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Dataset v2.0 (#461)

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Co-authored-by: Remi <remi.cadene@huggingface.co>
This commit is contained in:
Simon Alibert
2024-11-29 19:04:00 +01:00
committed by GitHub
parent 96c7052777
commit 32eb0cec8f
71 changed files with 6115 additions and 2235 deletions
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27
lerobot/common/datasets/card_template.md Normal file
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@@ -0,0 +1,27 @@
---
# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{{ card_data }}
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
{{ dataset_description | default("", true) }}
- **Homepage:** {{ url | default("[More Information Needed]", true)}}
- **Paper:** {{ paper | default("[More Information Needed]", true)}}
- **License:** {{ license | default("[More Information Needed]", true)}}
## Dataset Structure
{{ dataset_structure | default("[More Information Needed]", true)}}
## Citation
**BibTeX:**
```bibtex
{{ citation_bibtex | default("[More Information Needed]", true)}}
```

39
lerobot/common/datasets/compute_stats.py
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@@ -19,9 +19,6 @@ from math import ceil
import einops
import torch
import tqdm
from datasets import Image
from lerobot.common.datasets.video_utils import VideoFrame
def get_stats_einops_patterns(dataset, num_workers=0):
@@ -39,15 +36,13 @@ def get_stats_einops_patterns(dataset, num_workers=0):
batch = next(iter(dataloader))
stats_patterns = {}
for key, feats_type in dataset.features.items():
# NOTE: skip language_instruction embedding in stats computation
if key == "language_instruction":
continue
for key in dataset.features:
# sanity check that tensors are not float64
assert batch[key].dtype != torch.float64
if isinstance(feats_type, (VideoFrame, Image)):
# if isinstance(feats_type, (VideoFrame, Image)):
if key in dataset.meta.camera_keys:
# sanity check that images are channel first
_, c, h, w = batch[key].shape
assert c < h and c < w, f"expect channel first images, but instead {batch[key].shape}"
@@ -63,7 +58,7 @@ def get_stats_einops_patterns(dataset, num_workers=0):
elif batch[key].ndim == 1:
stats_patterns[key] = "b -> 1"
else:
raise ValueError(f"{key}, {feats_type}, {batch[key].shape}")
raise ValueError(f"{key}, {batch[key].shape}")
return stats_patterns
@@ -175,39 +170,45 @@ def aggregate_stats(ls_datasets) -> dict[str, torch.Tensor]:
"""
data_keys = set()
for dataset in ls_datasets:
data_keys.update(dataset.stats.keys())
data_keys.update(dataset.meta.stats.keys())
stats = {k: {} for k in data_keys}
for data_key in data_keys:
for stat_key in ["min", "max"]:
# compute `max(dataset_0["max"], dataset_1["max"], ...)`
stats[data_key][stat_key] = einops.reduce(
torch.stack([d.stats[data_key][stat_key] for d in ls_datasets if data_key in d.stats], dim=0),
torch.stack(
[ds.meta.stats[data_key][stat_key] for ds in ls_datasets if data_key in ds.meta.stats],
dim=0,
),
"n ... -> ...",
stat_key,
)
total_samples = sum(d.num_samples for d in ls_datasets if data_key in d.stats)
total_samples = sum(d.num_frames for d in ls_datasets if data_key in d.meta.stats)
# Compute the "sum" statistic by multiplying each mean by the number of samples in the respective
# dataset, then divide by total_samples to get the overall "mean".
# NOTE: the brackets around (d.num_samples / total_samples) are needed tor minimize the risk of
# NOTE: the brackets around (d.num_frames / total_samples) are needed tor minimize the risk of
# numerical overflow!
stats[data_key]["mean"] = sum(
d.stats[data_key]["mean"] * (d.num_samples / total_samples)
d.meta.stats[data_key]["mean"] * (d.num_frames / total_samples)
for d in ls_datasets
if data_key in d.stats
if data_key in d.meta.stats
)
# The derivation for standard deviation is a little more involved but is much in the same spirit as
# the computation of the mean.
# Given two sets of data where the statistics are known:
# σ_combined = sqrt[ (n1 * (σ1^2 + d1^2) + n2 * (σ2^2 + d2^2)) / (n1 + n2) ]
# where d1 = μ1 - μ_combined, d2 = μ2 - μ_combined
# NOTE: the brackets around (d.num_samples / total_samples) are needed tor minimize the risk of
# NOTE: the brackets around (d.num_frames / total_samples) are needed tor minimize the risk of
# numerical overflow!
stats[data_key]["std"] = torch.sqrt(
sum(
(d.stats[data_key]["std"] ** 2 + (d.stats[data_key]["mean"] - stats[data_key]["mean"]) ** 2)
* (d.num_samples / total_samples)
(
d.meta.stats[data_key]["std"] ** 2
+ (d.meta.stats[data_key]["mean"] - stats[data_key]["mean"]) ** 2
)
* (d.num_frames / total_samples)
for d in ls_datasets
if data_key in d.stats
if data_key in d.meta.stats
)
)
return stats

5
lerobot/common/datasets/factory.py
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@@ -91,9 +91,9 @@ def make_dataset(cfg, split: str = "train") -> LeRobotDataset | MultiLeRobotData
)
if isinstance(cfg.dataset_repo_id, str):
# TODO (aliberts): add 'episodes' arg from config after removing hydra
dataset = LeRobotDataset(
cfg.dataset_repo_id,
split=split,
delta_timestamps=cfg.training.get("delta_timestamps"),
image_transforms=image_transforms,
video_backend=cfg.video_backend,
@@ -101,7 +101,6 @@ def make_dataset(cfg, split: str = "train") -> LeRobotDataset | MultiLeRobotData
else:
dataset = MultiLeRobotDataset(
cfg.dataset_repo_id,
split=split,
delta_timestamps=cfg.training.get("delta_timestamps"),
image_transforms=image_transforms,
video_backend=cfg.video_backend,
@@ -112,6 +111,6 @@ def make_dataset(cfg, split: str = "train") -> LeRobotDataset | MultiLeRobotData
for stats_type, listconfig in stats_dict.items():
# example of stats_type: min, max, mean, std
stats = OmegaConf.to_container(listconfig, resolve=True)
dataset.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
dataset.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
return dataset

160
lerobot/common/datasets/image_writer.py Normal file
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@@ -0,0 +1,160 @@
#!/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 multiprocessing
import queue
import threading
from pathlib import Path
import numpy as np
import PIL.Image
import torch
def safe_stop_image_writer(func):
def wrapper(*args, **kwargs):
try:
return func(*args, **kwargs)
except Exception as e:
dataset = kwargs.get("dataset", None)
image_writer = getattr(dataset, "image_writer", None) if dataset else None
if image_writer is not None:
print("Waiting for image writer to terminate...")
image_writer.stop()
raise e
return wrapper
def image_array_to_image(image_array: np.ndarray) -> PIL.Image.Image:
# TODO(aliberts): handle 1 channel and 4 for depth images
if image_array.ndim == 3 and image_array.shape[0] in [1, 3]:
# Transpose from pytorch convention (C, H, W) to (H, W, C)
image_array = image_array.transpose(1, 2, 0)
if image_array.dtype != np.uint8:
# Assume the image is in [0, 1] range for floating-point data
image_array = np.clip(image_array, 0, 1)
image_array = (image_array * 255).astype(np.uint8)
return PIL.Image.fromarray(image_array)
def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path):
try:
if isinstance(image, np.ndarray):
img = image_array_to_image(image)
elif isinstance(image, PIL.Image.Image):
img = image
else:
raise TypeError(f"Unsupported image type: {type(image)}")
img.save(fpath)
except Exception as e:
print(f"Error writing image {fpath}: {e}")
def worker_thread_loop(queue: queue.Queue):
while True:
item = queue.get()
if item is None:
queue.task_done()
break
image_array, fpath = item
write_image(image_array, fpath)
queue.task_done()
def worker_process(queue: queue.Queue, num_threads: int):
threads = []
for _ in range(num_threads):
t = threading.Thread(target=worker_thread_loop, args=(queue,))
t.daemon = True
t.start()
threads.append(t)
for t in threads:
t.join()
class AsyncImageWriter:
"""
This class abstract away the initialisation of processes or/and threads to
save images on disk asynchrounously, which is critical to control a robot and record data
at a high frame rate.
When `num_processes=0`, it creates a threads pool of size `num_threads`.
When `num_processes>0`, it creates processes pool of size `num_processes`, where each subprocess starts
their own threads pool of size `num_threads`.
The optimal number of processes and threads depends on your computer capabilities.
We advise to use 4 threads per camera with 0 processes. If the fps is not stable, try to increase or lower
the number of threads. If it is still not stable, try to use 1 subprocess, or more.
"""
def __init__(self, num_processes: int = 0, num_threads: int = 1):
self.num_processes = num_processes
self.num_threads = num_threads
self.queue = None
self.threads = []
self.processes = []
self._stopped = False
if num_threads <= 0 and num_processes <= 0:
raise ValueError("Number of threads and processes must be greater than zero.")
if self.num_processes == 0:
# Use threading
self.queue = queue.Queue()
for _ in range(self.num_threads):
t = threading.Thread(target=worker_thread_loop, args=(self.queue,))
t.daemon = True
t.start()
self.threads.append(t)
else:
# Use multiprocessing
self.queue = multiprocessing.JoinableQueue()
for _ in range(self.num_processes):
p = multiprocessing.Process(target=worker_process, args=(self.queue, self.num_threads))
p.daemon = True
p.start()
self.processes.append(p)
def save_image(self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path):
if isinstance(image, torch.Tensor):
# Convert tensor to numpy array to minimize main process time
image = image.cpu().numpy()
self.queue.put((image, fpath))
def wait_until_done(self):
self.queue.join()
def stop(self):
if self._stopped:
return
if self.num_processes == 0:
for _ in self.threads:
self.queue.put(None)
for t in self.threads:
t.join()
else:
num_nones = self.num_processes * self.num_threads
for _ in range(num_nones):
self.queue.put(None)
for p in self.processes:
p.join()
if p.is_alive():
p.terminate()
self.queue.close()
self.queue.join_thread()
self._stopped = True

1078
lerobot/common/datasets/lerobot_dataset.py
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6
lerobot/common/datasets/online_buffer.py
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@@ -187,7 +187,7 @@ class OnlineBuffer(torch.utils.data.Dataset):
assert data[OnlineBuffer.INDEX_KEY][0].item() == 0
# Shift the incoming indices if necessary.
if self.num_samples > 0:
if self.num_frames > 0:
last_episode_index = self._data[OnlineBuffer.EPISODE_INDEX_KEY][next_index - 1]
last_data_index = self._data[OnlineBuffer.INDEX_KEY][next_index - 1]
data[OnlineBuffer.EPISODE_INDEX_KEY] += last_episode_index + 1
@@ -227,11 +227,11 @@ class OnlineBuffer(torch.utils.data.Dataset):
)
@property
def num_samples(self) -> int:
def num_frames(self) -> int:
return np.count_nonzero(self._data[OnlineBuffer.OCCUPANCY_MASK_KEY])
def __len__(self):
return self.num_samples
return self.num_frames
def _item_to_tensors(self, item: dict) -> dict:
item_ = {}

