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"""
Main dataset converter that can convert any dataset to HuggingFace format for Robometer model training.
This is a generic converter that works with any dataset-specific loader.
"""
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" # hide INFO/WARN/ERROR; only FATAL remains
import multiprocessing as mp
import numpy as np
from collections.abc import Callable
from dataclasses import dataclass, field
from functools import partial
from multiprocessing import Pool, cpu_count
from typing import Any, Optional
from pyrallis import wrap
from tqdm import tqdm
import datasets
from datasets import Dataset
# from robometer.data.dataset_types import Trajectory # not needed, just type hint
from dataset_upload.helpers import (
create_hf_trajectory,
create_output_directory,
flatten_task_data,
load_sentence_transformer_model,
)
from huggingface_hub import HfApi
# make sure these come after importing torch. otherwise something breaks...
try:
import absl.logging as absl_logging
absl_logging.set_verbosity(absl_logging.ERROR)
except Exception:
pass
try:
import tensorflow as tf
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
except Exception:
pass
os.environ["TOKENIZERS_PARALLELISM"] = "true"
def push_hf_dataset_and_video_files_to_hub(dataset, hub_repo_id, hub_token, dataset_name, output_dir):
print(f"Pushing dataset to HuggingFace Hub: {hub_repo_id}")
dataset.push_to_hub(
hub_repo_id,
config_name=dataset_name.lower(),
token=hub_token,
private=False,
commit_message=f"Add {dataset_name} dataset for Robometer training",
)
print(f"✅ Successfully pushed dataset to: https://huggingface.co/datasets/{hub_repo_id}")
api = HfApi(token=hub_token)
api.upload_large_folder(
folder_path=output_dir,
repo_id=hub_repo_id,
repo_type="dataset",
num_workers=min(4, cpu_count()),
)
print(f"✅ Successfully pushed video files for {dataset_name} to: https://huggingface.co/datasets/{hub_repo_id}")
def get_trajectory_subdir_path(trajectory_idx: int, files_per_subdir: int = 1000) -> str:
"""
Generate subdirectory path for a trajectory to avoid too many files per directory.
Args:
trajectory_idx: Index of the trajectory
files_per_subdir: Maximum files per subdirectory (default: 1000)
Returns:
str: Subdirectory name like 'batch_0000'
"""
subdir_index = trajectory_idx // files_per_subdir
return f"batch_{subdir_index:04d}"
# Global dataset features definition
BASE_FEATURES = {
"id": datasets.Value("string"),
"task": datasets.Value("string"),
"lang_vector": datasets.Sequence(datasets.Value("float32")),
"data_source": datasets.Value("string"),
"frames": None, # Will be set based on use_video parameter
"is_robot": datasets.Value("bool"),
"quality_label": datasets.Value("string"),
# "preference_group_id": datasets.Value("string"),
# "preference_rank": datasets.Value("int32"),
"partial_success": datasets.Value("float32"), # in [0, 1]
}
@dataclass
class DatasetConfig:
"""Config for dataset settings"""
dataset_path: str = field(default="", metadata={"help": "Path to the dataset"})
dataset_name: str = field(default=None, metadata={"help": "Name of the dataset (defaults to dataset_type)"})
exclude_wrist_cam: bool = field(default=False, metadata={"help": "Exclude wrist camera views (MIT Franka only)"})
@dataclass
class OutputConfig:
"""Config for output settings"""
output_dir: str = field(default="robometer_dataset", metadata={"help": "Output directory for the dataset"})
max_trajectories: Optional[int] = field(
default=None, metadata={"help": "Maximum number of trajectories to process (None for all)"}
)
max_frames: int = field(
default=64, metadata={"help": "Maximum number of frames per trajectory (-1 for no downsampling)"}
)
use_video: bool = field(default=True, metadata={"help": "Use MP4 videos instead of individual frame images"})
shortest_edge_size: Optional[int] = field(default=240, metadata={"help": "Shortest edge size for video resizing"})
center_crop: bool = field(
default=False,
metadata={"help": "Center crop the video to the target size. Defaults to False, which means no cropping."},
)
fps: int = field(default=10, metadata={"help": "Frames per second for video creation"})
num_workers: int = field(
default=-1, metadata={"help": "Number of parallel workers for processing (-1 for auto, 0 for sequential)"}
)
@dataclass
class HubConfig:
"""Config for HuggingFace Hub settings"""
push_to_hub: bool = field(default=False, metadata={"help": "Push dataset to HuggingFace Hub"})
hub_repo_id: str = field(default=None, metadata={"help": "HuggingFace Hub repository ID"})
hub_token: str = field(
default=None, metadata={"help": "HuggingFace Hub token (or set HF_TOKEN environment variable)"}
)
@dataclass
class GenerateConfig:
"""Main configuration for dataset generation"""
dataset: DatasetConfig = field(default_factory=DatasetConfig)
output: OutputConfig = field(default_factory=OutputConfig)
hub: HubConfig = field(default_factory=HubConfig)
def process_single_trajectory(args):
"""
Worker function to process a single trajectory.
