from pathlib import Path import jsonlines DATA_PATH = Path("/fsx/francesco_capuano/.cache/behavior-1k/2025-challenge-demos") NEW_PATH = Path("/fsx/jade_choghari/.cache/behavior-1k-task0/") import argparse import logging import shutil from pathlib import Path import pandas as pd import pyarrow as pa import tqdm from datasets import Dataset, Features, Image from huggingface_hub import HfApi, snapshot_download from requests import HTTPError from lerobot.datasets.compute_stats import aggregate_stats from lerobot.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset from lerobot.datasets.utils import ( DEFAULT_CHUNK_SIZE, DEFAULT_DATA_FILE_SIZE_IN_MB, DEFAULT_DATA_PATH, DEFAULT_VIDEO_FILE_SIZE_IN_MB, DEFAULT_VIDEO_PATH, LEGACY_EPISODES_PATH, LEGACY_EPISODES_STATS_PATH, LEGACY_TASKS_PATH, cast_stats_to_numpy, flatten_dict, get_file_size_in_mb, get_parquet_file_size_in_mb, get_parquet_num_frames, load_info, update_chunk_file_indices, write_episodes, write_info, write_stats, write_tasks, ) from lerobot.datasets.video_utils import concatenate_video_files, get_video_duration_in_s from lerobot.utils.constants import HF_LEROBOT_HOME from lerobot.utils.utils import init_logging # script to convert one single task to v3.1 # TASK = 1 NEW_ROOT = Path("/fsx/jade_choghari/tmp/bb") def get_total_episodes_task(local_dir: Path, task_id: int, task_ranges: dict, step) -> int: """ Calculates the total number of episodes for a single, specified task. """ # Simply load the episodes for the task and count them. episodes = legacy_load_episodes_task( local_dir=local_dir, task_id=task_id, task_ranges=task_ranges, step=step ) return len(episodes) NUM_CAMERAS = 9 def get_total_frames_task(local_dir, meta_path, task_id: int, task_ranges: dict, step: int) -> int: episodes_metadata = legacy_load_episodes_task( local_dir=local_dir, task_id=task_id, task_ranges=task_ranges, step=step ) total_frames = 0 # like 'duration' for ep in episodes_metadata.values(): duration_s = ep["length"] total_frames += int(duration_s) return total_frames def convert_info( root, new_root, data_file_size_in_mb, video_file_size_in_mb, meta_path, task_id: int, task_ranges, step ): info = load_info(root) info["codebase_version"] = "v3.0" del info["total_videos"] info["data_files_size_in_mb"] = data_file_size_in_mb info["video_files_size_in_mb"] = video_file_size_in_mb info["data_path"] = DEFAULT_DATA_PATH info["video_path"] = DEFAULT_VIDEO_PATH if info["video_path"] is not None else None info["fps"] = int(info["fps"]) for key in info["features"]: if info["features"][key]["dtype"] == "video": # already has fps in video_info continue info["features"][key]["fps"] = info["fps"] info["total_episodes"] = get_total_episodes_task(root, task_id, task_ranges, step) info["total_videos"] = info["total_episodes"] * NUM_CAMERAS info["total_frames"] = get_total_frames_task(root, meta_path, task_id, task_ranges, step) info["total_tasks"] = 1 write_info(info, new_root) # convert_info(DATA_PATH, 12, 24) def load_jsonlines(fpath: Path) -> list[any]: with jsonlines.open(fpath, "r") as reader: return list(reader) def legacy_load_tasks(local_dir: Path) -> tuple[dict, dict]: tasks = load_jsonlines(local_dir / LEGACY_TASKS_PATH) # return tasks dict such that tasks = {item["task_index"]: item["task"] for item in sorted(tasks, key=lambda x: x["task_index"])} task_to_task_index = {task: task_index for task_index, task in tasks.items()} return tasks, task_to_task_index def convert_tasks(root, new_root, task_id: int): tasks, _ = legacy_load_tasks(root) if task_id not in tasks: raise