468
lerobot/common/datasets/populate_dataset.py
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@@ -1,468 +0,0 @@
"""Functions to create an empty dataset, and populate it with frames."""
# TODO(rcadene, aliberts): to adapt as class methods of next version of LeRobotDataset
import concurrent
import json
import logging
import multiprocessing
import shutil
from pathlib import Path
import torch
import tqdm
from PIL import Image
from lerobot.common.datasets.compute_stats import compute_stats
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
from lerobot.common.datasets.push_dataset_to_hub.aloha_hdf5_format import to_hf_dataset
from lerobot.common.datasets.push_dataset_to_hub.utils import concatenate_episodes, get_default_encoding
from lerobot.common.datasets.utils import calculate_episode_data_index, create_branch
from lerobot.common.datasets.video_utils import encode_video_frames
from lerobot.common.utils.utils import log_say
from lerobot.scripts.push_dataset_to_hub import (
push_dataset_card_to_hub,
push_meta_data_to_hub,
push_videos_to_hub,
save_meta_data,
)
########################################################################################
# Asynchrounous saving of images on disk
########################################################################################
def safe_stop_image_writer(func):
# TODO(aliberts): Allow to pass custom exceptions
# (e.g. ThreadServiceExit, KeyboardInterrupt, SystemExit, UnpluggedError, DynamixelCommError)
def wrapper(*args, **kwargs):
try:
return func(*args, **kwargs)
except Exception as e:
image_writer = kwargs.get("dataset", {}).get("image_writer")
if image_writer is not None:
print("Waiting for image writer to terminate...")
stop_image_writer(image_writer, timeout=20)
raise e
return wrapper
def save_image(img_tensor, key, frame_index, episode_index, videos_dir: str):
img = Image.fromarray(img_tensor.numpy())
path = Path(videos_dir) / f"{key}_episode_{episode_index:06d}" / f"frame_{frame_index:06d}.png"
path.parent.mkdir(parents=True, exist_ok=True)
img.save(str(path), quality=100)
def loop_to_save_images_in_threads(image_queue, num_threads):
if num_threads < 1:
raise NotImplementedError(f"Only `num_threads>=1` is supported for now, but {num_threads=} given.")
with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
futures = []
while True:
# Blocks until a frame is available
frame_data = image_queue.get()
# As usually done, exit loop when receiving None to stop the worker
if frame_data is None:
break
image, key, frame_index, episode_index, videos_dir = frame_data
futures.append(executor.submit(save_image, image, key, frame_index, episode_index, videos_dir))
# Before exiting function, wait for all threads to complete
with tqdm.tqdm(total=len(futures), desc="Writing images") as progress_bar:
concurrent.futures.wait(futures)
progress_bar.update(len(futures))
def start_image_writer_processes(image_queue, num_processes, num_threads_per_process):
if num_processes < 1:
raise ValueError(f"Only `num_processes>=1` is supported, but {num_processes=} given.")
if num_threads_per_process < 1:
raise NotImplementedError(
"Only `num_threads_per_process>=1` is supported for now, but {num_threads_per_process=} given."
)
processes = []
for _ in range(num_processes):
process = multiprocessing.Process(
target=loop_to_save_images_in_threads,
args=(image_queue, num_threads_per_process),
)
process.start()
processes.append(process)
return processes
def stop_processes(processes, queue, timeout):
# Send None to each process to signal them to stop
for _ in processes:
queue.put(None)
# Wait maximum 20 seconds for all processes to terminate
for process in processes:
process.join(timeout=timeout)
# If not terminated after 20 seconds, force termination
if process.is_alive():
process.terminate()
# Close the queue, no more items can be put in the queue
queue.close()
# Ensure all background queue threads have finished
queue.join_thread()
def start_image_writer(num_processes, num_threads):
"""This function abstract away the initialisation of processes or/and threads to
save images on disk asynchrounously, which is critical to control a robot and record data
at a high frame rate.
When `num_processes=0`, it returns a dictionary containing a threads pool of size `num_threads`.
When `num_processes>0`, it returns a dictionary containing a processes pool of size `num_processes`,
where each subprocess starts their own threads pool of size `num_threads`.
The optimal number of processes and threads depends on your computer capabilities.
We advise to use 4 threads per camera with 0 processes. If the fps is not stable, try to increase or lower
the number of threads. If it is still not stable, try to use 1 subprocess, or more.
"""
image_writer = {}
if num_processes == 0:
futures = []
threads_pool = concurrent.futures.ThreadPoolExecutor(max_workers=num_threads)
image_writer["threads_pool"], image_writer["futures"] = threads_pool, futures
else:
# TODO(rcadene): When using num_processes>1, `multiprocessing.Manager().Queue()`
# might be better than `multiprocessing.Queue()`. Source: https://www.geeksforgeeks.org/python-multiprocessing-queue-vs-multiprocessing-manager-queue
image_queue = multiprocessing.Queue()
processes_pool = start_image_writer_processes(
image_queue, num_processes=num_processes, num_threads_per_process=num_threads
)
image_writer["processes_pool"], image_writer["image_queue"] = processes_pool, image_queue
return image_writer
def async_save_image(image_writer, image, key, frame_index, episode_index, videos_dir):
"""This function abstract away the saving of an image on disk asynchrounously. It uses a dictionary
called image writer which contains either a pool of processes or a pool of threads.
"""
if "threads_pool" in image_writer:
threads_pool, futures = image_writer["threads_pool"], image_writer["futures"]
futures.append(threads_pool.submit(save_image, image, key, frame_index, episode_index, videos_dir))
else:
image_queue = image_writer["image_queue"]
image_queue.put((image, key, frame_index, episode_index, videos_dir))
def stop_image_writer(image_writer, timeout):
if "threads_pool" in image_writer:
futures = image_writer["futures"]
# Before exiting function, wait for all threads to complete
with tqdm.tqdm(total=len(futures), desc="Writing images") as progress_bar:
concurrent.futures.wait(futures, timeout=timeout)
progress_bar.update(len(futures))
else:
processes_pool, image_queue = image_writer["processes_pool"], image_writer["image_queue"]
stop_processes(processes_pool, image_queue, timeout=timeout)
########################################################################################
# Functions to initialize, resume and populate a dataset
########################################################################################
def init_dataset(
repo_id,
root,
force_override,
fps,
video,
write_images,
num_image_writer_processes,
num_image_writer_threads,
):
local_dir = Path(root) / repo_id
if local_dir.exists() and force_override:
shutil.rmtree(local_dir)
episodes_dir = local_dir / "episodes"
episodes_dir.mkdir(parents=True, exist_ok=True)
videos_dir = local_dir / "videos"
videos_dir.mkdir(parents=True, exist_ok=True)
# Logic to resume data recording
rec_info_path = episodes_dir / "data_recording_info.json"
if rec_info_path.exists():
with open(rec_info_path) as f:
rec_info = json.load(f)
num_episodes = rec_info["last_episode_index"] + 1
else:
num_episodes = 0
dataset = {
"repo_id": repo_id,
"local_dir": local_dir,
"videos_dir": videos_dir,
"episodes_dir": episodes_dir,
"fps": fps,
"video": video,
"rec_info_path": rec_info_path,
"num_episodes": num_episodes,
}
if write_images:
# Initialize processes or/and threads dedicated to save images on disk asynchronously,
# which is critical to control a robot and record data at a high frame rate.
image_writer = start_image_writer(
num_processes=num_image_writer_processes,
num_threads=num_image_writer_threads,
)
dataset["image_writer"] = image_writer
return dataset
def add_frame(dataset, observation, action):
if "current_episode" not in dataset:
# initialize episode dictionary
ep_dict = {}
for key in observation:
if key not in ep_dict:
ep_dict[key] = []
for key in action:
if key not in ep_dict:
ep_dict[key] = []
ep_dict["episode_index"] = []
ep_dict["frame_index"] = []
ep_dict["timestamp"] = []
ep_dict["next.done"] = []
dataset["current_episode"] = ep_dict
dataset["current_frame_index"] = 0
ep_dict = dataset["current_episode"]
episode_index = dataset["num_episodes"]
frame_index = dataset["current_frame_index"]
videos_dir = dataset["videos_dir"]
video = dataset["video"]
fps = dataset["fps"]
ep_dict["episode_index"].append(episode_index)
ep_dict["frame_index"].append(frame_index)
ep_dict["timestamp"].append(frame_index / fps)
ep_dict["next.done"].append(False)
img_keys = [key for key in observation if "image" in key]
non_img_keys = [key for key in observation if "image" not in key]
# Save all observed modalities except images
for key in non_img_keys:
ep_dict[key].append(observation[key])
# Save actions
for key in action:
ep_dict[key].append(action[key])
if "image_writer" not in dataset:
dataset["current_frame_index"] += 1
return
# Save images
image_writer = dataset["image_writer"]
for key in img_keys:
imgs_dir = videos_dir / f"{key}_episode_{episode_index:06d}"
async_save_image(
image_writer,
image=observation[key],
key=key,
frame_index=frame_index,
episode_index=episode_index,
videos_dir=str(videos_dir),
)
if video:
fname = f"{key}_episode_{episode_index:06d}.mp4"
frame_info = {"path": f"videos/{fname}", "timestamp": frame_index / fps}
else:
frame_info = str(imgs_dir / f"frame_{frame_index:06d}.png")
ep_dict[key].append(frame_info)
dataset["current_frame_index"] += 1
def delete_current_episode(dataset):
del dataset["current_episode"]
del dataset["current_frame_index"]
# delete temporary images
episode_index = dataset["num_episodes"]
videos_dir = dataset["videos_dir"]
for tmp_imgs_dir in videos_dir.glob(f"*_episode_{episode_index:06d}"):
shutil.rmtree(tmp_imgs_dir)
def save_current_episode(dataset):
episode_index = dataset["num_episodes"]
ep_dict = dataset["current_episode"]
episodes_dir = dataset["episodes_dir"]
rec_info_path = dataset["rec_info_path"]
ep_dict["next.done"][-1] = True
for key in ep_dict:
if "observation" in key and "image" not in key:
ep_dict[key] = torch.stack(ep_dict[key])
ep_dict["action"] = torch.stack(ep_dict["action"])
ep_dict["episode_index"] = torch.tensor(ep_dict["episode_index"])
ep_dict["frame_index"] = torch.tensor(ep_dict["frame_index"])
ep_dict["timestamp"] = torch.tensor(ep_dict["timestamp"])
ep_dict["next.done"] = torch.tensor(ep_dict["next.done"])
ep_path = episodes_dir / f"episode_{episode_index}.pth"
torch.save(ep_dict, ep_path)
rec_info = {
"last_episode_index": episode_index,
}
with open(rec_info_path, "w") as f:
json.dump(rec_info, f)
# force re-initialization of episode dictionnary during add_frame
del dataset["current_episode"]
dataset["num_episodes"] += 1
def encode_videos(dataset, image_keys, play_sounds):
log_say("Encoding videos", play_sounds)
num_episodes = dataset["num_episodes"]
videos_dir = dataset["videos_dir"]
local_dir = dataset["local_dir"]
fps = dataset["fps"]
# Use ffmpeg to convert frames stored as png into mp4 videos
for episode_index in tqdm.tqdm(range(num_episodes)):
for key in image_keys:
# key = f"observation.images.{name}"
tmp_imgs_dir = videos_dir / f"{key}_episode_{episode_index:06d}"
fname = f"{key}_episode_{episode_index:06d}.mp4"
video_path = local_dir / "videos" / fname
if video_path.exists():
# Skip if video is already encoded. Could be the case when resuming data recording.
continue
# note: `encode_video_frames` is a blocking call. Making it asynchronous shouldn't speedup encoding,
# since video encoding with ffmpeg is already using multithreading.
encode_video_frames(tmp_imgs_dir, video_path, fps, overwrite=True)
shutil.rmtree(tmp_imgs_dir)
def from_dataset_to_lerobot_dataset(dataset, play_sounds):
log_say("Consolidate episodes", play_sounds)
num_episodes = dataset["num_episodes"]
episodes_dir = dataset["episodes_dir"]
videos_dir = dataset["videos_dir"]
video = dataset["video"]
fps = dataset["fps"]
repo_id = dataset["repo_id"]
ep_dicts = []
for episode_index in tqdm.tqdm(range(num_episodes)):
ep_path = episodes_dir / f"episode_{episode_index}.pth"
ep_dict = torch.load(ep_path)
ep_dicts.append(ep_dict)
data_dict = concatenate_episodes(ep_dicts)
if video:
image_keys = [key for key in data_dict if "image" in key]
encode_videos(dataset, image_keys, play_sounds)
hf_dataset = to_hf_dataset(data_dict, video)
episode_data_index = calculate_episode_data_index(hf_dataset)
info = {
"codebase_version": CODEBASE_VERSION,
"fps": fps,
"video": video,
}
if video:
info["encoding"] = get_default_encoding()
lerobot_dataset = LeRobotDataset.from_preloaded(
repo_id=repo_id,
hf_dataset=hf_dataset,
episode_data_index=episode_data_index,
info=info,
videos_dir=videos_dir,
)
return lerobot_dataset
def save_lerobot_dataset_on_disk(lerobot_dataset):
hf_dataset = lerobot_dataset.hf_dataset
info = lerobot_dataset.info
stats = lerobot_dataset.stats
episode_data_index = lerobot_dataset.episode_data_index
local_dir = lerobot_dataset.videos_dir.parent
meta_data_dir = local_dir / "meta_data"
hf_dataset = hf_dataset.with_format(None) # to remove transforms that cant be saved
hf_dataset.save_to_disk(str(local_dir / "train"))
save_meta_data(info, stats, episode_data_index, meta_data_dir)
def push_lerobot_dataset_to_hub(lerobot_dataset, tags):
hf_dataset = lerobot_dataset.hf_dataset
local_dir = lerobot_dataset.videos_dir.parent
videos_dir = lerobot_dataset.videos_dir
repo_id = lerobot_dataset.repo_id
video = lerobot_dataset.video
meta_data_dir = local_dir / "meta_data"
if not (local_dir / "train").exists():
raise ValueError(
"You need to run `save_lerobot_dataset_on_disk(lerobot_dataset)` before pushing to the hub."
)
hf_dataset.push_to_hub(repo_id, revision="main")
push_meta_data_to_hub(repo_id, meta_data_dir, revision="main")
push_dataset_card_to_hub(repo_id, revision="main", tags=tags)
if video:
push_videos_to_hub(repo_id, videos_dir, revision="main")
create_branch(repo_id, repo_type="dataset", branch=CODEBASE_VERSION)
def create_lerobot_dataset(dataset, run_compute_stats, push_to_hub, tags, play_sounds):
if "image_writer" in dataset:
logging.info("Waiting for image writer to terminate...")
image_writer = dataset["image_writer"]
stop_image_writer(image_writer, timeout=20)
lerobot_dataset = from_dataset_to_lerobot_dataset(dataset, play_sounds)
if run_compute_stats:
log_say("Computing dataset statistics", play_sounds)
lerobot_dataset.stats = compute_stats(lerobot_dataset)
else:
logging.info("Skipping computation of the dataset statistics")
lerobot_dataset.stats = {}
save_lerobot_dataset_on_disk(lerobot_dataset)
if push_to_hub:
push_lerobot_dataset_to_hub(lerobot_dataset, tags)
return lerobot_dataset

2
lerobot/common/datasets/push_dataset_to_hub/aloha_hdf5_format.py
View File

@@ -30,12 +30,12 @@ from PIL import Image as PILImage
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
from lerobot.common.datasets.push_dataset_to_hub.utils import (
calculate_episode_data_index,
concatenate_episodes,
get_default_encoding,
save_images_concurrently,
)
from lerobot.common.datasets.utils import (
calculate_episode_data_index,
hf_transform_to_torch,
)
from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames

7
lerobot/common/datasets/push_dataset_to_hub/cam_png_format.py
View File

@@ -24,8 +24,11 @@ from datasets import Dataset, Features, Image, Value
from PIL import Image as PILImage
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
from lerobot.common.datasets.push_dataset_to_hub.utils import concatenate_episodes
from lerobot.common.datasets.utils import calculate_episode_data_index, hf_transform_to_torch
from lerobot.common.datasets.push_dataset_to_hub.utils import (
calculate_episode_data_index,
concatenate_episodes,
)
from lerobot.common.datasets.utils import hf_transform_to_torch
from lerobot.common.datasets.video_utils import VideoFrame

2
lerobot/common/datasets/push_dataset_to_hub/dora_parquet_format.py
View File

@@ -26,8 +26,8 @@ import torch
from datasets import Dataset, Features, Image, Sequence, Value
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
from lerobot.common.datasets.push_dataset_to_hub.utils import calculate_episode_data_index
from lerobot.common.datasets.utils import (
calculate_episode_data_index,
hf_transform_to_torch,
)
from lerobot.common.datasets.video_utils import VideoFrame

2
lerobot/common/datasets/push_dataset_to_hub/openx_rlds_format.py
View File

@@ -42,12 +42,12 @@ from PIL import Image as PILImage
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
from lerobot.common.datasets.push_dataset_to_hub.openx.transforms import OPENX_STANDARDIZATION_TRANSFORMS
from lerobot.common.datasets.push_dataset_to_hub.utils import (
calculate_episode_data_index,
concatenate_episodes,
get_default_encoding,
save_images_concurrently,
)
from lerobot.common.datasets.utils import (
calculate_episode_data_index,
hf_transform_to_torch,
)
from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames

2
lerobot/common/datasets/push_dataset_to_hub/pusht_zarr_format.py
View File

@@ -27,12 +27,12 @@ from PIL import Image as PILImage
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
from lerobot.common.datasets.push_dataset_to_hub.utils import (
calculate_episode_data_index,
concatenate_episodes,
get_default_encoding,
save_images_concurrently,
)
from lerobot.common.datasets.utils import (
calculate_episode_data_index,
hf_transform_to_torch,
)
from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames

2
lerobot/common/datasets/push_dataset_to_hub/umi_zarr_format.py
View File

@@ -28,12 +28,12 @@ from PIL import Image as PILImage
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
from lerobot.common.datasets.push_dataset_to_hub._umi_imagecodecs_numcodecs import register_codecs
from lerobot.common.datasets.push_dataset_to_hub.utils import (
calculate_episode_data_index,
concatenate_episodes,
get_default_encoding,
save_images_concurrently,
)
from lerobot.common.datasets.utils import (
calculate_episode_data_index,
hf_transform_to_torch,
)
from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames

57
lerobot/common/datasets/push_dataset_to_hub/utils.py
View File

@@ -16,7 +16,9 @@
import inspect
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from typing import Dict
import datasets
import numpy
import PIL
import torch
@@ -72,3 +74,58 @@ def check_repo_id(repo_id: str) -> None:
f"""`repo_id` is expected to contain a community or user id `/` the name of the dataset
(e.g. 'lerobot/pusht'), but contains '{repo_id}'."""
)
# TODO(aliberts): remove
def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> Dict[str, torch.Tensor]:
"""
Calculate episode data index for the provided HuggingFace Dataset. Relies on episode_index column of hf_dataset.
Parameters:
- hf_dataset (datasets.Dataset): A HuggingFace dataset containing the episode index.
Returns:
- episode_data_index: A dictionary containing the data index for each episode. The dictionary has two keys:
- "from": A tensor containing the starting index of each episode.
- "to": A tensor containing the ending index of each episode.
"""
episode_data_index = {"from": [], "to": []}
current_episode = None
"""
The episode_index is a list of integers, each representing the episode index of the corresponding example.
For instance, the following is a valid episode_index:
[0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2]
Below, we iterate through the episode_index and populate the episode_data_index dictionary with the starting and
ending index of each episode. For the episode_index above, the episode_data_index dictionary will look like this:
{
"from": [0, 3, 7],
"to": [3, 7, 12]
}
"""
if len(hf_dataset) == 0:
episode_data_index = {
"from": torch.tensor([]),
"to": torch.tensor([]),
}
return episode_data_index
for idx, episode_idx in enumerate(hf_dataset["episode_index"]):
if episode_idx != current_episode:
# We encountered a new episode, so we append its starting location to the "from" list
episode_data_index["from"].append(idx)
# If this is not the first episode, we append the ending location of the previous episode to the "to" list
if current_episode is not None:
episode_data_index["to"].append(idx)
# Let's keep track of the current episode index
current_episode = episode_idx
else:
# We are still in the same episode, so there is nothing for us to do here
pass
# We have reached the end of the dataset, so we append the ending location of the last episode to the "to" list
episode_data_index["to"].append(idx + 1)
for k in ["from", "to"]:
episode_data_index[k] = torch.tensor(episode_data_index[k])
return episode_data_index

