get_video_size_in_mb(latest_path) latest_size_in_mb = get_video_size_in_mb(latest_path)
latest_duration_in_s = get_video_duration_in_s(latest_path) latest_duration_in_s = get_video_duration_in_s(latest_path)
if latest_size_in_mb + ep_size_in_mb >= self.meta.files_size_in_mb: if latest_size_in_mb + ep_size_in_mb >= self.meta.video_files_size_in_mb:
# Move temporary episode video to a new video file in the dataset # Move temporary episode video to a new video file in the dataset
chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.meta.chunks_size) chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.meta.chunks_size)
new_path = self.root / self.meta.video_path.format( new_path = self.root / self.meta.video_path.format(
@ -1115,16 +1114,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
if self.image_writer is not None: if self.image_writer is not None:
self.image_writer.wait_until_done() self.image_writer.wait_until_done()
# TODO(rcadene): this method is currently not used
# 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_temporary_episode_video(self, video_key: str, episode_index: int) -> dict: def _encode_temporary_episode_video(self, video_key: str, episode_index: int) -> dict:
""" """
Use ffmpeg to convert frames stored as png into mp4 videos. Use ffmpeg to convert frames stored as png into mp4 videos.

23
lerobot/common/datasets/utils.py
View File

@ -50,7 +50,8 @@ from lerobot.common.utils.utils import is_valid_numpy_dtype_string
from lerobot.configs.types import FeatureType, PolicyFeature from lerobot.configs.types import FeatureType, PolicyFeature
DEFAULT_CHUNK_SIZE = 1000 # Max number of files per chunk DEFAULT_CHUNK_SIZE = 1000 # Max number of files per chunk
DEFAULT_FILE_SIZE_IN_MB = 100.0 # Max size per file DEFAULT_DATA_FILE_SIZE_IN_MB = 100 # Max size per file
DEFAULT_VIDEO_FILE_SIZE_IN_MB = 500 # Max size per file
INFO_PATH = "meta/info.json" INFO_PATH = "meta/info.json"
STATS_PATH = "meta/stats.json" STATS_PATH = "meta/stats.json"
@ -142,6 +143,7 @@ def get_video_size_in_mb(mp4_path: Path):
def concat_video_files(paths_to_cat: list[Path], root: Path, video_key: str, chunk_idx: int, file_idx: int): def concat_video_files(paths_to_cat: list[Path], root: Path, video_key: str, chunk_idx: int, file_idx: int):
# TODO(rcadene): move to video_utils.py
# TODO(rcadene): add docstring # TODO(rcadene): add docstring
tmp_dir = Path(tempfile.mkdtemp(dir=root)) tmp_dir = Path(tempfile.mkdtemp(dir=root))
# Create a text file with the list of files to concatenate # Create a text file with the list of files to concatenate
@ -175,6 +177,7 @@ def concat_video_files(paths_to_cat: list[Path], root: Path, video_key: str, chu
def get_video_duration_in_s(mp4_file: Path): def get_video_duration_in_s(mp4_file: Path):
# TODO(rcadene): move to video_utils.py
command = [ command = [
"ffprobe", "ffprobe",
"-v", "-v",
@ -290,7 +293,7 @@ def load_stats(local_dir: Path) -> dict[str, dict[str, np.ndarray]]:
def write_hf_dataset(hf_dataset: Dataset, local_dir: Path): def write_hf_dataset(hf_dataset: Dataset, local_dir: Path):
if get_hf_dataset_size_in_mb(hf_dataset) > DEFAULT_FILE_SIZE_IN_MB: if get_hf_dataset_size_in_mb(hf_dataset) > DEFAULT_DATA_FILE_SIZE_IN_MB:
raise NotImplementedError("Contact a maintainer.") raise NotImplementedError("Contact a maintainer.")
path = local_dir / DEFAULT_DATA_PATH.format(chunk_index=0, file_index=0) path = local_dir / DEFAULT_DATA_PATH.format(chunk_index=0, file_index=0)
@ -310,7 +313,7 @@ def load_tasks(local_dir: Path):
def write_episodes(episodes: Dataset, local_dir: Path): def write_episodes(episodes: Dataset, local_dir: Path):
if get_hf_dataset_size_in_mb(episodes) > DEFAULT_FILE_SIZE_IN_MB: if get_hf_dataset_size_in_mb(episodes) > DEFAULT_DATA_FILE_SIZE_IN_MB:
raise NotImplementedError("Contact a maintainer.") raise NotImplementedError("Contact a maintainer.")
fpath = local_dir / DEFAULT_EPISODES_PATH.format(chunk_index=0, file_index=0) fpath = local_dir / DEFAULT_EPISODES_PATH.format(chunk_index=0, file_index=0)
@ -318,9 +321,13 @@ def write_episodes(episodes: Dataset, local_dir: Path):
episodes.to_parquet(fpath) episodes.to_parquet(fpath)
def load_episodes(local_dir: Path): def load_episodes(local_dir: Path) -> datasets.Dataset:
hf_dataset = load_nested_dataset(local_dir / EPISODES_DIR) episodes = load_nested_dataset(local_dir / EPISODES_DIR)
return hf_dataset # Select episode features/columns containing references to episode data and videos
# (e.g. tasks, dataset_from_index, dataset_to_index, data/chunk_index, data/file_index, etc.)
# This is to speedup access to these data, instead of having to load episode stats.
episodes = episodes.select_columns([key for key in episodes.features if not key.startswith("stats/")])
return episodes
def backward_compatible_episodes_stats( def backward_compatible_episodes_stats(
@ -528,9 +535,9 @@ def create_empty_dataset_info(
"total_episodes": 0, "total_episodes": 0,
"total_frames": 0, "total_frames": 0,
"total_tasks": 0, "total_tasks": 0,
"total_videos": 0,
"chunks_size": DEFAULT_CHUNK_SIZE, "chunks_size": DEFAULT_CHUNK_SIZE,
"files_size_in_mb": DEFAULT_FILE_SIZE_IN_MB, "data_files_size_in_mb": DEFAULT_DATA_FILE_SIZE_IN_MB,
"video_files_size_in_mb": DEFAULT_VIDEO_FILE_SIZE_IN_MB,
"fps": fps, "fps": fps,
"splits": {}, "splits": {},
"data_path": DEFAULT_DATA_PATH, "data_path": DEFAULT_DATA_PATH,