Args:
args: Tuple containing (trajectory_idx, trajectory, lang_vector, hf_creator_fn, output_dir, dataset_name, max_frames, use_video, fps)
Returns:
Dict: Processed trajectory data or None if failed
"""
trajectory_idx, trajectory, lang_vector, hf_creator_fn, output_dir, dataset_name, max_frames, use_video, fps = args
try:
# Create output directory for this trajectory with subdirectory structure
subdir_name = get_trajectory_subdir_path(trajectory_idx)
full_video_path = os.path.join(
output_dir, dataset_name.lower(), subdir_name, f"trajectory_{trajectory_idx:04d}.mp4"
)
relative_video_path = os.path.join(dataset_name.lower(), subdir_name, f"trajectory_{trajectory_idx:04d}.mp4")
os.makedirs(os.path.dirname(full_video_path), exist_ok=True)
# Process trajectory (lang_vector is already computed)
processed_trajectory = hf_creator_fn(
traj_dict=trajectory,
video_path=full_video_path,
lang_vector=lang_vector, # Pre-computed language vector
max_frames=max_frames,
dataset_name=dataset_name,
use_video=use_video,
fps=fps,
)
if processed_trajectory is None:
return None
# Replace the full path with relative path in the processed trajectory
if processed_trajectory and "frames" in processed_trajectory:
processed_trajectory["frames"] = relative_video_path
return processed_trajectory
except Exception as e:
print(f"❌ Error processing trajectory {trajectory_idx}: {e}")
return None
def convert_dataset_to_hf_format(
trajectories: list[dict],
hf_creator_fn: Callable[[dict, str, str, int, Any, int, str], Any],
output_dir: str = "robometer_dataset",
dataset_name: str = "",
max_trajectories: int | None = None,
max_frames: int = -1,
use_video: bool = True,
fps: int = 10,
num_workers: int = -1,
push_to_hub: bool = False,
hub_repo_id: str | None = None,
hub_token: str | None = None,
) -> Dataset:
"""Convert a list of trajectories to HuggingFace format."""
print(f"Converting {dataset_name} dataset to HuggingFace format...")
# Create output directory
create_output_directory(output_dir)
# Validate input
if not trajectories:
raise ValueError(f"No trajectories provided for {dataset_name} dataset.")
print(f"Processing {len(trajectories)} trajectories")
# Limit trajectories if specified
if max_trajectories != -1:
trajectories = trajectories[:max_trajectories]
# Determine number of workers
if num_workers == -1:
num_workers = min(cpu_count(), len(trajectories))
elif num_workers == 0:
num_workers = 1 # Sequential processing
print(f"Using {num_workers} worker(s) for parallel processing")
# Pre-compute language embeddings to avoid loading sentence transformer in each worker
print("Pre-computing language embeddings...")
lang_model = load_sentence_transformer_model()
lang_vectors = []
unique_tasks = {} # Cache for identical task descriptions
for trajectory in tqdm(trajectories, desc="Computing language embeddings"):
task_description = trajectory["task"]
# Use cache to avoid recomputing identical task descriptions
if task_description not in unique_tasks:
unique_tasks[task_description] = lang_model.encode(task_description)
lang_vectors.append(unique_tasks[task_description])
print(f"Computed embeddings for {len(unique_tasks)} unique task descriptions")
# Process trajectories
all_entries = []
if num_workers == 1:
# Sequential processing (using pre-computed embeddings)
for trajectory_idx, (trajectory, lang_vector) in enumerate(
tqdm(zip(trajectories, lang_vectors, strict=False), desc="Processing trajectories")
):
# Create output directory for this trajectory with subdirectory structure
subdir_name = get_trajectory_subdir_path(trajectory_idx)
trajectory_dir = os.path.join(
output_dir, dataset_name.lower(), subdir_name, f"trajectory_{trajectory_idx:04d}.mp4"
)
os.makedirs(os.path.dirname(trajectory_dir), exist_ok=True)
processed_trajectory = hf_creator_fn(
traj_dict=trajectory,
video_path=trajectory_dir,
lang_vector=lang_vector, # Pre-computed language vector
max_frames=max_frames,
dataset_name=dataset_name,
use_video=use_video,
fps=fps,
)
if processed_trajectory is None:
continue
all_entries.append(processed_trajectory)
else:
# Parallel processing
all_entries = [] # ensure defined if Pool raises before we filter results
print(f"Preparing {len(trajectories)} trajectories for parallel processing...")