2
lerobot/common/datasets/push_dataset_to_hub/xarm_pkl_format.py
View File

@@ -27,12 +27,12 @@ from PIL import Image as PILImage
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
from lerobot.common.datasets.push_dataset_to_hub.utils import (
calculate_episode_data_index,
concatenate_episodes,
get_default_encoding,
save_images_concurrently,
)
from lerobot.common.datasets.utils import (
calculate_episode_data_index,
hf_transform_to_torch,
)
from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames

652
lerobot/common/datasets/utils.py
View File

@@ -14,30 +14,56 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import re
import warnings
from functools import cache
import logging
import textwrap
from itertools import accumulate
from pathlib import Path
from typing import Dict
from pprint import pformat
from typing import Any
import datasets
import jsonlines
import numpy as np
import pyarrow.compute as pc
import torch
from datasets import load_dataset, load_from_disk
from huggingface_hub import DatasetCard, HfApi, hf_hub_download, snapshot_download
from datasets.table import embed_table_storage
from huggingface_hub import DatasetCard, DatasetCardData, HfApi
from PIL import Image as PILImage
from safetensors.torch import load_file
from torchvision import transforms
from lerobot.common.robot_devices.robots.utils import Robot
DEFAULT_CHUNK_SIZE = 1000 # Max number of episodes per chunk
INFO_PATH = "meta/info.json"
EPISODES_PATH = "meta/episodes.jsonl"
STATS_PATH = "meta/stats.json"
TASKS_PATH = "meta/tasks.jsonl"
DEFAULT_VIDEO_PATH = "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4"
DEFAULT_PARQUET_PATH = "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"
DEFAULT_IMAGE_PATH = "images/{image_key}/episode_{episode_index:06d}/frame_{frame_index:06d}.png"
DATASET_CARD_TEMPLATE = """
---
# Metadata will go there
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## {}
"""
DEFAULT_FEATURES = {
"timestamp": {"dtype": "float32", "shape": (1,), "names": None},
"frame_index": {"dtype": "int64", "shape": (1,), "names": None},
"episode_index": {"dtype": "int64", "shape": (1,), "names": None},
"index": {"dtype": "int64", "shape": (1,), "names": None},
"task_index": {"dtype": "int64", "shape": (1,), "names": None},
}
def flatten_dict(d, parent_key="", sep="/"):
def flatten_dict(d: dict, parent_key: str = "", sep: str = "/") -> dict:
"""Flatten a nested dictionary structure by collapsing nested keys into one key with a separator.
For example:
@@ -56,7 +82,7 @@ def flatten_dict(d, parent_key="", sep="/"):
return dict(items)
def unflatten_dict(d, sep="/"):
def unflatten_dict(d: dict, sep: str = "/") -> dict:
outdict = {}
for key, value in d.items():
parts = key.split(sep)
@@ -69,6 +95,82 @@ def unflatten_dict(d, sep="/"):
return outdict
def serialize_dict(stats: dict[str, torch.Tensor | np.ndarray | dict]) -> dict:
serialized_dict = {key: value.tolist() for key, value in flatten_dict(stats).items()}
return unflatten_dict(serialized_dict)
def write_parquet(dataset: datasets.Dataset, fpath: Path) -> None:
# Embed image bytes into the table before saving to parquet
format = dataset.format
dataset = dataset.with_format("arrow")
dataset = dataset.map(embed_table_storage, batched=False)
dataset = dataset.with_format(**format)
dataset.to_parquet(fpath)
def load_json(fpath: Path) -> Any:
with open(fpath) as f:
return json.load(f)
def write_json(data: dict, fpath: Path) -> None:
fpath.parent.mkdir(exist_ok=True, parents=True)
with open(fpath, "w") as f:
json.dump(data, f, indent=4, ensure_ascii=False)
def load_jsonlines(fpath: Path) -> list[Any]:
with jsonlines.open(fpath, "r") as reader:
return list(reader)
def write_jsonlines(data: dict, fpath: Path) -> None:
fpath.parent.mkdir(exist_ok=True, parents=True)
with jsonlines.open(fpath, "w") as writer:
writer.write_all(data)
def append_jsonlines(data: dict, fpath: Path) -> None:
fpath.parent.mkdir(exist_ok=True, parents=True)
with jsonlines.open(fpath, "a") as writer:
writer.write(data)
def load_info(local_dir: Path) -> dict:
info = load_json(local_dir / INFO_PATH)
for ft in info["features"].values():
ft["shape"] = tuple(ft["shape"])
return info
def load_stats(local_dir: Path) -> dict:
if not (local_dir / STATS_PATH).exists():
return None
stats = load_json(local_dir / STATS_PATH)
stats = {key: torch.tensor(value) for key, value in flatten_dict(stats).items()}
return unflatten_dict(stats)
def load_tasks(local_dir: Path) -> dict:
tasks = load_jsonlines(local_dir / TASKS_PATH)
return {item["task_index"]: item["task"] for item in sorted(tasks, key=lambda x: x["task_index"])}
def load_episodes(local_dir: Path) -> dict:
return load_jsonlines(local_dir / EPISODES_PATH)
def load_image_as_numpy(fpath: str | Path, dtype="float32", channel_first: bool = True) -> np.ndarray:
img = PILImage.open(fpath).convert("RGB")
img_array = np.array(img, dtype=dtype)
if channel_first: # (H, W, C) -> (C, H, W)
img_array = np.transpose(img_array, (2, 0, 1))
if "float" in dtype:
img_array /= 255.0
return img_array
def hf_transform_to_torch(items_dict: dict[torch.Tensor | None]):
"""Get a transform function that convert items from Hugging Face dataset (pyarrow)
to torch tensors. Importantly, images are converted from PIL, which corresponds to
@@ -80,14 +182,6 @@ def hf_transform_to_torch(items_dict: dict[torch.Tensor | None]):
if isinstance(first_item, PILImage.Image):
to_tensor = transforms.ToTensor()
items_dict[key] = [to_tensor(img) for img in items_dict[key]]
elif isinstance(first_item, str):
# TODO (michel-aractingi): add str2embedding via language tokenizer
# For now we leave this part up to the user to choose how to address
# language conditioned tasks
pass
elif isinstance(first_item, dict) and "path" in first_item and "timestamp" in first_item:
# video frame will be processed downstream
pass
elif first_item is None:
pass
else:
@@ -95,19 +189,67 @@ def hf_transform_to_torch(items_dict: dict[torch.Tensor | None]):
return items_dict
@cache
def get_hf_dataset_safe_version(repo_id: str, version: str) -> str:
def _get_major_minor(version: str) -> tuple[int]:
split = version.strip("v").split(".")
return int(split[0]), int(split[1])
class BackwardCompatibilityError(Exception):
def __init__(self, repo_id, version):
message = textwrap.dedent(f"""
BackwardCompatibilityError: The dataset you requested ({repo_id}) is in {version} format.
We introduced a new format since v2.0 which is not backward compatible with v1.x.
Please, use our conversion script. Modify the following command with your own task description:
```
python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \\
--repo-id {repo_id} \\
--single-task "TASK DESCRIPTION." # <---- /!\\ Replace TASK DESCRIPTION /!\\
```
A few examples to replace TASK DESCRIPTION: "Pick up the blue cube and place it into the bin.",
"Insert the peg into the socket.", "Slide open the ziploc bag.", "Take the elevator to the 1st floor.",
"Open the top cabinet, store the pot inside it then close the cabinet.", "Push the T-shaped block onto the T-shaped target.",
"Grab the spray paint on the shelf and place it in the bin on top of the robot dog.", "Fold the sweatshirt.", ...
If you encounter a problem, contact LeRobot maintainers on [Discord](https://discord.com/invite/s3KuuzsPFb)
or open an [issue on GitHub](https://github.com/huggingface/lerobot/issues/new/choose).
""")
super().__init__(message)
def check_version_compatibility(
repo_id: str, version_to_check: str, current_version: str, enforce_breaking_major: bool = True
) -> None:
current_major, _ = _get_major_minor(current_version)
major_to_check, _ = _get_major_minor(version_to_check)
if major_to_check < current_major and enforce_breaking_major:
raise BackwardCompatibilityError(repo_id, version_to_check)
elif float(version_to_check.strip("v")) < float(current_version.strip("v")):
logging.warning(
f"""The dataset you requested ({repo_id}) was created with a previous version ({version_to_check}) of the
codebase. The current codebase version is {current_version}. You should be fine since
backward compatibility is maintained. If you encounter a problem, contact LeRobot maintainers on
Discord ('https://discord.com/invite/s3KuuzsPFb') or open an issue on github.""",
)
def get_hub_safe_version(repo_id: str, version: str) -> str:
api = HfApi()
dataset_info = api.list_repo_refs(repo_id, repo_type="dataset")
branches = [b.name for b in dataset_info.branches]
if version not in branches:
warnings.warn(
num_version = float(version.strip("v"))
hub_num_versions = [float(v.strip("v")) for v in branches if v.startswith("v")]
if num_version >= 2.0 and all(v < 2.0 for v in hub_num_versions):
raise BackwardCompatibilityError(repo_id, version)
logging.warning(
f"""You are trying to load a dataset from {repo_id} created with a previous version of the
codebase. The following versions are available: {branches}.
The requested version ('{version}') is not found. You should be fine since
backward compatibility is maintained. If you encounter a problem, contact LeRobot maintainers on
Discord ('https://discord.com/invite/s3KuuzsPFb') or open an issue on github.""",
stacklevel=1,
)
if "main" not in branches:
raise ValueError(f"Version 'main' not found on {repo_id}")
@@ -116,275 +258,184 @@ def get_hf_dataset_safe_version(repo_id: str, version: str) -> str:
return version
def load_hf_dataset(repo_id: str, version: str, root: Path, split: str) -> datasets.Dataset:
"""hf_dataset contains all the observations, states, actions, rewards, etc."""
if root is not None:
hf_dataset = load_from_disk(str(Path(root) / repo_id / "train"))
# TODO(rcadene): clean this which enables getting a subset of dataset
if split != "train":
if "%" in split:
raise NotImplementedError(f"We dont support splitting based on percentage for now ({split}).")
match_from = re.search(r"train\[(\d+):\]", split)
match_to = re.search(r"train\[:(\d+)\]", split)
if match_from:
from_frame_index = int(match_from.group(1))
hf_dataset = hf_dataset.select(range(from_frame_index, len(hf_dataset)))
elif match_to:
to_frame_index = int(match_to.group(1))
hf_dataset = hf_dataset.select(range(to_frame_index))
else:
raise ValueError(
f'`split` ({split}) should either be "train", "train[INT:]", or "train[:INT]"'
)
else:
safe_version = get_hf_dataset_safe_version(repo_id, version)
hf_dataset = load_dataset(repo_id, revision=safe_version, split=split)
hf_dataset.set_transform(hf_transform_to_torch)
return hf_dataset
def load_episode_data_index(repo_id, version, root) -> dict[str, torch.Tensor]:
"""episode_data_index contains the range of indices for each episode
Example:
```python
from_id = episode_data_index["from"][episode_id].item()
to_id = episode_data_index["to"][episode_id].item()
episode_frames = [dataset[i] for i in range(from_id, to_id)]
```
"""
if root is not None:
path = Path(root) / repo_id / "meta_data" / "episode_data_index.safetensors"
else:
safe_version = get_hf_dataset_safe_version(repo_id, version)
path = hf_hub_download(
repo_id, "meta_data/episode_data_index.safetensors", repo_type="dataset", revision=safe_version
)
return load_file(path)
def load_stats(repo_id, version, root) -> dict[str, dict[str, torch.Tensor]]:
"""stats contains the statistics per modality computed over the full dataset, such as max, min, mean, std
Example:
```python
normalized_action = (action - stats["action"]["mean"]) / stats["action"]["std"]
```
"""
if root is not None:
path = Path(root) / repo_id / "meta_data" / "stats.safetensors"
else:
safe_version = get_hf_dataset_safe_version(repo_id, version)
path = hf_hub_download(
repo_id, "meta_data/stats.safetensors", repo_type="dataset", revision=safe_version
)
stats = load_file(path)
return unflatten_dict(stats)
def load_info(repo_id, version, root) -> dict:
"""info contains useful information regarding the dataset that are not stored elsewhere
Example:
```python
print("frame per second used to collect the video", info["fps"])
```
"""
if root is not None:
path = Path(root) / repo_id / "meta_data" / "info.json"
else:
safe_version = get_hf_dataset_safe_version(repo_id, version)
path = hf_hub_download(repo_id, "meta_data/info.json", repo_type="dataset", revision=safe_version)
with open(path) as f:
info = json.load(f)
return info
def load_videos(repo_id, version, root) -> Path:
if root is not None:
path = Path(root) / repo_id / "videos"
else:
# TODO(rcadene): we download the whole repo here. see if we can avoid this
safe_version = get_hf_dataset_safe_version(repo_id, version)
repo_dir = snapshot_download(repo_id, repo_type="dataset", revision=safe_version)
path = Path(repo_dir) / "videos"
return path
def load_previous_and_future_frames(
item: dict[str, torch.Tensor],
hf_dataset: datasets.Dataset,
episode_data_index: dict[str, torch.Tensor],
delta_timestamps: dict[str, list[float]],
tolerance_s: float,
) -> dict[torch.Tensor]:
"""
Given a current item in the dataset containing a timestamp (e.g. 0.6 seconds), and a list of time differences of
some modalities (e.g. delta_timestamps={"observation.image": [-0.8, -0.2, 0, 0.2]}), this function computes for each
given modality (e.g. "observation.image") a list of query timestamps (e.g. [-0.2, 0.4, 0.6, 0.8]) and loads the closest
frames in the dataset.
Importantly, when no frame can be found around a query timestamp within a specified tolerance window, this function
raises an AssertionError. When a timestamp is queried before the first available timestamp of the episode or after
the last available timestamp, the violation of the tolerance doesnt raise an AssertionError, and the function
populates a boolean array indicating which frames are outside of the episode range. For instance, this boolean array