ValueError(f"Task ID {task_id} not found in tasks (available: {list(tasks.keys())})") tasks = {task_id: tasks[task_id]} task_indices = tasks.keys() task_strings = tasks.values() df_tasks = pd.DataFrame({"task_index": task_indices}, index=task_strings) write_tasks(df_tasks, new_root) # convert_tasks(DATA_PATH) def concat_data_files(paths_to_cat, new_root, chunk_idx, file_idx, image_keys): # TODO(rcadene): to save RAM use Dataset.from_parquet(file) and concatenate_datasets dataframes = [pd.read_parquet(file) for file in paths_to_cat] # Concatenate all DataFrames along rows concatenated_df = pd.concat(dataframes, ignore_index=True) path = new_root / DEFAULT_DATA_PATH.format(chunk_index=chunk_idx, file_index=file_idx) path.parent.mkdir(parents=True, exist_ok=True) if len(image_keys) > 0: schema = pa.Schema.from_pandas(concatenated_df) features = Features.from_arrow_schema(schema) for key in image_keys: features[key] = Image() schema = features.arrow_schema else: schema = None concatenated_df.to_parquet(path, index=False, schema=schema) def get_image_keys(root): info = load_info(root) features = info["features"] image_keys = [key for key, ft in features.items() if ft["dtype"] == "image"] return image_keys def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int, task_index: int): task_dir_name = f"task-000{task_index}" data_dir = root / "data" / task_dir_name # print("data_dir", data_dir) ep_paths = sorted(data_dir.glob("*.parquet")) # ep_paths = sorted(data_dir.glob("*/*.parquet")) # print("ep_paths", ep_paths) image_keys = get_image_keys(root) ep_idx = 0 chunk_idx = 0 file_idx = 0 size_in_mb = 0 num_frames = 0 paths_to_cat = [] episodes_metadata = [] logging.info(f"Converting data files from {len(ep_paths)} episodes") for ep_path in tqdm.tqdm(ep_paths, desc="convert data files"): ep_size_in_mb = get_parquet_file_size_in_mb(ep_path) ep_num_frames = get_parquet_num_frames(ep_path) ep_metadata = { "episode_index": ep_idx, "data/chunk_index": chunk_idx, "data/file_index": file_idx, "dataset_from_index": num_frames, "dataset_to_index": num_frames + ep_num_frames, } size_in_mb += ep_size_in_mb num_frames += ep_num_frames episodes_metadata.append(ep_metadata) ep_idx += 1 if size_in_mb < data_file_size_in_mb: paths_to_cat.append(ep_path) continue if paths_to_cat: concat_data_files(paths_to_cat, new_root, chunk_idx, file_idx, image_keys) # Reset for the next file size_in_mb = ep_size_in_mb paths_to_cat = [ep_path] chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, DEFAULT_CHUNK_SIZE) # Write remaining data if any if paths_to_cat: concat_data_files(paths_to_cat, new_root, chunk_idx, file_idx, image_keys) return episodes_metadata # episodes_metadata = convert_data(DATA_PATH, NEW_ROOT, 50, TASK) # print("episodes meta: ", episodes_metadata) def convert_videos_of_camera( root: Path, new_root: Path, video_key: str, video_file_size_in_mb: int, task_index: int ): # Access old paths to mp4 # videos_dir = root / "videos" # ep_paths = sorted(videos_dir.glob(f"*/{video_key}/*.mp4")) task_dir_name = f"task-000{task_index}" videos_dir = root / "videos" / task_dir_name / video_key ep_paths = sorted(videos_dir.glob("*.mp4")) print("ep_paths", ep_paths) ep_idx = 0 chunk_idx = 0 file_idx = 0 size_in_mb = 0 duration_in_s = 0.0 paths_to_cat = [] episodes_metadata = [] for ep_path in tqdm.tqdm(ep_paths, desc=f"convert videos of {video_key}"): ep_size_in_mb = get_file_size_in_mb(ep_path) ep_duration_in_s = get_video_duration_in_s(ep_path) # Check if adding this episode would exceed the limit if size_in_mb + ep_size_in_mb >= video_file_size_in_mb and len(paths_to_cat) > 0: # Size limit would be exceeded, save current accumulation WITHOUT this episode concatenate_video_files( paths_to_cat, new_root / DEFAULT_VIDEO_PATH.format(video_key=video_key, chunk_index=chunk_idx, file_index=file_idx), ) # Update episodes metadata for the file we just saved for i, _ in enumerate(paths_to_cat): past_ep_idx = ep_idx - len(paths_to_cat) + i episodes_metadata[past_ep_idx][f"videos/{video_key}/chunk_index"] = chunk_idx episodes_metadata[past_ep_idx][f"videos/{video_key}/file_index"] = file_idx # Move to next file and start fresh with current episode chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, DEFAULT_CHUNK_SIZE) size_in_mb = 0 duration_in_s = 0.0 paths_to_cat = [] # Add current episode metadata ep_metadata = { "episode_index": ep_idx, f"videos/{video_key}/chunk_index": chunk_idx, # Will be updated when file is saved f"videos/{video_key}/file_index": file_idx, # Will be updated when file is saved f"videos/{video_key}/from_timestamp": duration_in_s, f"videos/{video_key}/to_timestamp": duration_in_s + ep_duration_in_s, } episodes_metadata.append(ep_metadata) # Add current episode to accumulation paths_to_cat.append(ep_path) size_in_mb += ep_size_in_mb duration_in_s += ep_duration_in_s ep_idx += 1 # Write remaining videos if any if paths_to_cat: concatenate_video_files( paths_to_cat, new_root / DEFAULT_VIDEO_PATH.format(video_key=video_key, chunk_index=chunk_idx, file_index=file_idx), ) # Update episodes metadata for the final file for i, _ in enumerate(paths_to_cat): past_ep_idx = ep_idx - len(paths_to_cat) + i episodes_metadata[past_ep_idx][f"videos/{video_key}/chunk_index"] = chunk_idx episodes_metadata[past_ep_idx][f"videos/{video_key}/file_index"] = file_idx return episodes_metadata def get_video_keys(root): info = load_info(root) features = info["features"] video_keys = [key for key, ft in features.items() if ft["dtype"] == "video"] return video_keys def convert_videos(root: Path, new_root: Path, video_file_size_in_mb: int, task_id: int): logging.info(f"Converting videos from {root} to {new_root}") video_keys = get_video_keys(root) if len(video_keys) == 0: return None video_keys = sorted(video_keys) eps_metadata_per_cam = [] for camera in video_keys: eps_metadata = convert_videos_of_camera(root, new_root, camera, video_file_size_in_mb, task_id) eps_metadata_per_cam.append(eps_metadata) num_eps_per_cam = [len(eps_cam_map) for eps_cam_map in eps_metadata_per_cam] if len(set(num_eps_per_cam)) != 1: raise ValueError(f"All cams dont have same number of episodes ({num_eps_per_cam}).") episods_metadata = [] num_cameras = len(video_keys) num_episodes = num_eps_per_cam[0] for ep_idx in tqdm.tqdm(range(num_episodes), desc="convert videos"): # Sanity check ep_ids = [eps_metadata_per_cam[cam_idx][ep_idx]["episode_index"] for cam_idx in range(num_cameras)] ep_ids += [ep_idx] if len(set(ep_ids)) != 1: raise ValueError(f"All episode indices need to match ({ep_ids}).") ep_dict = {} for cam_idx in range(num_cameras): ep_dict.update(eps_metadata_per_cam[cam_idx][ep_idx]) episods_metadata.append(ep_dict) return episods_metadata import json from pathlib import Path def infer_task_episode_ranges(episodes_jsonl_path: Path) -> dict: """ Parse the Behavior-1K episodes.jsonl metadata and infer contiguous episode ranges per unique task. Returns a dict: { task_id: { "task_string": ..., "ep_start": ..., "ep_end": ... } } """ task_ranges = {} task_id = 0 current_task_str = None ep_start = None ep_end = None with open(episodes_jsonl_path) as f: for line in