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import os
from pathlib import Path
from typing import Any, Optional
import numpy as np
from datasets import Dataset, load_dataset
from PIL import Image
from tqdm import tqdm
from dataset_upload.helpers import (
create_hf_trajectory,
generate_unique_id,
load_sentence_transformer_model,
)
def _stable_shard_for_index(index: int, shard_modulus: int = 1000) -> str:
try:
idx = int(index)
except Exception:
idx = abs(hash(str(index)))
shard_index = idx // shard_modulus
return f"shard_{shard_index:04d}"
def _build_molmo_video_paths(
output_dir: str,
dataset_label: str,
episode_idx: int,
view_key: str,
) -> tuple[str, str]:
shard_dir = _stable_shard_for_index(episode_idx)
episode_dir = os.path.join(output_dir, dataset_label.lower(), shard_dir, f"episode_{episode_idx:06d}")
os.makedirs(episode_dir, exist_ok=True)
filename = f"clip@{view_key}.mp4"
full_path = os.path.join(episode_dir, filename)
rel_path = os.path.join(dataset_label.lower(), shard_dir, f"episode_{episode_idx:06d}", filename)
return full_path, rel_path
def _to_rgb_numpy(img_cell: Any) -> Optional[np.ndarray]:
"""Convert a datasets Image cell (dict with bytes/path, PIL.Image, or np.ndarray) to RGB uint8 ndarray."""
if img_cell is None:
return None
# Already numpy HxWxC
if isinstance(img_cell, np.ndarray):
if img_cell.ndim == 3 and img_cell.shape[-1] in (1, 3, 4):
if img_cell.shape[-1] == 1:
img_cell = np.repeat(img_cell, 3, axis=-1)
elif img_cell.shape[-1] == 4:
img_cell = img_cell[..., :3]
if img_cell.dtype != np.uint8:
img_cell = img_cell.astype(np.uint8, copy=False)
return img_cell
return None
# PIL
if isinstance(img_cell, Image.Image):
return np.asarray(img_cell.convert("RGB"), dtype=np.uint8)
# dict with bytes
if isinstance(img_cell, dict):
data = img_cell.get("bytes")
if data is None:
path = img_cell.get("path")
if path and os.path.exists(path):
with Image.open(path) as im:
return np.asarray(im.convert("RGB"), dtype=np.uint8)
return None
with Image.open(io.BytesIO(data)) as im:
return np.asarray(im.convert("RGB"), dtype=np.uint8)
# Unknown
return None
def convert_molmoact_dataset_to_hf(
dataset_path: str,
dataset_name: str,
output_dir: str,
max_trajectories: int | None = None,
max_frames: int = 64,
fps: int = 10,
) -> Dataset:
"""Stream MolmoAct LeRobot (parquet) and convert to HF, using episodes.jsonl for task text.
Assumes dataset_path contains one or more subdirectories, each with parquet files and an
associated episodes.jsonl. We iterate per subdirectory to avoid episode_index collisions,
grouping rows by `episode_index` and writing videos for `first_view`, `second_view`, and `wrist_image`.
"""
root = Path(os.path.expanduser(dataset_path)) / dataset_name
if not root.exists():
raise FileNotFoundError(f"MolmoAct dataset path not found: {root}")
# Discover dataset subdirectories that have episodes.jsonl; if none, fallback to root
assert (root / "train" / "meta" / "episodes.jsonl").exists(), "episodes.jsonl not found"
# Language model and cache
lang_model = load_sentence_transformer_model()
lang_cache: dict[str, Any] = {}
entries: list[dict] = []
produced = 0
max_limit = float("inf") if (max_trajectories is None or max_trajectories == -1) else int(max_trajectories)
def load_episode_text_map(ds_dir: Path) -> dict[int, str]:
mapping: dict[int, str] = {}
jsonl_path = ds_dir / "train" / "meta" / "episodes.jsonl"
if not jsonl_path.exists():
return mapping
try:
import json
with open(jsonl_path, "r") as f:
for line in f:
line = line.strip()
if not line:
continue
try:
obj = json.loads(line)
except Exception:
continue
ep_idx = obj.get("episode_index")
if ep_idx is None:
ep_idx = obj.get("index")
if ep_idx is None:
continue
text = (obj.get("tasks"))[0]
if isinstance(text, str) and text.strip():
mapping[int(ep_idx)] = text.strip()
except Exception:
pass
return mapping
def flush_episode(ep_idx: int, task_text: str, label: str, frames_by_view: dict[str, list[np.ndarray]]) -> None:
nonlocal produced, entries
if not frames_by_view:
return
if task_text not in lang_cache:
lang_cache[task_text] = lang_model.encode(task_text)
lang_vec = lang_cache[task_text]
for view_key, frames in frames_by_view.items():
if not frames:
continue
if isinstance(frames[0], np.ndarray) and np.all(frames[0] == 0):
continue
full_path, rel_path = _build_molmo_video_paths(
output_dir=output_dir,
dataset_label=label,
episode_idx=ep_idx,
view_key=view_key,
)
traj_dict = {
"id": generate_unique_id(),
"frames": frames,
"task": task_text,
"is_robot": True,
"quality_label": "successful",
"preference_group_id": None,
"preference_rank": None,
}
entry = create_hf_trajectory(
traj_dict=traj_dict,
video_path=full_path,
lang_vector=lang_vec,
max_frames=max_frames,
dataset_name=dataset_name,
use_video=True,
fps=fps,
)
if entry:
entry["frames"] = rel_path
entries.append(entry)
produced += 1
# Process each dataset directory independently to avoid ep-index collisions
ep_text_map = load_episode_text_map(root)
# Discover parquet files in ds_dir
data_files: list[str] = []
for pat in ("**/*.parquet", "*.parquet"):
data_files.extend([str(p) for p in root.glob(pat)])
if not data_files:
raise ValueError("No parquet files found")
ds_iter = load_dataset(
"parquet",
data_files={"train": data_files},
split="train",
streaming=True,
)
current_ep: Optional[int] = None
frames_by_view: dict[str, list[np.ndarray]] = {}
label = f"{dataset_name}"
for row in tqdm(ds_iter, desc=f"MolmoAct rows ({dataset_name})"):
if produced >= max_limit:
break
ep_idx = int(row.get("episode_index", -1))
if ep_idx < 0:
continue
if current_ep is None:
current_ep = ep_idx
frames_by_view = {"first_view": [], "second_view": []}
elif ep_idx != current_ep:
task_text = ep_text_map.get(current_ep)
print(f"{task_text} episode loaded")
flush_episode(current_ep, task_text, label, frames_by_view)
current_ep = ep_idx
frames_by_view = {"first_view": [], "second_view": []}
for view_key in ("first_view", "second_view"):
cell = row.get(view_key)
img = _to_rgb_numpy(cell)
if img is not None:
frames_by_view[view_key].append(img)
if produced >= max_limit:
break
if current_ep is not None and produced < max_limit:
task_text = ep_text_map.get(current_ep)
print(f"{task_text} episode loaded")
flush_episode(current_ep, task_text, label, frames_by_view)
if not entries:
return Dataset.from_dict({
"id": [],
"task": [],
"lang_vector": [],
"data_source": [],
"frames": [],
"is_robot": [],
"quality_label": [],
"preference_group_id": [],
"preference_rank": [],
})
return Dataset.from_list(entries)
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