is useful during batched training to not supervise actions associated to timestamps coming after the end of the
episode, or to pad the observations in a specific way. Note that by default the observation frames before the start
of the episode are the same as the first frame of the episode.
Parameters:
- item (dict): A dictionary containing all the data related to a frame. It is the result of `dataset[idx]`. Each key
corresponds to a different modality (e.g., "timestamp", "observation.image", "action").
- hf_dataset (datasets.Dataset): A dictionary containing the full dataset. Each key corresponds to a different
modality (e.g., "timestamp", "observation.image", "action").
- episode_data_index (dict): A dictionary containing two keys ("from" and "to") associated to dataset indices.
They indicate the start index and end index of each episode in the dataset.
- delta_timestamps (dict): A dictionary containing lists of delta timestamps for each possible modality to be
retrieved. These deltas are added to the item timestamp to form the query timestamps.
- tolerance_s (float, optional): The tolerance level (in seconds) used to determine if a data point is close enough to the query
timestamp by asserting `tol > difference`. It is suggested to set `tol` to a smaller value than the
smallest expected inter-frame period, but large enough to account for jitter.
Returns:
- The same item with the queried frames for each modality specified in delta_timestamps, with an additional key for
each modality (e.g. "observation.image_is_pad").
Raises:
- AssertionError: If any of the frames unexpectedly violate the tolerance level. This could indicate synchronization
issues with timestamps during data collection.
"""
# get indices of the frames associated to the episode, and their timestamps
ep_id = item["episode_index"].item()
ep_data_id_from = episode_data_index["from"][ep_id].item()
ep_data_id_to = episode_data_index["to"][ep_id].item()
ep_data_ids = torch.arange(ep_data_id_from, ep_data_id_to, 1)
# load timestamps
ep_timestamps = hf_dataset.select_columns("timestamp")[ep_data_id_from:ep_data_id_to]["timestamp"]
ep_timestamps = torch.stack(ep_timestamps)
# we make the assumption that the timestamps are sorted
ep_first_ts = ep_timestamps[0]
ep_last_ts = ep_timestamps[-1]
current_ts = item["timestamp"].item()
for key in delta_timestamps:
# get timestamps used as query to retrieve data of previous/future frames
delta_ts = delta_timestamps[key]
query_ts = current_ts + torch.tensor(delta_ts)
# compute distances between each query timestamp and all timestamps of all the frames belonging to the episode
dist = torch.cdist(query_ts[:, None], ep_timestamps[:, None], p=1)
min_, argmin_ = dist.min(1)
# TODO(rcadene): synchronize timestamps + interpolation if needed
is_pad = min_ > tolerance_s
# check violated query timestamps are all outside the episode range
assert ((query_ts[is_pad] < ep_first_ts) | (ep_last_ts < query_ts[is_pad])).all(), (
f"One or several timestamps unexpectedly violate the tolerance ({min_} > {tolerance_s=}) inside episode range."
"This might be due to synchronization issues with timestamps during data collection."
)
# get dataset indices corresponding to frames to be loaded
data_ids = ep_data_ids[argmin_]
# load frames modality
item[key] = hf_dataset.select_columns(key)[data_ids][key]
if isinstance(item[key][0], dict) and "path" in item[key][0]:
# video mode where frame are expressed as dict of path and timestamp
item[key] = item[key]
def get_hf_features_from_features(features: dict) -> datasets.Features:
hf_features = {}
for key, ft in features.items():
if ft["dtype"] == "video":
continue
elif ft["dtype"] == "image":
hf_features[key] = datasets.Image()
elif ft["shape"] == (1,):
hf_features[key] = datasets.Value(dtype=ft["dtype"])
else:
item[key] = torch.stack(item[key])
assert len(ft["shape"]) == 1
hf_features[key] = datasets.Sequence(
length=ft["shape"][0], feature=datasets.Value(dtype=ft["dtype"])
)
item[f"{key}_is_pad"] = is_pad
return item
return datasets.Features(hf_features)
def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> Dict[str, torch.Tensor]:
"""
Calculate episode data index for the provided HuggingFace Dataset. Relies on episode_index column of hf_dataset.
Parameters:
- hf_dataset (datasets.Dataset): A HuggingFace dataset containing the episode index.
Returns:
- episode_data_index: A dictionary containing the data index for each episode. The dictionary has two keys:
- "from": A tensor containing the starting index of each episode.
- "to": A tensor containing the ending index of each episode.
"""
episode_data_index = {"from": [], "to": []}
current_episode = None
"""
The episode_index is a list of integers, each representing the episode index of the corresponding example.
For instance, the following is a valid episode_index:
[0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2]
Below, we iterate through the episode_index and populate the episode_data_index dictionary with the starting and
ending index of each episode. For the episode_index above, the episode_data_index dictionary will look like this:
{
"from": [0, 3, 7],
"to": [3, 7, 12]
def get_features_from_robot(robot: Robot, use_videos: bool = True) -> dict:
camera_ft = {}
if robot.cameras:
camera_ft = {
key: {"dtype": "video" if use_videos else "image", **ft}
for key, ft in robot.camera_features.items()
}
"""
if len(hf_dataset) == 0:
episode_data_index = {
"from": torch.tensor([]),
"to": torch.tensor([]),
}
return episode_data_index
for idx, episode_idx in enumerate(hf_dataset["episode_index"]):
if episode_idx != current_episode:
# We encountered a new episode, so we append its starting location to the "from" list
episode_data_index["from"].append(idx)
# If this is not the first episode, we append the ending location of the previous episode to the "to" list
if current_episode is not None:
episode_data_index["to"].append(idx)
# Let's keep track of the current episode index
current_episode = episode_idx
else:
# We are still in the same episode, so there is nothing for us to do here
pass
# We have reached the end of the dataset, so we append the ending location of the last episode to the "to" list
episode_data_index["to"].append(idx + 1)
for k in ["from", "to"]:
episode_data_index[k] = torch.tensor(episode_data_index[k])
return episode_data_index
return {**robot.motor_features, **camera_ft, **DEFAULT_FEATURES}
def reset_episode_index(hf_dataset: datasets.Dataset) -> datasets.Dataset:
"""Reset the `episode_index` of the provided HuggingFace Dataset.
`episode_data_index` (and related functionality such as `load_previous_and_future_frames`) requires the
`episode_index` to be sorted, continuous (1,1,1 and not 1,2,1) and start at 0.
This brings the `episode_index` to the required format.
"""
if len(hf_dataset) == 0:
return hf_dataset
unique_episode_idxs = torch.stack(hf_dataset["episode_index"]).unique().tolist()
episode_idx_to_reset_idx_mapping = {
ep_id: reset_ep_id for reset_ep_id, ep_id in enumerate(unique_episode_idxs)
def create_empty_dataset_info(
codebase_version: str,
fps: int,
robot_type: str,
features: dict,
use_videos: bool,
) -> dict:
return {
"codebase_version": codebase_version,
"robot_type": robot_type,
"total_episodes": 0,
"total_frames": 0,
"total_tasks": 0,
"total_videos": 0,
"total_chunks": 0,
"chunks_size": DEFAULT_CHUNK_SIZE,
"fps": fps,
"splits": {},
"data_path": DEFAULT_PARQUET_PATH,
"video_path": DEFAULT_VIDEO_PATH if use_videos else None,
"features": features,
}
def modify_ep_idx_func(example):
example["episode_index"] = episode_idx_to_reset_idx_mapping[example["episode_index"].item()]
return example
hf_dataset = hf_dataset.map(modify_ep_idx_func)
def get_episode_data_index(
episode_dicts: list[dict], episodes: list[int] | None = None
) -> dict[str, torch.Tensor]:
episode_lengths = {ep_idx: ep_dict["length"] for ep_idx, ep_dict in enumerate(episode_dicts)}
if episodes is not None:
episode_lengths = {ep_idx: episode_lengths[ep_idx] for ep_idx in episodes}
return hf_dataset
cumulative_lenghts = list(accumulate(episode_lengths.values()))
return {
"from": torch.LongTensor([0] + cumulative_lenghts[:-1]),
"to": torch.LongTensor(cumulative_lenghts),
}
def calculate_total_episode(
hf_dataset: datasets.Dataset, raise_if_not_contiguous: bool = True
) -> dict[str, torch.Tensor]:
episode_indices = sorted(hf_dataset.unique("episode_index"))
total_episodes = len(episode_indices)
if raise_if_not_contiguous and episode_indices != list(range(total_episodes)):
raise ValueError("episode_index values are not sorted and contiguous.")
return total_episodes
def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> dict[str, torch.Tensor]:
episode_lengths = []
table = hf_dataset.data.table
total_episodes = calculate_total_episode(hf_dataset)
for ep_idx in range(total_episodes):
ep_table = table.filter(pc.equal(table["episode_index"], ep_idx))
episode_lengths.insert(ep_idx, len(ep_table))
cumulative_lenghts = list(accumulate(episode_lengths))
return {
"from": torch.LongTensor([0] + cumulative_lenghts[:-1]),
"to": torch.LongTensor(cumulative_lenghts),
}
def check_timestamps_sync(
hf_dataset: datasets.Dataset,
episode_data_index: dict[str, torch.Tensor],
fps: int,
tolerance_s: float,
raise_value_error: bool = True,
) -> bool:
"""
This check is to make sure that each timestamps is separated to the next by 1/fps +/- tolerance to
account for possible numerical error.
"""
timestamps = torch.stack(hf_dataset["timestamp"])
diffs = torch.diff(timestamps)
within_tolerance = torch.abs(diffs - 1 / fps) <= tolerance_s
# We mask differences between the timestamp at the end of an episode
# and the one at the start of the next episode since these are expected
# to be outside tolerance.
mask = torch.ones(len(diffs), dtype=torch.bool)
ignored_diffs = episode_data_index["to"][:-1] - 1
mask[ignored_diffs] = False
filtered_within_tolerance = within_tolerance[mask]
if not torch.all(filtered_within_tolerance):
# Track original indices before masking
original_indices = torch.arange(len(diffs))
filtered_indices = original_indices[mask]
outside_tolerance_filtered_indices = torch.nonzero(~filtered_within_tolerance) # .squeeze()
outside_tolerance_indices = filtered_indices[outside_tolerance_filtered_indices]
episode_indices = torch.stack(hf_dataset["episode_index"])
outside_tolerances = []
for idx in outside_tolerance_indices:
entry = {
"timestamps": [timestamps[idx], timestamps[idx + 1]],
"diff": diffs[idx],
"episode_index": episode_indices[idx].item(),
}
outside_tolerances.append(entry)
if raise_value_error:
raise ValueError(
f"""One or several timestamps unexpectedly violate the tolerance inside episode range.
This might be due to synchronization issues with timestamps during data collection.
\n{pformat(outside_tolerances)}"""
)
return False
return True
def check_delta_timestamps(
delta_timestamps: dict[str, list[float]], fps: int, tolerance_s: float, raise_value_error: bool = True
) -> bool:
"""This will check if all the values in delta_timestamps are multiples of 1/fps +/- tolerance.
This is to ensure that these delta_timestamps added to any timestamp from a dataset will themselves be
actual timestamps from the dataset.
"""
outside_tolerance = {}
for key, delta_ts in delta_timestamps.items():
within_tolerance = [abs(ts * fps - round(ts * fps)) / fps <= tolerance_s for ts in delta_ts]
if not all(within_tolerance):
outside_tolerance[key] = [
ts for ts, is_within in zip(delta_ts, within_tolerance, strict=True) if not is_within
]
if len(outside_tolerance) > 0:
if raise_value_error:
raise ValueError(
f"""
The following delta_timestamps are found outside of tolerance range.
Please make sure they are multiples of 1/{fps} +/- tolerance and adjust
their values accordingly.
\n{pformat(outside_tolerance)}
"""
)
return False
return True
def get_delta_indices(delta_timestamps: dict[str, list[float]], fps: int) -> dict[str, list[int]]:
delta_indices = {}
for key, delta_ts in delta_timestamps.items():
delta_indices[key] = (torch.tensor(delta_ts) * fps).long().tolist()
return delta_indices
def cycle(iterable):
@@ -400,7 +451,7 @@ def cycle(iterable):
iterator = iter(iterable)
def create_branch(repo_id, *, branch: str, repo_type: str | None = None):
def create_branch(repo_id, *, branch: str, repo_type: str | None = None) -> None:
"""Create a branch on a existing Hugging Face repo. Delete the branch if it already
exists before creating it.
"""
@@ -415,12 +466,35 @@ def create_branch(repo_id, *, branch: str, repo_type: str | None = None):
api.create_branch(repo_id, repo_type=repo_type, branch=branch)
def create_lerobot_dataset_card(tags: list | None = None, text: str | None = None) -> DatasetCard:
card = DatasetCard(DATASET_CARD_TEMPLATE)
card.data.task_categories = ["robotics"]
card.data.tags = ["LeRobot"]
if tags is not None:
card.data.tags += tags
if text is not None:
card.text += text
return card
def create_lerobot_dataset_card(
tags: list | None = None,
dataset_info: dict | None = None,
**kwargs,
) -> DatasetCard:
"""
Keyword arguments will be used to replace values in ./lerobot/common/datasets/card_template.md.
Note: If specified, license must be one of https://huggingface.co/docs/hub/repositories-licenses.
"""
card_tags = ["LeRobot"]
if tags:
card_tags += tags
if dataset_info:
dataset_structure = "[meta/info.json](meta/info.json):\n"
dataset_structure += f"```json\n{json.dumps(dataset_info, indent=4)}\n```\n"
kwargs = {**kwargs, "dataset_structure": dataset_structure}
card_data = DatasetCardData(
license=kwargs.get("license"),
tags=card_tags,
task_categories=["robotics"],
configs=[
{
"config_name": "default",
"data_files": "data/*/*.parquet",
}
],
)
return DatasetCard.from_template(
card_data=card_data,
template_path="./lerobot/common/datasets/card_template.md",
**kwargs,
)