f: if not line.strip(): continue ep = json.loads(line) ep_idx = ep["episode_index"] task_str = ep["tasks"][0] if ep["tasks"] else "UNKNOWN" if current_task_str is None: current_task_str = task_str ep_start = ep_idx ep_end = ep_idx elif task_str == current_task_str: ep_end = ep_idx else: # close previous task group task_ranges[task_id] = { "task_string": current_task_str, "ep_start": ep_start, "ep_end": ep_end, } task_id += 1 # start new one current_task_str = task_str ep_start = ep_idx ep_end = ep_idx # store last task if current_task_str is not None: task_ranges[task_id] = { "task_string": current_task_str, "ep_start": ep_start, "ep_end": ep_end, } return task_ranges def legacy_load_episodes_task(local_dir: Path, task_id: int, task_ranges: dict, step: int = 10) -> dict: """ Load only the episodes belonging to a specific task, inferred automatically from episode ranges. Args: local_dir (Path): Root path containing legacy meta/episodes.jsonl task_id (int): Which task to load (key from the inferred task_ranges dict) task_ranges (dict): Mapping from infer_task_episode_ranges() step (int): Episode index step (Behavior-1K = 10) """ all_episodes = legacy_load_episodes(local_dir) # get the range for this task if task_id not in task_ranges: raise ValueError(f"Task id {task_id} not found in task_ranges") ep_start = task_ranges[task_id]["ep_start"] ep_end = task_ranges[task_id]["ep_end"] task_episode_indices = range(ep_start, ep_end + step, step) return {i: all_episodes[i] for i in task_episode_indices if i in all_episodes} def legacy_load_episodes(local_dir: Path) -> dict: episodes = load_jsonlines(local_dir / LEGACY_EPISODES_PATH) return {item["episode_index"]: item for item in sorted(episodes, key=lambda x: x["episode_index"])} # episodes_videos_metadata = convert_videos(DATA_PATH, NEW_ROOT, 50) # episodes_legacy_metadata = legacy_load_episodes(DATA_PATH) # episodes_task_0 = legacy_load_episodes_task(DATA_PATH, task_id=TASK, task_ranges=task_ranges) def legacy_load_episodes_stats(local_dir: Path) -> dict: episodes_stats = load_jsonlines(local_dir / LEGACY_EPISODES_STATS_PATH) return { item["episode_index"]: cast_stats_to_numpy(item["stats"]) for item in sorted(episodes_stats, key=lambda x: x["episode_index"]) } def legacy_load_episodes_stats_task(local_dir: Path, task_id: int, task_ranges: dict, step: int = 10) -> dict: all_stats = legacy_load_episodes_stats(local_dir) if task_id not in task_ranges: raise ValueError(f"Task id {task_id} not found in task_ranges") ep_start = task_ranges[task_id]["ep_start"] ep_end = task_ranges[task_id]["ep_end"] task_episode_indices = range(ep_start, ep_end + step, step) return {i: all_stats[i] for i in task_episode_indices if i in all_stats} # ep = legacy_load_episodes_stats_task(DATA_PATH, task_id=TASK, task_ranges=task_ranges) def generate_episode_metadata_dict( episodes_legacy_metadata, episodes_metadata, episodes_stats, episodes_videos=None ): num_episodes = len(episodes_metadata) episodes_legacy_metadata_vals = list(episodes_legacy_metadata.values()) episodes_stats_vals = list(episodes_stats.values()) episodes_stats_keys = list(episodes_stats.keys()) for i in range(num_episodes): ep_legacy_metadata = episodes_legacy_metadata_vals[i] ep_metadata = episodes_metadata[i] ep_stats = episodes_stats_vals[i] ep_ids_set = { ep_legacy_metadata["episode_index"], ep_metadata["episode_index"], episodes_stats_keys[i], } if episodes_videos is None: ep_video = {} else: ep_video = episodes_videos[i] ep_ids_set.add(ep_video["episode_index"]) # if len(ep_ids_set) != 1: # raise