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import json
import random
import tarfile
from contextlib import contextmanager
import tempfile
import pickle
import base64
try:
import h5py
import imageio
import requests
import zstandard as zstd
from datasets import load_dataset
from huggingface_hub import hf_hub_url, HfFolder
except ImportError:
print(
"Please setup your environment with e.g."
" `pip install h5py 'imageio[ffmpeg]' requests zstandard datasets huggingface_hub`"
)
raise
TOKEN = HfFolder.get_token()
REPO = "allenai/molmobot-data"
TASK_CONFIGS = [
"DoorOpeningDataGenConfig",
"FrankaPickAndPlaceColorOmniCamConfig",
"FrankaPickAndPlaceNextToOmniCamConfig",
"FrankaPickAndPlaceOmniCamConfig",
"FrankaPickOmniCamConfig",
"RBY1OpenDataGenConfig",
"RBY1PickAndPlaceDataGenConfig",
"RBY1PickDataGenConfig",
"FrankaPickAndPlaceOmniCamConfig_ObjectBackfill",
]
SPLIT = "train"
@contextmanager
def stream_pkg(
entry: dict, config_name: str, buffer_size: int = 8192, repo_id: str = REPO
):
"""
Streams a single compressed archive (tar.zst) from within a shard using
an HTTP Range request. Each shard contains multiple archives packed
contiguously; the entry's offset and size identify the byte range for
one archive. This context manager exposes an open tarfile.
"""
url = hf_hub_url(
repo_id=repo_id,
filename=f"{config_name}/{SPLIT}_shards/{entry['shard_id']:05d}.tar",
repo_type="dataset",
revision="main",
)
start = entry["offset"]
end = start + entry["size"] - 1
headers = {"Range": f"bytes={start}-{end}"}
if TOKEN:
headers["Authorization"] = f"Bearer {TOKEN}"
with requests.get(url, headers=headers, stream=True) as response:
response.raise_for_status()
dctx = zstd.ZstdDecompressor()
with dctx.stream_reader(response.raw) as reader:
buffered = io.BufferedReader(reader, buffer_size=buffer_size)
with tarfile.open(fileobj=buffered, mode="r|") as tar:
yield tar
def collect_scene_data(entry: dict, config_name: str, keep_videos=True):
"""Collects scene identification info, and h5 and mp4 buffers from the archive."""
def keep_scene_info(name):
return f'part{entry["part"]}_{name.split("/")[0]}'
def mp4_info(name):
info = name.split("/")[1].split(".")[0]
traj_cam_info, batch_info = info.split("_batch_")
traj_cam_info = traj_cam_info.split("_")
traj_idx = int(traj_cam_info[1])
cam = "_".join(traj_cam_info[2:])
return batch_info, traj_idx, cam
def h5_info(name):
info = name.split("/")[1].split(".")[0]
batch_info = info.split("_batch_")[1]
return batch_info
scene_info = None
batch_to_h5_and_ep_to_cams = {}
with stream_pkg(entry, config_name) as tar:
for member in tar:
if member.name.endswith(".h5"):
batch = h5_info(member.name)
if scene_info is None:
scene_info = keep_scene_info(member.name)
if batch not in batch_to_h5_and_ep_to_cams:
batch_to_h5_and_ep_to_cams[batch] = {}
batch_to_h5_and_ep_to_cams[batch]["h5"] = tar.extractfile(member).read()
elif member.name.endswith(".mp4") and keep_videos:
batch, ep, cam = mp4_info(member.name)
if batch not in batch_to_h5_and_ep_to_cams:
batch_to_h5_and_ep_to_cams[batch] = {}
if ep not in batch_to_h5_and_ep_to_cams[batch]:
batch_to_h5_and_ep_to_cams[batch][ep] = {}
batch_to_h5_and_ep_to_cams[batch][ep][cam] = tar.extractfile(
member
).read()
return scene_info, batch_to_h5_and_ep_to_cams
def pop_frames(h5_and_ep_to_cams, eid):
"""Pops video buffers for an episode and decodes them into numpy frame arrays."""
frames = {}
for cam in list(h5_and_ep_to_cams[eid].keys()):
vid_data = h5_and_ep_to_cams[eid].pop(cam)
# imageio ffmpeg reader needs
with tempfile.NamedTemporaryFile(suffix=".mp4") as tmp:
tmp.write(vid_data)
# ensure data is written to disk
tmp.flush()
tmp.seek(0)
with imageio.get_reader(tmp.name, format="ffmpeg") as vid:
frames[cam] = [frame for frame in vid]
return frames
def decode_datum(datum):
"""Decodes a null-padded JSON bytes array into a Python object."""
return json.loads(datum.tobytes().decode("utf-8").rstrip("\x00"))
class Config:
"""Generic placeholder for unpickling config classes."""
def __init__(self, *args, **kwargs):
self._args = args
self._kwargs = kwargs
def __setstate__(self, state):
self.__dict__ = state["__dict__"]
def __repr__(self):
return f"{self.__dict__}"
class ConfigUnpickler(pickle.Unpickler):
"""Unpickler that resolves numpy/pathlib classes normally and stubs everything else."""
def find_class(self, module, name):
if module.startswith(("numpy", "pathlib")):
import importlib
loaded = importlib.import_module(module)
return getattr(loaded, name)
return Config
def safe_load_config(encoded_frozen_config):
"""
Deserializes a base64-encoded pickled config, replacing unknown
classes with a generic Config placeholder. Returns None on failure.