11
lerobot/common/datasets/v30/convert_dataset_v21_to_v30.py
View File

@ -34,8 +34,9 @@ from lerobot.common.datasets.compute_stats import aggregate_stats
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
from lerobot.common.datasets.utils import ( from lerobot.common.datasets.utils import (
DEFAULT_CHUNK_SIZE, DEFAULT_CHUNK_SIZE,
DEFAULT_DATA_FILE_SIZE_IN_MB,
DEFAULT_DATA_PATH, DEFAULT_DATA_PATH,
DEFAULT_FILE_SIZE_IN_MB, DEFAULT_VIDEO_FILE_SIZE_IN_MB,
DEFAULT_VIDEO_PATH, DEFAULT_VIDEO_PATH,
cast_stats_to_numpy, cast_stats_to_numpy,
concat_video_files, concat_video_files,
@ -174,7 +175,7 @@ def convert_data(root, new_root):
episodes_metadata.append(ep_metadata) episodes_metadata.append(ep_metadata)
ep_idx += 1 ep_idx += 1
if size_in_mb < DEFAULT_FILE_SIZE_IN_MB: if size_in_mb < DEFAULT_DATA_FILE_SIZE_IN_MB:
paths_to_cat.append(ep_path) paths_to_cat.append(ep_path)
continue continue
@ -263,7 +264,7 @@ def convert_videos_of_camera(root: Path, new_root: Path, video_key):
episodes_metadata.append(ep_metadata) episodes_metadata.append(ep_metadata)
ep_idx += 1 ep_idx += 1
if size_in_mb < DEFAULT_FILE_SIZE_IN_MB: if size_in_mb < DEFAULT_VIDEO_FILE_SIZE_IN_MB:
paths_to_cat.append(ep_path) paths_to_cat.append(ep_path)
continue continue
@ -337,8 +338,8 @@ def convert_info(root, new_root):
info["codebase_version"] = "v3.0" info["codebase_version"] = "v3.0"
del info["total_chunks"] del info["total_chunks"]
del info["total_videos"] del info["total_videos"]
info["files_size_in_mb"] = DEFAULT_FILE_SIZE_IN_MB info["data_files_size_in_mb"] = DEFAULT_DATA_FILE_SIZE_IN_MB
# TODO(rcadene): chunk- or chunk_ or file- or file_ info["video_files_size_in_mb"] = DEFAULT_VIDEO_FILE_SIZE_IN_MB
info["data_path"] = DEFAULT_DATA_PATH info["data_path"] = DEFAULT_DATA_PATH
info["video_path"] = DEFAULT_VIDEO_PATH info["video_path"] = DEFAULT_VIDEO_PATH
info["fps"] = float(info["fps"]) info["fps"] = float(info["fps"])