882
lerobot/common/datasets/v2/batch_convert_dataset_v1_to_v2.py Normal file
View File

@@ -0,0 +1,882 @@
#!/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.
"""
This script is for internal use to convert all datasets under the 'lerobot' hub user account to v2.
Note: Since the original Aloha datasets don't use shadow motors, you need to comment those out in
lerobot/configs/robot/aloha.yaml before running this script.
"""
import traceback
from pathlib import Path
from textwrap import dedent
from lerobot import available_datasets
from lerobot.common.datasets.v2.convert_dataset_v1_to_v2 import convert_dataset, parse_robot_config
LOCAL_DIR = Path("data/")
ALOHA_CONFIG = Path("lerobot/configs/robot/aloha.yaml")
ALOHA_MOBILE_INFO = {
"robot_config": parse_robot_config(ALOHA_CONFIG),
"license": "mit",
"url": "https://mobile-aloha.github.io/",
"paper": "https://arxiv.org/abs/2401.02117",
"citation_bibtex": dedent(r"""
@inproceedings{fu2024mobile,
author = {Fu, Zipeng and Zhao, Tony Z. and Finn, Chelsea},
title = {Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation},
booktitle = {arXiv},
year = {2024},
}""").lstrip(),
}
ALOHA_STATIC_INFO = {
"robot_config": parse_robot_config(ALOHA_CONFIG),
"license": "mit",
"url": "https://tonyzhaozh.github.io/aloha/",
"paper": "https://arxiv.org/abs/2304.13705",
"citation_bibtex": dedent(r"""
@article{Zhao2023LearningFB,
title={Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware},
author={Tony Zhao and Vikash Kumar and Sergey Levine and Chelsea Finn},
journal={RSS},
year={2023},
volume={abs/2304.13705},
url={https://arxiv.org/abs/2304.13705}
}""").lstrip(),
}
PUSHT_INFO = {
"license": "mit",
"url": "https://diffusion-policy.cs.columbia.edu/",
"paper": "https://arxiv.org/abs/2303.04137v5",
"citation_bibtex": dedent(r"""
@article{chi2024diffusionpolicy,
author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song},
title ={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
journal = {The International Journal of Robotics Research},
year = {2024},
}""").lstrip(),
}
XARM_INFO = {
"license": "mit",
"url": "https://www.nicklashansen.com/td-mpc/",
"paper": "https://arxiv.org/abs/2203.04955",
"citation_bibtex": dedent(r"""
@inproceedings{Hansen2022tdmpc,
title={Temporal Difference Learning for Model Predictive Control},
author={Nicklas Hansen and Xiaolong Wang and Hao Su},
booktitle={ICML},
year={2022}
}
"""),
}
UNITREEH_INFO = {
"license": "apache-2.0",
}
DATASETS = {
"aloha_mobile_cabinet": {
"single_task": "Open the top cabinet, store the pot inside it then close the cabinet.",
**ALOHA_MOBILE_INFO,
},
"aloha_mobile_chair": {
"single_task": "Push the chairs in front of the desk to place them against it.",
**ALOHA_MOBILE_INFO,
},
"aloha_mobile_elevator": {
"single_task": "Take the elevator to the 1st floor.",
**ALOHA_MOBILE_INFO,
},
"aloha_mobile_shrimp": {
"single_task": "Sauté the raw shrimp on both sides, then serve it in the bowl.",
**ALOHA_MOBILE_INFO,
},
"aloha_mobile_wash_pan": {
"single_task": "Pick up the pan, rinse it in the sink and then place it in the drying rack.",
**ALOHA_MOBILE_INFO,
},
"aloha_mobile_wipe_wine": {
"single_task": "Pick up the wet cloth on the faucet and use it to clean the spilled wine on the table and underneath the glass.",
**ALOHA_MOBILE_INFO,
},
"aloha_static_battery": {
"single_task": "Place the battery into the slot of the remote controller.",
**ALOHA_STATIC_INFO,
},
"aloha_static_candy": {"single_task": "Pick up the candy and unwrap it.", **ALOHA_STATIC_INFO},
"aloha_static_coffee": {
"single_task": "Place the coffee capsule inside the capsule container, then place the cup onto the center of the cup tray, then push the 'Hot Water' and 'Travel Mug' buttons.",
**ALOHA_STATIC_INFO,
},
"aloha_static_coffee_new": {
"single_task": "Place the coffee capsule inside the capsule container, then place the cup onto the center of the cup tray.",
**ALOHA_STATIC_INFO,
},
"aloha_static_cups_open": {
"single_task": "Pick up the plastic cup and open its lid.",
**ALOHA_STATIC_INFO,
},
"aloha_static_fork_pick_up": {
"single_task": "Pick up the fork and place it on the plate.",
**ALOHA_STATIC_INFO,
},
"aloha_static_pingpong_test": {
"single_task": "Transfer one of the two balls in the right glass into the left glass, then transfer it back to the right glass.",
**ALOHA_STATIC_INFO,
},
"aloha_static_pro_pencil": {
"single_task": "Pick up the pencil with the right arm, hand it over to the left arm then place it back onto the table.",
**ALOHA_STATIC_INFO,
},
"aloha_static_screw_driver": {
"single_task": "Pick up the screwdriver with the right arm, hand it over to the left arm then place it into the cup.",
**ALOHA_STATIC_INFO,
},
"aloha_static_tape": {
"single_task": "Cut a small piece of tape from the tape dispenser then place it on the cardboard box's edge.",
**ALOHA_STATIC_INFO,
},
"aloha_static_thread_velcro": {
"single_task": "Pick up the velcro cable tie with the left arm, then insert the end of the velcro tie into the other end's loop with the right arm.",
**ALOHA_STATIC_INFO,
},
"aloha_static_towel": {
"single_task": "Pick up a piece of paper towel and place it on the spilled liquid.",
**ALOHA_STATIC_INFO,
},
"aloha_static_vinh_cup": {
"single_task": "Pick up the platic cup with the right arm, then pop its lid open with the left arm.",
**ALOHA_STATIC_INFO,
},
"aloha_static_vinh_cup_left": {
"single_task": "Pick up the platic cup with the left arm, then pop its lid open with the right arm.",
**ALOHA_STATIC_INFO,
},
"aloha_static_ziploc_slide": {"single_task": "Slide open the ziploc bag.", **ALOHA_STATIC_INFO},
"aloha_sim_insertion_scripted": {"single_task": "Insert the peg into the socket.", **ALOHA_STATIC_INFO},
"aloha_sim_insertion_scripted_image": {
"single_task": "Insert the peg into the socket.",
**ALOHA_STATIC_INFO,
},
"aloha_sim_insertion_human": {"single_task": "Insert the peg into the socket.", **ALOHA_STATIC_INFO},
"aloha_sim_insertion_human_image": {
"single_task": "Insert the peg into the socket.",
**ALOHA_STATIC_INFO,
},
"aloha_sim_transfer_cube_scripted": {
"single_task": "Pick up the cube with the right arm and transfer it to the left arm.",
**ALOHA_STATIC_INFO,
},
"aloha_sim_transfer_cube_scripted_image": {
"single_task": "Pick up the cube with the right arm and transfer it to the left arm.",
**ALOHA_STATIC_INFO,
},
"aloha_sim_transfer_cube_human": {
"single_task": "Pick up the cube with the right arm and transfer it to the left arm.",
**ALOHA_STATIC_INFO,
},
"aloha_sim_transfer_cube_human_image": {
"single_task": "Pick up the cube with the right arm and transfer it to the left arm.",
**ALOHA_STATIC_INFO,
},
"pusht": {"single_task": "Push the T-shaped block onto the T-shaped target.", **PUSHT_INFO},
"pusht_image": {"single_task": "Push the T-shaped block onto the T-shaped target.", **PUSHT_INFO},
"unitreeh1_fold_clothes": {"single_task": "Fold the sweatshirt.", **UNITREEH_INFO},
"unitreeh1_rearrange_objects": {"single_task": "Put the object into the bin.", **UNITREEH_INFO},
"unitreeh1_two_robot_greeting": {
"single_task": "Greet the other robot with a high five.",
**UNITREEH_INFO,
},
"unitreeh1_warehouse": {
"single_task": "Grab the spray paint on the shelf and place it in the bin on top of the robot dog.",
**UNITREEH_INFO,
},
"xarm_lift_medium": {"single_task": "Pick up the cube and lift it.", **XARM_INFO},
"xarm_lift_medium_image": {"single_task": "Pick up the cube and lift it.", **XARM_INFO},
"xarm_lift_medium_replay": {"single_task": "Pick up the cube and lift it.", **XARM_INFO},
"xarm_lift_medium_replay_image": {"single_task": "Pick up the cube and lift it.", **XARM_INFO},
"xarm_push_medium": {"single_task": "Push the cube onto the target.", **XARM_INFO},
"xarm_push_medium_image": {"single_task": "Push the cube onto the target.", **XARM_INFO},
"xarm_push_medium_replay": {"single_task": "Push the cube onto the target.", **XARM_INFO},
"xarm_push_medium_replay_image": {"single_task": "Push the cube onto the target.", **XARM_INFO},
"umi_cup_in_the_wild": {
"single_task": "Put the cup on the plate.",
"license": "apache-2.0",
},
"asu_table_top": {
"tasks_col": "language_instruction",
"license": "mit",
"paper": "https://link.springer.com/article/10.1007/s10514-023-10129-1",
"citation_bibtex": dedent(r"""
@inproceedings{zhou2023modularity,
title={Modularity through Attention: Efficient Training and Transfer of Language-Conditioned Policies for Robot Manipulation},
author={Zhou, Yifan and Sonawani, Shubham and Phielipp, Mariano and Stepputtis, Simon and Amor, Heni},
booktitle={Conference on Robot Learning},
pages={1684--1695},
year={2023},
organization={PMLR}
}
@article{zhou2023learning,
title={Learning modular language-conditioned robot policies through attention},
author={Zhou, Yifan and Sonawani, Shubham and Phielipp, Mariano and Ben Amor, Heni and Stepputtis, Simon},
journal={Autonomous Robots},
pages={1--21},
year={2023},
publisher={Springer}
}""").lstrip(),
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"tasks_col": "language_instruction",
"license": "mit",
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"citation_bibtex": dedent(r"""
@article{zhu2022bottom,
title={Bottom-Up Skill Discovery From Unsegmented Demonstrations for Long-Horizon Robot Manipulation},
author={Zhu, Yifeng and Stone, Peter and Zhu, Yuke},
journal={IEEE Robotics and Automation Letters},
volume={7},
number={2},
pages={4126--4133},
year={2022},
publisher={IEEE}
}""").lstrip(),
},
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"tasks_col": "language_instruction",
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"citation_bibtex": dedent(r"""
@inproceedings{nasiriany2022sailor,
title={Learning and Retrieval from Prior Data for Skill-based Imitation Learning},
author={Soroush Nasiriany and Tian Gao and Ajay Mandlekar and Yuke Zhu},
booktitle={Conference on Robot Learning (CoRL)},
year={2022}
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
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"citation_bibtex": dedent(r"""
@inproceedings{liu2022robot,
title = {Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment},
author = {Huihan Liu and Soroush Nasiriany and Lance Zhang and Zhiyao Bao and Yuke Zhu},
booktitle = {Robotics: Science and Systems (RSS)},
year = {2023}
}""").lstrip(),
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@misc{BerkeleyUR5Website,
title = {Berkeley {UR5} Demonstration Dataset},
author = {Lawrence Yunliang Chen and Simeon Adebola and Ken Goldberg},
howpublished = {https://sites.google.com/view/berkeley-ur5/home},
}""").lstrip(),
},
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"citation_bibtex": dedent(r"""
@article{luo2023multistage,
author = {Jianlan Luo and Charles Xu and Xinyang Geng and Gilbert Feng and Kuan Fang and Liam Tan and Stefan Schaal and Sergey Levine},
title = {Multi-Stage Cable Routing through Hierarchical Imitation Learning},
journal = {arXiv pre-print},
year = {2023},
url = {https://arxiv.org/abs/2307.08927},
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
"url": "https://sites.google.com/berkeley.edu/fanuc-manipulation",
"citation_bibtex": dedent(r"""
@article{fanuc_manipulation2023,
title={Fanuc Manipulation: A Dataset for Learning-based Manipulation with FANUC Mate 200iD Robot},
author={Zhu, Xinghao and Tian, Ran and Xu, Chenfeng and Ding, Mingyu and Zhan, Wei and Tomizuka, Masayoshi},
year={2023},
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
"paper": "https://arxiv.org/abs/1709.10489",
"citation_bibtex": dedent(r"""
@inproceedings{kahn2018self,
title={Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation},
author={Kahn, Gregory and Villaflor, Adam and Ding, Bosen and Abbeel, Pieter and Levine, Sergey},
booktitle={2018 IEEE international conference on robotics and automation (ICRA)},
pages={5129--5136},
year={2018},
organization={IEEE}
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
"url": "https://sites.google.com/view/recon-robot",
"paper": "https://arxiv.org/abs/2104.05859",
"citation_bibtex": dedent(r"""
@inproceedings{shah2021rapid,
title={Rapid Exploration for Open-World Navigation with Latent Goal Models},
author={Dhruv Shah and Benjamin Eysenbach and Nicholas Rhinehart and Sergey Levine},
booktitle={5th Annual Conference on Robot Learning },
year={2021},
url={https://openreview.net/forum?id=d_SWJhyKfVw}
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
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"citation_bibtex": dedent(r"""
@article{hirose2023sacson,
title={SACSoN: Scalable Autonomous Data Collection for Social Navigation},
author={Hirose, Noriaki and Shah, Dhruv and Sridhar, Ajay and Levine, Sergey},
journal={arXiv preprint arXiv:2306.01874},
year={2023}
}""").lstrip(),
},
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"license": "mit",
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"citation_bibtex": dedent(r"""
@InProceedings{Radosavovic2022,
title = {Real-World Robot Learning with Masked Visual Pre-training},
author = {Ilija Radosavovic and Tete Xiao and Stephen James and Pieter Abbeel and Jitendra Malik and Trevor Darrell},
booktitle = {CoRL},
year = {2022}
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
"paper": "https://arxiv.org/abs/2306.10007",
"citation_bibtex": dedent(r"""
@article{Radosavovic2023,
title={Robot Learning with Sensorimotor Pre-training},
author={Ilija Radosavovic and Baifeng Shi and Letian Fu and Ken Goldberg and Trevor Darrell and Jitendra Malik},
year={2023},
journal={arXiv:2306.10007}
}""").lstrip(),
},
"cmu_franka_exploration_dataset": {
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"license": "mit",
"url": "https://human-world-model.github.io/",
"paper": "https://arxiv.org/abs/2308.10901",
"citation_bibtex": dedent(r"""
@inproceedings{mendonca2023structured,
title={Structured World Models from Human Videos},
author={Mendonca, Russell and Bahl, Shikhar and Pathak, Deepak},
journal={RSS},
year={2023}
}""").lstrip(),
},
"cmu_play_fusion": {
"tasks_col": "language_instruction",
"license": "mit",
"url": "https://play-fusion.github.io/",
"paper": "https://arxiv.org/abs/2312.04549",
"citation_bibtex": dedent(r"""
@inproceedings{chen2023playfusion,
title={PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play},
author={Chen, Lili and Bahl, Shikhar and Pathak, Deepak},
booktitle={CoRL},
year={2023}
}""").lstrip(),
},
"cmu_stretch": {
"tasks_col": "language_instruction",
"license": "mit",
"url": "https://robo-affordances.github.io/",
"paper": "https://arxiv.org/abs/2304.08488",
"citation_bibtex": dedent(r"""
@inproceedings{bahl2023affordances,
title={Affordances from Human Videos as a Versatile Representation for Robotics},
author={Bahl, Shikhar and Mendonca, Russell and Chen, Lili and Jain, Unnat and Pathak, Deepak},
booktitle={CVPR},
year={2023}
}
@article{mendonca2023structured,
title={Structured World Models from Human Videos},
author={Mendonca, Russell and Bahl, Shikhar and Pathak, Deepak},
journal={CoRL},
year={2023}
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
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"paper": "https://arxiv.org/abs/2303.04137v5",
"citation_bibtex": dedent(r"""
@inproceedings{chi2023diffusionpolicy,
title={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
author={Chi, Cheng and Feng, Siyuan and Du, Yilun and Xu, Zhenjia and Cousineau, Eric and Burchfiel, Benjamin and Song, Shuran},
booktitle={Proceedings of Robotics: Science and Systems (RSS)},
year={2023}
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
"url": "https://sites.google.com/view/conq-hose-manipulation-dataset/home",
"citation_bibtex": dedent(r"""
@misc{ConqHoseManipData,
author={Peter Mitrano and Dmitry Berenson},
title={Conq Hose Manipulation Dataset, v1.15.0},
year={2024},
howpublished={https://sites.google.com/view/conq-hose-manipulation-dataset}
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
"paper": "https://ieeexplore.ieee.org/document/9341156",
"citation_bibtex": dedent(r"""
@inproceedings{vogel_edan_2020,
title = {EDAN - an EMG-Controlled Daily Assistant to Help People with Physical Disabilities},
language = {en},
booktitle = {2020 {IEEE}/{RSJ} {International} {Conference} on {Intelligent} {Robots} and {Systems} ({IROS})},