ValueError(f"Number of episodes is not the same ({ep_ids_set}).") ep_dict = {**ep_metadata, **ep_video, **ep_legacy_metadata, **flatten_dict({"stats": ep_stats})} ep_dict["meta/episodes/chunk_index"] = 0 ep_dict["meta/episodes/file_index"] = 0 yield ep_dict def convert_episodes_metadata( root, new_root, episodes_metadata, task_id: int, task_ranges, episodes_video_metadata=None ): logging.info(f"Converting episodes metadata from {root} to {new_root}") # filter by task episodes_legacy_metadata = legacy_load_episodes_task(root, task_id=task_id, task_ranges=task_ranges) episodes_stats = legacy_load_episodes_stats_task(root, task_id=task_id, task_ranges=task_ranges) num_eps_set = {len(episodes_legacy_metadata), len(episodes_metadata)} if episodes_video_metadata is not None: num_eps_set.add(len(episodes_video_metadata)) if len(num_eps_set) != 1: raise ValueError(f"Number of episodes is not the same ({num_eps_set}).") ds_episodes = Dataset.from_generator( lambda: generate_episode_metadata_dict( episodes_legacy_metadata, episodes_metadata, episodes_stats, episodes_video_metadata ) ) write_episodes(ds_episodes, new_root) stats = aggregate_stats(list(episodes_stats.values())) write_stats(stats, new_root) from pathlib import Path def convert_dataset_local( data_path: Path, new_repo: Path, task_id: int, data_file_size_in_mb: int = DEFAULT_DATA_FILE_SIZE_IN_MB, video_file_size_in_mb: int = DEFAULT_VIDEO_FILE_SIZE_IN_MB, force_conversion: bool = False, ): """ Convert a local dataset to v3.x format, task-by-task, without using the Hugging Face Hub. Args: data_path (Path): path to local dataset root (e.g. /fsx/.../2025-challenge-demos) new_repo (Path): path where converted dataset will be written (e.g. /fsx/.../behavior1k_v3) task_id (int): which task to convert (index) data_file_size_in_mb (int): max size per data chunk video_file_size_in_mb (int): max size per video chunk force_conversion (bool): overwrite existing conversion if True """ root = Path(data_path) new_root = Path(new_repo) # Clean up if needed if new_root.exists() and force_conversion: shutil.rmtree(new_root) new_root.mkdir(parents=True, exist_ok=True) print(f"🔹 Starting conversion for task {task_id}") print(f"Input root: {root}") print(f"Output root: {new_root}") STEP = 10 # Infer task episode ranges EPISODES_META_PATH = DATA_PATH / "meta" / "episodes.jsonl" task_ranges = infer_task_episode_ranges(EPISODES_META_PATH) # def convert_info(root, new_root, data_file_size_in_mb, video_file_size_in_mb, meta_path, task_id: int, task_ranges, step): convert_info( root, new_root, data_file_size_in_mb, video_file_size_in_mb, EPISODES_META_PATH, task_id, task_ranges, STEP, ) convert_tasks(root, new_root, task_id) episodes_metadata = convert_data(root, new_root, data_file_size_in_mb, task_index=task_id) episodes_videos_metadata = convert_videos(root, new_root, video_file_size_in_mb, task_id=task_id) convert_episodes_metadata( root, new_root, episodes_metadata, task_id=task_id, task_ranges=task_ranges, episodes_video_metadata=episodes_videos_metadata, ) print(f"✅ Conversion complete for task {task_id}") print(f"Converted dataset written to: {new_root}") if __name__ == "__main__": import argparse from pathlib import Path init_logging() parser = argparse.ArgumentParser( description="Convert Behavior-1K tasks to LeRobot v3 format (local only)" ) parser.add_argument( "--data-path", type=str, required=True, help="Path to the local Behavior-1K dataset (e.g. /fsx/francesco_capuano/.cache/behavior-1k/2025-challenge-demos)", ) parser.add_argument( "--new-repo", type=str, required=True, help="Path