1
lerobot/common/datasets/video_utils.py
View File

@ -155,6 +155,7 @@ def decode_video_frames_torchvision(
) )
# get closest frames to the query timestamps # get closest frames to the query timestamps
# TODO(rcadene): remove torch.stack
closest_frames = torch.stack([loaded_frames[idx] for idx in argmin_]) closest_frames = torch.stack([loaded_frames[idx] for idx in argmin_])
closest_ts = loaded_ts[argmin_] closest_ts = loaded_ts[argmin_]

12
tests/fixtures/dataset_factories.py vendored
View File

@ -28,12 +28,14 @@ from datasets import Dataset
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset, LeRobotDatasetMetadata from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset, LeRobotDatasetMetadata
from lerobot.common.datasets.utils import ( from lerobot.common.datasets.utils import (
DEFAULT_CHUNK_SIZE, DEFAULT_CHUNK_SIZE,
DEFAULT_DATA_FILE_SIZE_IN_MB,
DEFAULT_DATA_PATH, DEFAULT_DATA_PATH,
DEFAULT_FEATURES, DEFAULT_FEATURES,
DEFAULT_FILE_SIZE_IN_MB, DEFAULT_VIDEO_FILE_SIZE_IN_MB,
DEFAULT_VIDEO_PATH, DEFAULT_VIDEO_PATH,
flatten_dict, flatten_dict,
get_hf_features_from_features, get_hf_features_from_features,
hf_transform_to_torch,
) )
from tests.fixtures.constants import ( from tests.fixtures.constants import (
DEFAULT_FPS, DEFAULT_FPS,
@ -121,7 +123,8 @@ def info_factory(features_factory):
total_tasks: int = 0, total_tasks: int = 0,
total_videos: int = 0, total_videos: int = 0,
chunks_size: int = DEFAULT_CHUNK_SIZE, chunks_size: int = DEFAULT_CHUNK_SIZE,
files_size_in_mb: float = DEFAULT_FILE_SIZE_IN_MB, data_files_size_in_mb: float = DEFAULT_DATA_FILE_SIZE_IN_MB,
video_files_size_in_mb: float = DEFAULT_VIDEO_FILE_SIZE_IN_MB,
data_path: str = DEFAULT_DATA_PATH, data_path: str = DEFAULT_DATA_PATH,
video_path: str = DEFAULT_VIDEO_PATH, video_path: str = DEFAULT_VIDEO_PATH,
motor_features: dict = DUMMY_MOTOR_FEATURES, motor_features: dict = DUMMY_MOTOR_FEATURES,
@ -137,7 +140,8 @@ def info_factory(features_factory):
"total_tasks": total_tasks, "total_tasks": total_tasks,
"total_videos": total_videos, "total_videos": total_videos,
"chunks_size": chunks_size, "chunks_size": chunks_size,
"files_size_in_mb": files_size_in_mb, "data_files_size_in_mb": data_files_size_in_mb,
"video_files_size_in_mb": video_files_size_in_mb,
"fps": fps, "fps": fps,
"splits": {}, "splits": {},
"data_path": data_path, "data_path": data_path,
@ -352,7 +356,7 @@ def hf_dataset_factory(features_factory, tasks_factory, episodes_factory, img_ar
}, },
features=hf_features, features=hf_features,
) )
dataset.set_format("torch") dataset.set_transform(hf_transform_to_torch)
return dataset return dataset
return _create_hf_dataset return _create_hf_dataset