author = {Vogel, Jörn and Hagengruber, Annette and Iskandar, Maged and Quere, Gabriel and Leipscher, Ulrike and Bustamante, Samuel and Dietrich, Alexander and Hoeppner, Hannes and Leidner, Daniel and Albu-Schäffer, Alin},
year = {2020}
}
@inproceedings{quere_shared_2020,
address = {Paris, France},
title = {Shared {Control} {Templates} for {Assistive} {Robotics}},
language = {en},
booktitle = {2020 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},
author = {Quere, Gabriel and Hagengruber, Annette and Iskandar, Maged and Bustamante, Samuel and Leidner, Daniel and Stulp, Freek and Vogel, Joern},
year = {2020},
pages = {7},
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
"paper": "https://www.researchsquare.com/article/rs-3289569/v1",
"citation_bibtex": dedent(r"""
@article{padalkar2023guided,
title={A guided reinforcement learning approach using shared control templates for learning manipulation skills in the real world},
author={Padalkar, Abhishek and Quere, Gabriel and Raffin, Antonin and Silv{\'e}rio, Jo{\~a}o and Stulp, Freek},
journal={Research square preprint rs-3289569/v1},
year={2023}
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
"paper": "https://elib.dlr.de/193739/1/padalkar2023rlsct.pdf",
"citation_bibtex": dedent(r"""
@inproceedings{padalkar2023guiding,
title={Guiding Reinforcement Learning with Shared Control Templates},
author={Padalkar, Abhishek and Quere, Gabriel and Steinmetz, Franz and Raffin, Antonin and Nieuwenhuisen, Matthias and Silv{\'e}rio, Jo{\~a}o and Stulp, Freek},
booktitle={40th IEEE International Conference on Robotics and Automation, ICRA 2023},
year={2023},
organization={IEEE}
}""").lstrip(),
},
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"license": "mit",
"url": "https://droid-dataset.github.io/",
"paper": "https://arxiv.org/abs/2403.12945",
"citation_bibtex": dedent(r"""
@article{khazatsky2024droid,
title = {DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset},
author = {Alexander Khazatsky and Karl Pertsch and Suraj Nair and Ashwin Balakrishna and Sudeep Dasari and Siddharth Karamcheti and Soroush Nasiriany and Mohan Kumar Srirama and Lawrence Yunliang Chen and Kirsty Ellis and Peter David Fagan and Joey Hejna and Masha Itkina and Marion Lepert and Yecheng Jason Ma and Patrick Tree Miller and Jimmy Wu and Suneel Belkhale and Shivin Dass and Huy Ha and Arhan Jain and Abraham Lee and Youngwoon Lee and Marius Memmel and Sungjae Park and Ilija Radosavovic and Kaiyuan Wang and Albert Zhan and Kevin Black and Cheng Chi and Kyle Beltran Hatch and Shan Lin and Jingpei Lu and Jean Mercat and Abdul Rehman and Pannag R Sanketi and Archit Sharma and Cody Simpson and Quan Vuong and Homer Rich Walke and Blake Wulfe and Ted Xiao and Jonathan Heewon Yang and Arefeh Yavary and Tony Z. Zhao and Christopher Agia and Rohan Baijal and Mateo Guaman Castro and Daphne Chen and Qiuyu Chen and Trinity Chung and Jaimyn Drake and Ethan Paul Foster and Jensen Gao and David Antonio Herrera and Minho Heo and Kyle Hsu and Jiaheng Hu and Donovon Jackson and Charlotte Le and Yunshuang Li and Kevin Lin and Roy Lin and Zehan Ma and Abhiram Maddukuri and Suvir Mirchandani and Daniel Morton and Tony Nguyen and Abigail O'Neill and Rosario Scalise and Derick Seale and Victor Son and Stephen Tian and Emi Tran and Andrew E. Wang and Yilin Wu and Annie Xie and Jingyun Yang and Patrick Yin and Yunchu Zhang and Osbert Bastani and Glen Berseth and Jeannette Bohg and Ken Goldberg and Abhinav Gupta and Abhishek Gupta and Dinesh Jayaraman and Joseph J Lim and Jitendra Malik and Roberto Martín-Martín and Subramanian Ramamoorthy and Dorsa Sadigh and Shuran Song and Jiajun Wu and Michael C. Yip and Yuke Zhu and Thomas Kollar and Sergey Levine and Chelsea Finn},
year = {2024},
}""").lstrip(),
},
"fmb": {
"tasks_col": "language_instruction",
"license": "cc-by-4.0",
"url": "https://functional-manipulation-benchmark.github.io/",
"paper": "https://arxiv.org/abs/2401.08553",
"citation_bibtex": dedent(r"""
@article{luo2024fmb,
title={FMB: a Functional Manipulation Benchmark for Generalizable Robotic Learning},
author={Luo, Jianlan and Xu, Charles and Liu, Fangchen and Tan, Liam and Lin, Zipeng and Wu, Jeffrey and Abbeel, Pieter and Levine, Sergey},
journal={arXiv preprint arXiv:2401.08553},
year={2024}
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
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"paper": "https://arxiv.org/abs/2401.14502",
"citation_bibtex": dedent(r"""
@inproceedings{saxena2023multiresolution,
title={Multi-Resolution Sensing for Real-Time Control with Vision-Language Models},
author={Saumya Saxena and Mohit Sharma and Oliver Kroemer},
booktitle={7th Annual Conference on Robot Learning},
year={2023},
url={https://openreview.net/forum?id=WuBv9-IGDUA}
}""").lstrip(),
},
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"license": "mit",
},
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"license": "cc-by-4.0",
"url": "https://github.com/clvrai/clvr_jaco_play_dataset",
"citation_bibtex": dedent(r"""
@software{dass2023jacoplay,
author = {Dass, Shivin and Yapeter, Jullian and Zhang, Jesse and Zhang, Jiahui
and Pertsch, Karl and Nikolaidis, Stefanos and Lim, Joseph J.},
title = {CLVR Jaco Play Dataset},
url = {https://github.com/clvrai/clvr_jaco_play_dataset},
version = {1.0.0},
year = {2023}
}""").lstrip(),
},
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"license": "cc-by-4.0",
"url": "https://github.com/JaeHyung-Kim/rlds_dataset_builder",
"citation_bibtex": dedent(r"""
@article{kimpre,
title={Pre-and post-contact policy decomposition for non-prehensile manipulation with zero-shot sim-to-real transfer},
author={Kim, Minchan and Han, Junhyek and Kim, Jaehyung and Kim, Beomjoon},
booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year={2023},
organization={IEEE}
}""").lstrip(),
},
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"license": "mit",
"url": "https://jyopari.github.io/VINN/",
"paper": "https://arxiv.org/abs/2112.01511",
"citation_bibtex": dedent(r"""
@misc{pari2021surprising,
title={The Surprising Effectiveness of Representation Learning for Visual Imitation},
author={Jyothish Pari and Nur Muhammad Shafiullah and Sridhar Pandian Arunachalam and Lerrel Pinto},
year={2021},
eprint={2112.01511},
archivePrefix={arXiv},
primaryClass={cs.RO}
}""").lstrip(),
},
"nyu_franka_play_dataset": {
"tasks_col": "language_instruction",
"license": "mit",
"url": "https://play-to-policy.github.io/",
"paper": "https://arxiv.org/abs/2210.10047",
"citation_bibtex": dedent(r"""
@article{cui2022play,
title = {From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data},
author = {Cui, Zichen Jeff and Wang, Yibin and Shafiullah, Nur Muhammad Mahi and Pinto, Lerrel},
journal = {arXiv preprint arXiv:2210.10047},
year = {2022}
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
"url": "https://rot-robot.github.io/",
"paper": "https://arxiv.org/abs/2206.15469",
"citation_bibtex": dedent(r"""
@inproceedings{haldar2023watch,
title={Watch and match: Supercharging imitation with regularized optimal transport},
author={Haldar, Siddhant and Mathur, Vaibhav and Yarats, Denis and Pinto, Lerrel},
booktitle={Conference on Robot Learning},
pages={32--43},
year={2023},
organization={PMLR}
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
"url": "https://roboturk.stanford.edu/dataset_real.html",
"paper": "PAPER",
"citation_bibtex": dedent(r"""
@inproceedings{mandlekar2019scaling,
title={Scaling robot supervision to hundreds of hours with roboturk: Robotic manipulation dataset through human reasoning and dexterity},
author={Mandlekar, Ajay and Booher, Jonathan and Spero, Max and Tung, Albert and Gupta, Anchit and Zhu, Yuke and Garg, Animesh and Savarese, Silvio and Fei-Fei, Li},
booktitle={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={1048--1055},
year={2019},
organization={IEEE}
}""").lstrip(),
},
"stanford_hydra_dataset": {
"tasks_col": "language_instruction",
"license": "mit",
"url": "https://sites.google.com/view/hydra-il-2023",
"paper": "https://arxiv.org/abs/2306.17237",
"citation_bibtex": dedent(r"""
@article{belkhale2023hydra,
title={HYDRA: Hybrid Robot Actions for Imitation Learning},
author={Belkhale, Suneel and Cui, Yuchen and Sadigh, Dorsa},
journal={arxiv},
year={2023}
}""").lstrip(),
},
"stanford_kuka_multimodal_dataset": {
"tasks_col": "language_instruction",
"license": "mit",
"url": "https://sites.google.com/view/visionandtouch",
"paper": "https://arxiv.org/abs/1810.10191",
"citation_bibtex": dedent(r"""
@inproceedings{lee2019icra,
title={Making sense of vision and touch: Self-supervised learning of multimodal representations for contact-rich tasks},
author={Lee, Michelle A and Zhu, Yuke and Srinivasan, Krishnan and Shah, Parth and Savarese, Silvio and Fei-Fei, Li and Garg, Animesh and Bohg, Jeannette},
booktitle={2019 IEEE International Conference on Robotics and Automation (ICRA)},
year={2019},
url={https://arxiv.org/abs/1810.10191}
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
"url": "https://hshi74.github.io/robocook/",
"paper": "https://arxiv.org/abs/2306.14447",
"citation_bibtex": dedent(r"""
@article{shi2023robocook,
title={RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools},
author={Shi, Haochen and Xu, Huazhe and Clarke, Samuel and Li, Yunzhu and Wu, Jiajun},
journal={arXiv preprint arXiv:2306.14447},
year={2023}
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "cc-by-4.0",
"url": "https://www.kaggle.com/datasets/oiermees/taco-robot",
"paper": "https://arxiv.org/abs/2209.08959, https://arxiv.org/abs/2210.01911",
"citation_bibtex": dedent(r"""
@inproceedings{rosete2022tacorl,
author = {Erick Rosete-Beas and Oier Mees and Gabriel Kalweit and Joschka Boedecker and Wolfram Burgard},
title = {Latent Plans for Task Agnostic Offline Reinforcement Learning},
journal = {Proceedings of the 6th Conference on Robot Learning (CoRL)},
year = {2022}
}
@inproceedings{mees23hulc2,
title={Grounding Language with Visual Affordances over Unstructured Data},
author={Oier Mees and Jessica Borja-Diaz and Wolfram Burgard},
booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
year={2023},
address = {London, UK}
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
"url": "URL",
"paper": "https://arxiv.org/abs/2107.05842",
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@Article{Osa22,
author = {Takayuki Osa},
journal = {The International Journal of Robotics Research},
title = {Motion Planning by Learning the Solution Manifold in Trajectory Optimization},
year = {2022},
number = {3},
pages = {291--311},
volume = {41},
}""").lstrip(),
},
"toto": {
"tasks_col": "language_instruction",
"license": "mit",
"url": "https://toto-benchmark.org/",
"paper": "https://arxiv.org/abs/2306.00942",
"citation_bibtex": dedent(r"""
@inproceedings{zhou2023train,
author={Zhou, Gaoyue and Dean, Victoria and Srirama, Mohan Kumar and Rajeswaran, Aravind and Pari, Jyothish and Hatch, Kyle and Jain, Aryan and Yu, Tianhe and Abbeel, Pieter and Pinto, Lerrel and Finn, Chelsea and Gupta, Abhinav},
booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
title={Train Offline, Test Online: A Real Robot Learning Benchmark},
year={2023},
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
"citation_bibtex": dedent(r"""
@ARTICLE{ucsd_kitchens,
author = {Ge Yan, Kris Wu, and Xiaolong Wang},
title = {{ucsd kitchens Dataset}},
year = {2023},
month = {August}
}""").lstrip(),
},
"ucsd_pick_and_place_dataset": {
"tasks_col": "language_instruction",
"license": "mit",
"url": "https://owmcorl.github.io/#",
"paper": "https://arxiv.org/abs/2310.16029",
"citation_bibtex": dedent(r"""
@preprint{Feng2023Finetuning,
title={Finetuning Offline World Models in the Real World},
author={Yunhai Feng, Nicklas Hansen, Ziyan Xiong, Chandramouli Rajagopalan, Xiaolong Wang},
year={2023}
}""").lstrip(),
},
"uiuc_d3field": {
"tasks_col": "language_instruction",
"license": "mit",
"url": "https://robopil.github.io/d3fields/",
"paper": "https://arxiv.org/abs/2309.16118",
"citation_bibtex": dedent(r"""
@article{wang2023d3field,
title={D^3Field: Dynamic 3D Descriptor Fields for Generalizable Robotic Manipulation},
author={Wang, Yixuan and Li, Zhuoran and Zhang, Mingtong and Driggs-Campbell, Katherine and Wu, Jiajun and Fei-Fei, Li and Li, Yunzhu},
journal={arXiv preprint arXiv:},
year={2023},
}""").lstrip(),
},
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"tasks_col": "language_instruction",
"license": "mit",
"url": "https://uscresl.github.io/dmfd/",
"paper": "https://arxiv.org/abs/2207.10148",
"citation_bibtex": dedent(r"""
@article{salhotra2022dmfd,
author={Salhotra, Gautam and Liu, I-Chun Arthur and Dominguez-Kuhne, Marcus and Sukhatme, Gaurav S.},
journal={IEEE Robotics and Automation Letters},
title={Learning Deformable Object Manipulation From Expert Demonstrations},
year={2022},
volume={7},
number={4},
pages={8775-8782},
doi={10.1109/LRA.2022.3187843}
}""").lstrip(),
},
"utaustin_mutex": {
"tasks_col": "language_instruction",
"license": "mit",
"url": "https://ut-austin-rpl.github.io/MUTEX/",
"paper": "https://arxiv.org/abs/2309.14320",
"citation_bibtex": dedent(r"""
@inproceedings{shah2023mutex,
title={{MUTEX}: Learning Unified Policies from Multimodal Task Specifications},
author={Rutav Shah and Roberto Mart{\'\i}n-Mart{\'\i}n and Yuke Zhu},
booktitle={7th Annual Conference on Robot Learning},
year={2023},
url={https://openreview.net/forum?id=PwqiqaaEzJ}
}""").lstrip(),
},
"utokyo_pr2_opening_fridge": {
"tasks_col": "language_instruction",
"license": "mit",
"citation_bibtex": dedent(r"""
@misc{oh2023pr2utokyodatasets,
author={Jihoon Oh and Naoaki Kanazawa and Kento Kawaharazuka},
title={X-Embodiment U-Tokyo PR2 Datasets},
year={2023},
url={https://github.com/ojh6404/rlds_dataset_builder},
}""").lstrip(),
},
"utokyo_pr2_tabletop_manipulation": {
"tasks_col": "language_instruction",
"license": "mit",
"citation_bibtex": dedent(r"""
@misc{oh2023pr2utokyodatasets,
author={Jihoon Oh and Naoaki Kanazawa and Kento Kawaharazuka},
title={X-Embodiment U-Tokyo PR2 Datasets},
year={2023},
url={https://github.com/ojh6404/rlds_dataset_builder},
}""").lstrip(),
},
"utokyo_saytap": {
"tasks_col": "language_instruction",
"license": "mit",
"url": "https://saytap.github.io/",
"paper": "https://arxiv.org/abs/2306.07580",
"citation_bibtex": dedent(r"""
@article{saytap2023,
author = {Yujin Tang and Wenhao Yu and Jie Tan and Heiga Zen and Aleksandra Faust and
Tatsuya Harada},
title = {SayTap: Language to Quadrupedal Locomotion},
eprint = {arXiv:2306.07580},
url = {https://saytap.github.io},
note = {https://saytap.github.io},
year = {2023}
}""").lstrip(),
},
"utokyo_xarm_bimanual": {
"tasks_col": "language_instruction",
"license": "cc-by-4.0",
"citation_bibtex": dedent(r"""
@misc{matsushima2023weblab,
title={Weblab xArm Dataset},
author={Tatsuya Matsushima and Hiroki Furuta and Yusuke Iwasawa and Yutaka Matsuo},
year={2023},
}""").lstrip(),
},
"utokyo_xarm_pick_and_place": {
"tasks_col": "language_instruction",
"license": "cc-by-4.0",
"citation_bibtex": dedent(r"""
@misc{matsushima2023weblab,
title={Weblab xArm Dataset},
author={Tatsuya Matsushima and Hiroki Furuta and Yusuke Iwasawa and Yutaka Matsuo},
year={2023},
}""").lstrip(),
},
"viola": {
"tasks_col": "language_instruction",
"license": "mit",
"url": "https://ut-austin-rpl.github.io/VIOLA/",
"paper": "https://arxiv.org/abs/2210.11339",
"citation_bibtex": dedent(r"""
@article{zhu2022viola,
title={VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors},
author={Zhu, Yifeng and Joshi, Abhishek and Stone, Peter and Zhu, Yuke},
journal={6th Annual Conference on Robot Learning (CoRL)},
year={2022}
}""").lstrip(),
},
}
def batch_convert():
status = {}
logfile = LOCAL_DIR / "conversion_log.txt"
assert set(DATASETS) == {id_.split("/")[1] for id_ in available_datasets}
for num, (name, kwargs) in enumerate(DATASETS.items()):
repo_id = f"lerobot/{name}"
print(f"\nConverting {repo_id} ({num}/{len(DATASETS)})")
print("---------------------------------------------------------")
try:
convert_dataset(repo_id, LOCAL_DIR, **kwargs)
status = f"{repo_id}: success."
with open(logfile, "a") as file:
file.write(status + "\n")
except Exception:
status = f"{repo_id}: failed\n {traceback.format_exc()}"
with open(logfile, "a") as file:
file.write(status + "\n")
continue
if __name__ == "__main__":
batch_convert()