to the output directory for the converted dataset", ) parser.add_argument( "--task-id", type=int, required=True, help="Task index to convert (e.g. 0, 1, 2, ...)", ) parser.add_argument( "--data-file-size-in-mb", type=int, default=DEFAULT_DATA_FILE_SIZE_IN_MB, help=f"Maximum size per data chunk (default: {DEFAULT_DATA_FILE_SIZE_IN_MB})", ) parser.add_argument( "--video-file-size-in-mb", type=int, default=DEFAULT_VIDEO_FILE_SIZE_IN_MB, help=f"Maximum size per video chunk (default: {DEFAULT_VIDEO_FILE_SIZE_IN_MB})", ) parser.add_argument( "--force-conversion", action="store_true", help="Force overwrite of existing conversion output if present.", ) args = parser.parse_args() convert_dataset_local( data_path=Path(args.data_path), new_repo=Path(args.new_repo), task_id=args.task_id, data_file_size_in_mb=args.data_file_size_in_mb, video_file_size_in_mb=args.video_file_size_in_mb, force_conversion=args.force_conversion, ) def convert_dataset( repo_id: str, branch: str | None = None, data_file_size_in_mb: int | None = None, video_file_size_in_mb: int | None = None, root: str | Path | None = None, push_to_hub: bool = True, force_conversion: bool = False, ): if data_file_size_in_mb is None: data_file_size_in_mb = DEFAULT_DATA_FILE_SIZE_IN_MB if video_file_size_in_mb is None: video_file_size_in_mb = DEFAULT_VIDEO_FILE_SIZE_IN_MB # First check if the dataset already has a v3.0 version if root is None and not force_conversion: try: print("Trying to download v3.0 version of the dataset from the hub...") snapshot_download(repo_id, repo_type="dataset", revision=V30, local_dir=HF_LEROBOT_HOME / repo_id) return except Exception: print("Dataset does not have an uploaded v3.0 version. Continuing with conversion.") # Set root based on whether local dataset path is provided use_local_dataset = False root = HF_LEROBOT_HOME / repo_id if root is None else Path(root) / repo_id if root.exists(): validate_local_dataset_version(root) use_local_dataset = True print(f"Using local dataset at {root}") old_root = root.parent / f"{root.name}_old" new_root = root.parent / f"{root.name}_v30" # Handle old_root cleanup if both old_root and root exist if old_root.is_dir() and root.is_dir(): shutil.rmtree(str(root)) shutil.move(str(old_root), str(root)) if new_root.is_dir(): shutil.rmtree(new_root) if not use_local_dataset: snapshot_download( repo_id, repo_type="dataset", revision=V21, local_dir=root, ) EPISODES_META_PATH = DATA_PATH / "meta" / "episodes.jsonl" task_ranges = infer_task_episode_ranges(EPISODES_META_PATH) convert_info(root, new_root, data_file_size_in_mb, video_file_size_in_mb) # convert_tasks(root, new_root, TASK) # episodes_metadata = convert_data(root, new_root, data_file_size_in_mb, task_index=TASK) # episodes_videos_metadata = convert_videos(root, new_root, video_file_size_in_mb, task_id=TASK) # convert_episodes_metadata(root, new_root, episodes_metadata, task_id=TASK, task_ranges=task_ranges, episodes_videos_metadata=episodes_videos_metadata) shutil.move(str(root), str(old_root)) shutil.move(str(new_root), str(root)) if push_to_hub: hub_api = HfApi() try: hub_api.delete_tag(repo_id, tag=CODEBASE_VERSION, repo_type="dataset") except HTTPError as e: print(f"tag={CODEBASE_VERSION} probably doesn't exist. Skipping exception ({e})") pass hub_api.delete_files( delete_patterns=["data/chunk*/episode_*", "meta/*.jsonl", "videos/chunk*"], repo_id=repo_id, revision=branch, repo_type="dataset", ) hub_api.create_tag(repo_id, tag=CODEBASE_VERSION, revision=branch, repo_type="dataset") LeRobotDataset(repo_id).push_to_hub()