"""
try:
return ConfigUnpickler(
io.BytesIO(base64.b64decode(encoded_frozen_config))
).load()
except Exception as e:
print(f"Warning: config pickle could not be fully loaded: {e}")
return None
def iterate_data(entry: dict, config_name: str):
"""
Yields per-step dicts (traj_info, step, action, camera frames)
for all valid episodes in the given scene package.
"""
scene_info, batch_to_h5_and_ep_to_cams = collect_scene_data(entry, config_name)
for batch in list(batch_to_h5_and_ep_to_cams.keys()):
h5_and_ep_to_cams = batch_to_h5_and_ep_to_cams.pop(batch)
if "h5" not in h5_and_ep_to_cams:
# Incomplete data, skip
continue
h5 = h5_and_ep_to_cams["h5"]
with h5py.File(io.BytesIO(h5), "r") as f:
if "valid_traj_mask" in f.keys():
valid_traj_mask = f["valid_traj_mask"][()]
else:
traj_keys = {
int(key.split("traj_")[-1])
for key in f.keys()
if key.startswith("traj_")
}
valid_traj_mask = [
True if idx in traj_keys else False
for idx in range(max(traj_keys) + 1)
]
for eid, val in enumerate(valid_traj_mask):
if not val:
# Skip non-valid trajectories
continue
traj = f[f"traj_{eid}"]
obs_scene = json.loads(traj["obs_scene"][()].decode())
obs_scene["config"] = safe_load_config(obs_scene.pop("frozen_config"))
obs_scene["scene_id"] = scene_info
obs_scene["traj_id"] = f"{batch}_ep{eid}"
actions = [
decode_datum(action)
for action in traj["actions"]["commanded_action"][()]
]
frames = pop_frames(h5_and_ep_to_cams, eid)
for fid, action in enumerate(actions):
yield {
"traj_info": obs_scene,
"step": fid,
"action": action,
**{cam: frames[cam][fid] for cam in frames},
}
def main():
grand_size = 0
grand_inflated = 0
grand_largest = 0
grand_parts = 0
for config in TASK_CONFIGS:
ds = load_dataset(REPO, name=config, split=f"{SPLIT}_pkgs")
current_size = sum(row["size"] for row in ds)
current_inflated = sum(row["inflated_size"] for row in ds)
current_largest = max(row["inflated_size"] for row in ds)
current_parts = len(set(row["part"] for row in ds))
print(f"Task config {config}:")
print(f" Compressed: {current_size / 1024 ** 3:.2f} GiB")
print(f" Inflated: {current_inflated / 1024 ** 3:.2f} GiB")
print(f" Largest archive: {current_largest / 1024 ** 3:.2f} GiB")
print(f" Collection parts: {current_parts}")
grand_size += current_size
grand_inflated += current_inflated
grand_largest = max(current_largest, grand_largest)
grand_parts += current_parts
parts = [[] for _ in range(current_parts)]
for entry in ds:
parts[entry["part"]].append(entry)
for part in parts:
random_scene_pkg = random.choice(part)
for it, item in enumerate(iterate_data(random_scene_pkg, config)):
if it == 1:
info = item.pop("traj_info")
scene_id = info["scene_id"]
traj_id = info["traj_id"]
task_type = info["task_type"]
task_description = info["task_description"]
robot_name = info["config"].robot_config.name
step = item.pop("step")
action_keys = sorted(item.pop("action").keys())
cam_shapes = {cam: frame.shape for cam, frame in item.items()}
print(
f"{step=} {traj_id=} {scene_id=}"
f"\n {task_type=}"
f"\n {task_description=}"
f"\n {robot_name=}"
f"\n {action_keys=}"
f"\n {cam_shapes=}"
)
break
print(f"TOTAL across {len(TASK_CONFIGS)} task configs:")
print(f" Compressed: {grand_size / 1024 ** 4:.2f} TiB")
print(f" Inflated: {grand_inflated / 1024 ** 4:.2f} TiB")
print(f" Largest archive: {grand_largest / 1024 ** 3:.2f} GiB")
print(f" Collection parts: {grand_parts}")
if __name__ == "__main__":
main()
print("DONE")
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