665
lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py Normal file
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@@ -0,0 +1,665 @@
#!/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.
"""
This script will help you convert any LeRobot dataset already pushed to the hub from codebase version 1.6 to
2.0. You will be required to provide the 'tasks', which is a short but accurate description in plain English
for each of the task performed in the dataset. This will allow to easily train models with task-conditionning.
We support 3 different scenarios for these tasks (see instructions below):
1. Single task dataset: all episodes of your dataset have the same single task.
2. Single task episodes: the episodes of your dataset each contain a single task but they can differ from
one episode to the next.
3. Multi task episodes: episodes of your dataset may each contain several different tasks.
Can you can also provide a robot config .yaml file (not mandatory) to this script via the option
'--robot-config' so that it writes information about the robot (robot type, motors names) this dataset was
recorded with. For now, only Aloha/Koch type robots are supported with this option.
# 1. Single task dataset
If your dataset contains a single task, you can simply provide it directly via the CLI with the
'--single-task' option.
Examples:
```bash
python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
--repo-id lerobot/aloha_sim_insertion_human_image \
--single-task "Insert the peg into the socket." \
--robot-config lerobot/configs/robot/aloha.yaml \
--local-dir data
```
```bash
python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
--repo-id aliberts/koch_tutorial \
--single-task "Pick the Lego block and drop it in the box on the right." \
--robot-config lerobot/configs/robot/koch.yaml \
--local-dir data
```
# 2. Single task episodes
If your dataset is a multi-task dataset, you have two options to provide the tasks to this script:
- If your dataset already contains a language instruction column in its parquet file, you can simply provide
this column's name with the '--tasks-col' arg.
Example:
```bash
python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
--repo-id lerobot/stanford_kuka_multimodal_dataset \
--tasks-col "language_instruction" \
--local-dir data
```
- If your dataset doesn't contain a language instruction, you should provide the path to a .json file with the
'--tasks-path' arg. This file should have the following structure where keys correspond to each
episode_index in the dataset, and values are the language instruction for that episode.
Example:
```json
{
"0": "Do something",
"1": "Do something else",
"2": "Do something",
"3": "Go there",
...
}
```
# 3. Multi task episodes
If you have multiple tasks per episodes, your dataset should contain a language instruction column in its
parquet file, and you must provide this column's name with the '--tasks-col' arg.
Example:
```bash
python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \
--repo-id lerobot/stanford_kuka_multimodal_dataset \
--tasks-col "language_instruction" \
--local-dir data
```
"""
import argparse
import contextlib
import filecmp
import json
import logging
import math
import shutil
import subprocess
import tempfile
from pathlib import Path
import datasets
import pyarrow.compute as pc
import pyarrow.parquet as pq
import torch
from datasets import Dataset
from huggingface_hub import HfApi
from huggingface_hub.errors import EntryNotFoundError, HfHubHTTPError
from safetensors.torch import load_file
from lerobot.common.datasets.utils import (
DEFAULT_CHUNK_SIZE,
DEFAULT_PARQUET_PATH,
DEFAULT_VIDEO_PATH,
EPISODES_PATH,
INFO_PATH,
STATS_PATH,
TASKS_PATH,
create_branch,
create_lerobot_dataset_card,
flatten_dict,
get_hub_safe_version,
load_json,
unflatten_dict,
write_json,
write_jsonlines,
)
from lerobot.common.datasets.video_utils import (
VideoFrame, # noqa: F401
get_image_pixel_channels,
get_video_info,
)
from lerobot.common.utils.utils import init_hydra_config
V16 = "v1.6"
V20 = "v2.0"
GITATTRIBUTES_REF = "aliberts/gitattributes_reference"
V1_VIDEO_FILE = "{video_key}_episode_{episode_index:06d}.mp4"
V1_INFO_PATH = "meta_data/info.json"
V1_STATS_PATH = "meta_data/stats.safetensors"
def parse_robot_config(config_path: Path, config_overrides: list[str] | None = None) -> tuple[str, dict]:
robot_cfg = init_hydra_config(config_path, config_overrides)
if robot_cfg["robot_type"] in ["aloha", "koch"]:
state_names = [
f"{arm}_{motor}" if len(robot_cfg["follower_arms"]) > 1 else motor
for arm in robot_cfg["follower_arms"]
for motor in robot_cfg["follower_arms"][arm]["motors"]
]
action_names = [
# f"{arm}_{motor}" for arm in ["left", "right"] for motor in robot_cfg["leader_arms"][arm]["motors"]
f"{arm}_{motor}" if len(robot_cfg["leader_arms"]) > 1 else motor
for arm in robot_cfg["leader_arms"]
for motor in robot_cfg["leader_arms"][arm]["motors"]
]
# elif robot_cfg["robot_type"] == "stretch3": TODO
else:
raise NotImplementedError(
"Please provide robot_config={'robot_type': ..., 'names': ...} directly to convert_dataset()."
)
return {
"robot_type": robot_cfg["robot_type"],
"names": {
"observation.state": state_names,
"observation.effort": state_names,
"action": action_names,
},
}
def convert_stats_to_json(v1_dir: Path, v2_dir: Path) -> None:
safetensor_path = v1_dir / V1_STATS_PATH
stats = load_file(safetensor_path)
serialized_stats = {key: value.tolist() for key, value in stats.items()}
serialized_stats = unflatten_dict(serialized_stats)
json_path = v2_dir / STATS_PATH
json_path.parent.mkdir(exist_ok=True, parents=True)
with open(json_path, "w") as f:
json.dump(serialized_stats, f, indent=4)
# Sanity check
with open(json_path) as f:
stats_json = json.load(f)
stats_json = flatten_dict(stats_json)
stats_json = {key: torch.tensor(value) for key, value in stats_json.items()}
for key in stats:
torch.testing.assert_close(stats_json[key], stats[key])
def get_features_from_hf_dataset(dataset: Dataset, robot_config: dict | None = None) -> dict[str, list]:
features = {}
for key, ft in dataset.features.items():
if isinstance(ft, datasets.Value):
dtype = ft.dtype
shape = (1,)
names = None
if isinstance(ft, datasets.Sequence):
assert isinstance(ft.feature, datasets.Value)
dtype = ft.feature.dtype
shape = (ft.length,)
motor_names = (
robot_config["names"][key] if robot_config else [f"motor_{i}" for i in range(ft.length)]
)
assert len(motor_names) == shape[0]
names = {"motors": motor_names}
elif isinstance(ft, datasets.Image):
dtype = "image"
image = dataset[0][key] # Assuming first row
channels = get_image_pixel_channels(image)
shape = (image.height, image.width, channels)
names = ["height", "width", "channel"]
elif ft._type == "VideoFrame":
dtype = "video"
shape = None # Add shape later
names = ["height", "width", "channel"]
features[key] = {
"dtype": dtype,
"shape": shape,
"names": names,
}
return features
def add_task_index_by_episodes(dataset: Dataset, tasks_by_episodes: dict) -> tuple[Dataset, list[str]]:
df = dataset.to_pandas()
tasks = list(set(tasks_by_episodes.values()))
tasks_to_task_index = {task: task_idx for task_idx, task in enumerate(tasks)}
episodes_to_task_index = {ep_idx: tasks_to_task_index[task] for ep_idx, task in tasks_by_episodes.items()}
df["task_index"] = df["episode_index"].map(episodes_to_task_index).astype(int)
features = dataset.features
features["task_index"] = datasets.Value(dtype="int64")
dataset = Dataset.from_pandas(df, features=features, split="train")
return dataset, tasks
def add_task_index_from_tasks_col(
dataset: Dataset, tasks_col: str
) -> tuple[Dataset, dict[str, list[str]], list[str]]:
df = dataset.to_pandas()
# HACK: This is to clean some of the instructions in our version of Open X datasets
prefix_to_clean = "tf.Tensor(b'"
suffix_to_clean = "', shape=(), dtype=string)"
df[tasks_col] = df[tasks_col].str.removeprefix(prefix_to_clean).str.removesuffix(suffix_to_clean)
# Create task_index col
tasks_by_episode = df.groupby("episode_index")[tasks_col].unique().apply(lambda x: x.tolist()).to_dict()
tasks = df[tasks_col].unique().tolist()
tasks_to_task_index = {task: idx for idx, task in enumerate(tasks)}
df["task_index"] = df[tasks_col].map(tasks_to_task_index).astype(int)
# Build the dataset back from df
features = dataset.features
features["task_index"] = datasets.Value(dtype="int64")
dataset = Dataset.from_pandas(df, features=features, split="train")
dataset = dataset.remove_columns(tasks_col)
return dataset, tasks, tasks_by_episode
def split_parquet_by_episodes(
dataset: Dataset,
total_episodes: int,
total_chunks: int,
output_dir: Path,
) -> list:
table = dataset.data.table
episode_lengths = []
for ep_chunk in range(total_chunks):
ep_chunk_start = DEFAULT_CHUNK_SIZE * ep_chunk
ep_chunk_end = min(DEFAULT_CHUNK_SIZE * (ep_chunk + 1), total_episodes)
chunk_dir = "/".join(DEFAULT_PARQUET_PATH.split("/")[:-1]).format(episode_chunk=ep_chunk)
(output_dir / chunk_dir).mkdir(parents=True, exist_ok=True)
for ep_idx in range(ep_chunk_start, ep_chunk_end):
ep_table = table.filter(pc.equal(table["episode_index"], ep_idx))
episode_lengths.insert(ep_idx, len(ep_table))
output_file = output_dir / DEFAULT_PARQUET_PATH.format(
episode_chunk=ep_chunk, episode_index=ep_idx
)
pq.write_table(ep_table, output_file)
return episode_lengths
def move_videos(
repo_id: str,
video_keys: list[str],
total_episodes: int,
total_chunks: int,
work_dir: Path,
clean_gittatributes: Path,
branch: str = "main",
) -> None:
"""
HACK: Since HfApi() doesn't provide a way to move files directly in a repo, this function will run git
commands to fetch git lfs video files references to move them into subdirectories without having to
actually download them.
"""
_lfs_clone(repo_id, work_dir, branch)
videos_moved = False
video_files = [str(f.relative_to(work_dir)) for f in work_dir.glob("videos*/*.mp4")]
if len(video_files) == 0:
video_files = [str(f.relative_to(work_dir)) for f in work_dir.glob("videos*/*/*/*.mp4")]
videos_moved = True # Videos have already been moved
assert len(video_files) == total_episodes * len(video_keys)
lfs_untracked_videos = _get_lfs_untracked_videos(work_dir, video_files)
current_gittatributes = work_dir / ".gitattributes"
if not filecmp.cmp(current_gittatributes, clean_gittatributes, shallow=False):
fix_gitattributes(work_dir, current_gittatributes, clean_gittatributes)
if lfs_untracked_videos:
fix_lfs_video_files_tracking(work_dir, video_files)
if videos_moved:
return
video_dirs = sorted(work_dir.glob("videos*/"))
for ep_chunk in range(total_chunks):
ep_chunk_start = DEFAULT_CHUNK_SIZE * ep_chunk
ep_chunk_end = min(DEFAULT_CHUNK_SIZE * (ep_chunk + 1), total_episodes)
for vid_key in video_keys:
chunk_dir = "/".join(DEFAULT_VIDEO_PATH.split("/")[:-1]).format(
episode_chunk=ep_chunk, video_key=vid_key
)
(work_dir / chunk_dir).mkdir(parents=True, exist_ok=True)
for ep_idx in range(ep_chunk_start, ep_chunk_end):
target_path = DEFAULT_VIDEO_PATH.format(
episode_chunk=ep_chunk, video_key=vid_key, episode_index=ep_idx
)
video_file = V1_VIDEO_FILE.format(video_key=vid_key, episode_index=ep_idx)
if len(video_dirs) == 1:
video_path = video_dirs[0] / video_file
else:
for dir in video_dirs:
if (dir / video_file).is_file():
video_path = dir / video_file
break
video_path.rename(work_dir / target_path)
commit_message = "Move video files into chunk subdirectories"
subprocess.run(["git", "add", "."], cwd=work_dir, check=True)
subprocess.run(["git", "commit", "-m", commit_message], cwd=work_dir, check=True)
subprocess.run(["git", "push"], cwd=work_dir, check=True)
def fix_lfs_video_files_tracking(work_dir: Path, lfs_untracked_videos: list[str]) -> None:
"""
HACK: This function fixes the tracking by git lfs which was not properly set on some repos. In that case,
there's no other option than to download the actual files and reupload them with lfs tracking.
"""
for i in range(0, len(lfs_untracked_videos), 100):
files = lfs_untracked_videos[i : i + 100]
try:
subprocess.run(["git", "rm", "--cached", *files], cwd=work_dir, capture_output=True, check=True)
except subprocess.CalledProcessError as e:
print("git rm --cached ERROR:")
print(e.stderr)
subprocess.run(["git", "add", *files], cwd=work_dir, check=True)
commit_message = "Track video files with git lfs"
subprocess.run(["git", "commit", "-m", commit_message], cwd=work_dir, check=True)
subprocess.run(["git", "push"], cwd=work_dir, check=True)
def fix_gitattributes(work_dir: Path, current_gittatributes: Path, clean_gittatributes: Path) -> None:
shutil.copyfile(clean_gittatributes, current_gittatributes)
subprocess.run(["git", "add", ".gitattributes"], cwd=work_dir, check=True)
subprocess.run(["git", "commit", "-m", "Fix .gitattributes"], cwd=work_dir, check=True)
subprocess.run(["git", "push"], cwd=work_dir, check=True)
def _lfs_clone(repo_id: str, work_dir: Path, branch: str) -> None:
subprocess.run(["git", "lfs", "install"], cwd=work_dir, check=True)
repo_url = f"https://huggingface.co/datasets/{repo_id}"
env = {"GIT_LFS_SKIP_SMUDGE": "1"} # Prevent downloading LFS files
subprocess.run(
["git", "clone", "--branch", branch, "--single-branch", "--depth", "1", repo_url, str(work_dir)],
check=True,
env=env,
)
def _get_lfs_untracked_videos(work_dir: Path, video_files: list[str]) -> list[str]:
lfs_tracked_files = subprocess.run(
["git", "lfs", "ls-files", "-n"], cwd=work_dir, capture_output=True, text=True, check=True
)
lfs_tracked_files = set(lfs_tracked_files.stdout.splitlines())
return [f for f in video_files if f not in lfs_tracked_files]
def get_videos_info(repo_id: str, local_dir: Path, video_keys: list[str], branch: str) -> dict:
# Assumes first episode
video_files = [
DEFAULT_VIDEO_PATH.format(episode_chunk=0, video_key=vid_key, episode_index=0)
for vid_key in video_keys
]
hub_api = HfApi()
hub_api.snapshot_download(
repo_id=repo_id, repo_type="dataset", local_dir=local_dir, revision=branch, allow_patterns=video_files
)
videos_info_dict = {}
for vid_key, vid_path in zip(video_keys, video_files, strict=True):
videos_info_dict[vid_key] = get_video_info(local_dir / vid_path)
return videos_info_dict
def convert_dataset(
repo_id: str,
local_dir: Path,
single_task: str | None = None,
tasks_path: Path | None = None,
tasks_col: Path | None = None,
robot_config: dict | None = None,
test_branch: str | None = None,
**card_kwargs,
):
v1 = get_hub_safe_version(repo_id, V16)
v1x_dir = local_dir / V16 / repo_id
v20_dir = local_dir / V20 / repo_id
v1x_dir.mkdir(parents=True, exist_ok=True)
v20_dir.mkdir(parents=True, exist_ok=True)
hub_api = HfApi()
hub_api.snapshot_download(
repo_id=repo_id, repo_type="dataset", revision=v1, local_dir=v1x_dir, ignore_patterns="videos*/"
)
branch = "main"
if test_branch:
branch = test_branch
create_branch(repo_id=repo_id, branch=test_branch, repo_type="dataset")
metadata_v1 = load_json(v1x_dir / V1_INFO_PATH)
dataset = datasets.load_dataset("parquet", data_dir=v1x_dir / "data", split="train")
features = get_features_from_hf_dataset(dataset, robot_config)
video_keys = [key for key, ft in features.items() if ft["dtype"] == "video"]
if single_task and "language_instruction" in dataset.column_names:
logging.warning(
"'single_task' provided but 'language_instruction' tasks_col found. Using 'language_instruction'.",
)
single_task = None
tasks_col = "language_instruction"
# Episodes & chunks
episode_indices = sorted(dataset.unique("episode_index"))
total_episodes = len(episode_indices)
assert episode_indices == list(range(total_episodes))
total_videos = total_episodes * len(video_keys)
total_chunks = total_episodes // DEFAULT_CHUNK_SIZE
if total_episodes % DEFAULT_CHUNK_SIZE != 0:
total_chunks += 1
# Tasks
if single_task:
tasks_by_episodes = {ep_idx: single_task for ep_idx in episode_indices}
dataset, tasks = add_task_index_by_episodes(dataset, tasks_by_episodes)
tasks_by_episodes = {ep_idx: [task] for ep_idx, task in tasks_by_episodes.items()}
elif tasks_path:
tasks_by_episodes = load_json(tasks_path)
tasks_by_episodes = {int(ep_idx): task for ep_idx, task in tasks_by_episodes.items()}
dataset, tasks = add_task_index_by_episodes(dataset, tasks_by_episodes)
tasks_by_episodes = {ep_idx: [task] for ep_idx, task in tasks_by_episodes.items()}
elif tasks_col:
dataset, tasks, tasks_by_episodes = add_task_index_from_tasks_col(dataset, tasks_col)
else:
raise ValueError
assert set(tasks) == {task for ep_tasks in tasks_by_episodes.values() for task in ep_tasks}
tasks = [{"task_index": task_idx, "task": task} for task_idx, task in enumerate(tasks)]
write_jsonlines(tasks, v20_dir / TASKS_PATH)
features["task_index"] = {
"dtype": "int64",
"shape": (1,),
"names": None,
}
# Videos
if video_keys:
assert metadata_v1.get("video", False)
dataset = dataset.remove_columns(video_keys)
clean_gitattr = Path(
hub_api.hf_hub_download(
repo_id=GITATTRIBUTES_REF, repo_type="dataset", local_dir=local_dir, filename=".gitattributes"
)
).absolute()
with tempfile.TemporaryDirectory() as tmp_video_dir:
move_videos(
repo_id, video_keys, total_episodes, total_chunks, Path(tmp_video_dir), clean_gitattr, branch
)
videos_info = get_videos_info(repo_id, v1x_dir, video_keys=video_keys, branch=branch)
for key in video_keys:
features[key]["shape"] = (
videos_info[key].pop("video.height"),
videos_info[key].pop("video.width"),
videos_info[key].pop("video.channels"),
)
features[key]["video_info"] = videos_info[key]
assert math.isclose(videos_info[key]["video.fps"], metadata_v1["fps"], rel_tol=1e-3)
if "encoding" in metadata_v1:
assert videos_info[key]["video.pix_fmt"] == metadata_v1["encoding"]["pix_fmt"]
else:
assert metadata_v1.get("video", 0) == 0
videos_info = None
# Split data into 1 parquet file by episode
episode_lengths = split_parquet_by_episodes(dataset, total_episodes, total_chunks, v20_dir)
if robot_config is not None:
robot_type = robot_config["robot_type"]
repo_tags = [robot_type]
else:
robot_type = "unknown"
repo_tags = None
# Episodes
episodes = [
{"episode_index": ep_idx, "tasks": tasks_by_episodes[ep_idx], "length": episode_lengths[ep_idx]}
for ep_idx in episode_indices
]
write_jsonlines(episodes, v20_dir / EPISODES_PATH)
# Assemble metadata v2.0
metadata_v2_0 = {
"codebase_version": V20,
"robot_type": robot_type,
"total_episodes": total_episodes,
"total_frames": len(dataset),
"total_tasks": len(tasks),
"total_videos": total_videos,
"total_chunks": total_chunks,
"chunks_size": DEFAULT_CHUNK_SIZE,
"fps": metadata_v1["fps"],
"splits": {"train": f"0:{total_episodes}"},
"data_path": DEFAULT_PARQUET_PATH,
"video_path": DEFAULT_VIDEO_PATH if video_keys else None,
"features": features,
}
write_json(metadata_v2_0, v20_dir / INFO_PATH)
convert_stats_to_json(v1x_dir, v20_dir)
card = create_lerobot_dataset_card(tags=repo_tags, dataset_info=metadata_v2_0, **card_kwargs)
with contextlib.suppress(EntryNotFoundError, HfHubHTTPError):
hub_api.delete_folder(repo_id=repo_id, path_in_repo="data", repo_type="dataset", revision=branch)
with contextlib.suppress(EntryNotFoundError, HfHubHTTPError):
hub_api.delete_folder(repo_id=repo_id, path_in_repo="meta_data", repo_type="dataset", revision=branch)
with contextlib.suppress(EntryNotFoundError, HfHubHTTPError):
hub_api.delete_folder(repo_id=repo_id, path_in_repo="meta", repo_type="dataset", revision=branch)
hub_api.upload_folder(
repo_id=repo_id,
path_in_repo="data",
folder_path=v20_dir / "data",
repo_type="dataset",
revision=branch,
)
hub_api.upload_folder(
repo_id=repo_id,
path_in_repo="meta",
folder_path=v20_dir / "meta",
repo_type="dataset",
revision=branch,
)
card.push_to_hub(repo_id=repo_id, repo_type="dataset", revision=branch)
if not test_branch:
create_branch(repo_id=repo_id, branch=V20, repo_type="dataset")
def main():
parser = argparse.ArgumentParser()
task_args = parser.add_mutually_exclusive_group(required=True)
parser.add_argument(
"--repo-id",
type=str,
required=True,
help="Repository identifier on Hugging Face: a community or a user name `/` the name of the dataset (e.g. `lerobot/pusht`, `cadene/aloha_sim_insertion_human`).",
)
task_args.add_argument(
"--single-task",
type=str,
help="A short but accurate description of the single task performed in the dataset.",
)
task_args.add_argument(
"--tasks-col",
type=str,
help="The name of the column containing language instructions",
)
task_args.add_argument(
"--tasks-path",
type=Path,
help="The path to a .json file containing one language instruction for each episode_index",
)
parser.add_argument(
"--robot-config",
type=Path,
default=None,
help="Path to the robot's config yaml the dataset during conversion.",
)
parser.add_argument(
"--robot-overrides",
type=str,
nargs="*",
help="Any key=value arguments to override the robot config values (use dots for.nested=overrides)",
)
parser.add_argument(
"--local-dir",
type=Path,
default=None,
help="Local directory to store the dataset during conversion. Defaults to /tmp/lerobot_dataset_v2",
)
parser.add_argument(
"--license",
type=str,
default="apache-2.0",
help="Repo license. Must be one of https://huggingface.co/docs/hub/repositories-licenses. Defaults to mit.",
)
parser.add_argument(
"--test-branch",
type=str,
default=None,
help="Repo branch to test your conversion first (e.g. 'v2.0.test')",
)
args = parser.parse_args()
if not args.local_dir:
args.local_dir = Path("/tmp/lerobot_dataset_v2")
robot_config = parse_robot_config(args.robot_config, args.robot_overrides) if args.robot_config else None
del args.robot_config, args.robot_overrides
convert_dataset(**vars(args), robot_config=robot_config)
if __name__ == "__main__":
main()

146
lerobot/common/datasets/video_utils.py
View File

@@ -13,6 +13,7 @@
# 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 json
import logging
import subprocess
import warnings
@@ -25,47 +26,11 @@ import pyarrow as pa
import torch
import torchvision
from datasets.features.features import register_feature
def load_from_videos(
item: dict[str, torch.Tensor],
video_frame_keys: list[str],
videos_dir: Path,
tolerance_s: float,
backend: str = "pyav",
):
"""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.
"""
# since video path already contains "videos" (e.g. videos_dir="data/videos", path="videos/episode_0.mp4")
data_dir = videos_dir.parent
for key in video_frame_keys:
if isinstance(item[key], list):
# load multiple frames at once (expected when delta_timestamps is not None)
timestamps = [frame["timestamp"] for frame in item[key]]
paths = [frame["path"] for frame in item[key]]
if len(set(paths)) > 1:
raise NotImplementedError("All video paths are expected to be the same for now.")
video_path = data_dir / paths[0]
frames = decode_video_frames_torchvision(video_path, timestamps, tolerance_s, backend)
item[key] = frames
else:
# load one frame
timestamps = [item[key]["timestamp"]]
video_path = data_dir / item[key]["path"]
frames = decode_video_frames_torchvision(video_path, timestamps, tolerance_s, backend)
item[key] = frames[0]
return item
from PIL import Image
def decode_video_frames_torchvision(
video_path: str,
video_path: Path | str,
timestamps: list[float],
tolerance_s: float,
backend: str = "pyav",
@@ -163,8 +128,8 @@ def decode_video_frames_torchvision(
def encode_video_frames(
imgs_dir: Path,
video_path: Path,
imgs_dir: Path | str,
video_path: Path | str,
fps: int,
vcodec: str = "libsvtav1",
pix_fmt: str = "yuv420p",
@@ -247,3 +212,104 @@ with warnings.catch_warnings():
)
# to make VideoFrame available in HuggingFace `datasets`
register_feature(VideoFrame, "VideoFrame")
def get_audio_info(video_path: Path | str) -> dict:
ffprobe_audio_cmd = [
"ffprobe",
"-v",
"error",
"-select_streams",
"a:0",
"-show_entries",
"stream=channels,codec_name,bit_rate,sample_rate,bit_depth,channel_layout,duration",
"-of",
"json",
str(video_path),
]
result = subprocess.run(ffprobe_audio_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
if result.returncode != 0:
raise RuntimeError(f"Error running ffprobe: {result.stderr}")
info = json.loads(result.stdout)
audio_stream_info = info["streams"][0] if info.get("streams") else None
if audio_stream_info is None:
return {"has_audio": False}
# Return the information, defaulting to None if no audio stream is present
return {
"has_audio": True,
"audio.channels": audio_stream_info.get("channels", None),
"audio.codec": audio_stream_info.get("codec_name", None),
"audio.bit_rate": int(audio_stream_info["bit_rate"]) if audio_stream_info.get("bit_rate") else None,
"audio.sample_rate": int(audio_stream_info["sample_rate"])
if audio_stream_info.get("sample_rate")
else None,
"audio.bit_depth": audio_stream_info.get("bit_depth", None),
"audio.channel_layout": audio_stream_info.get("channel_layout", None),
}
def get_video_info(video_path: Path | str) -> dict:
ffprobe_video_cmd = [
"ffprobe",
"-v",
"error",
"-select_streams",
"v:0",
"-show_entries",
"stream=r_frame_rate,width,height,codec_name,nb_frames,duration,pix_fmt",
"-of",
"json",
str(video_path),
]
result = subprocess.run(ffprobe_video_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
if result.returncode != 0:
raise RuntimeError(f"Error running ffprobe: {result.stderr}")
info = json.loads(result.stdout)
video_stream_info = info["streams"][0]
# Calculate fps from r_frame_rate
r_frame_rate = video_stream_info["r_frame_rate"]
num, denom = map(int, r_frame_rate.split("/"))
fps = num / denom
pixel_channels = get_video_pixel_channels(video_stream_info["pix_fmt"])
video_info = {
"video.fps": fps,
"video.height": video_stream_info["height"],
"video.width": video_stream_info["width"],
"video.channels": pixel_channels,
"video.codec": video_stream_info["codec_name"],
"video.pix_fmt": video_stream_info["pix_fmt"],
"video.is_depth_map": False,
**get_audio_info(video_path),
}
return video_info
def get_video_pixel_channels(pix_fmt: str) -> int:
if "gray" in pix_fmt or "depth" in pix_fmt or "monochrome" in pix_fmt:
return 1
elif "rgba" in pix_fmt or "yuva" in pix_fmt:
return 4
elif "rgb" in pix_fmt or "yuv" in pix_fmt:
return 3
else:
raise ValueError("Unknown format")
def get_image_pixel_channels(image: Image):
if image.mode == "L":
return 1 # Grayscale
elif image.mode == "LA":
return 2 # Grayscale + Alpha
elif image.mode == "RGB":
return 3 # RGB
elif image.mode == "RGBA":
return 4 # RGBA
else:
raise ValueError("Unknown format")