# Prepare arguments for worker processes
worker_args = []
for trajectory_idx, (trajectory, lang_vector) in enumerate(zip(trajectories, lang_vectors, strict=False)):
args = (
trajectory_idx,
trajectory,
lang_vector, # Pre-computed language vector
hf_creator_fn,
output_dir,
dataset_name,
max_frames,
use_video,
fps,
)
worker_args.append(args)
# Use spawn to avoid CUDA context issues from forking after TF import
try:
mp.set_start_method("spawn", force=True)
except RuntimeError:
pass
# Process trajectories in parallel
with Pool(processes=num_workers) as pool:
results = list(
tqdm(
pool.imap_unordered(process_single_trajectory, worker_args),
total=len(worker_args),
desc="Processing trajectories",
)
)
# Filter out failed trajectories (None results)
all_entries = [result for result in results if result is not None]
if len(all_entries) < len(trajectories):
failed_count = len(trajectories) - len(all_entries)
print(f"⚠️ {failed_count} trajectories failed to process and were skipped")
# Create HuggingFace dataset with proper features
print(f"Creating HuggingFace dataset with {len(all_entries)} entries...")
# Convert list of entries to dictionary format for from_dict()
data_dict = {
"id": [entry["id"] for entry in all_entries],
"task": [entry["task"] for entry in all_entries],
"lang_vector": [entry["lang_vector"] for entry in all_entries],
"data_source": [entry["data_source"] for entry in all_entries],
"frames": [entry["frames"] for entry in all_entries],
"is_robot": [entry["is_robot"] for entry in all_entries],
"quality_label": [entry.get("quality_label") for entry in all_entries],
"partial_success": [entry.get("partial_success") for entry in all_entries],
# "preference_group_id": [entry.get("preference_group_id") for entry in all_entries],
# "preference_rank": [entry.get("preference_rank") for entry in all_entries],
}
# Set frames feature based on video mode
features_dict = BASE_FEATURES.copy()
if use_video:
features_dict["frames"] = datasets.Value("string") # Video file paths as strings
else:
features_dict["frames"] = datasets.Sequence(datasets.Image())
features = datasets.Features(features_dict)
dataset = Dataset.from_dict(data_dict, features=features)
print(f"{dataset_name} HuggingFace dataset created successfully!")
print(f"Total entries: {len(all_entries)}")
# Push to HuggingFace Hub if requested
if push_to_hub and hub_repo_id:
print(f"\nPushing dataset to HuggingFace Hub: {hub_repo_id}")
try:
# Push the dataset to the hub with dataset name as config name
dataset.push_to_hub(
hub_repo_id,
config_name=dataset_name.lower(), # Use dataset name as config name
token=hub_token,
private=False,
commit_message=f"Add {dataset_name} dataset for Robometer training",
)
print(f"✅ Successfully pushed dataset to: https://huggingface.co/datasets/{hub_repo_id}")
print(f"📁 Dataset available as config: {dataset_name.lower()}")
# Also push the video files folder to the hub
print("\nPushing video files to HuggingFace Hub...")
from huggingface_hub import HfApi
api = HfApi(token=hub_token)
# Upload the entire output directory (which contains all the video files)
api.upload_large_folder(
folder_path=output_dir,
repo_id=hub_repo_id,
repo_type="dataset",
# commit_message=f"Add video files for {dataset_name} dataset"
)
print(f"✅ Successfully pushed video files to: https://huggingface.co/datasets/{hub_repo_id}")
except Exception as e:
print(f"❌ Error pushing to hub: {e}")
print("Dataset was created locally but failed to push to hub")
elif push_to_hub and not hub_repo_id:
print("❌ push_to_hub=True but no hub_repo_id provided")
else:
# Only save locally if not pushing to hub (to avoid redundant Arrow files)
dataset_path = os.path.join(output_dir, dataset_name.lower())
dataset.save_to_disk(dataset_path)
print(f"Dataset saved locally to: {dataset_path}")
return dataset
@wrap()
def main(cfg: GenerateConfig):
"""Main function to convert any dataset to HuggingFace format."""
# Get hub token from environment if not provided
if cfg.hub.hub_token is None:
cfg.hub.hub_token = os.getenv("HF_TOKEN")
# Only require HF_USERNAME if pushing to hub
if cfg.hub.push_to_hub:
username = os.getenv("HF_USERNAME")
if not username:
raise ValueError(
"HF_USERNAME is not set. Please export it to push to the Hub, or set hub.push_to_hub=false."
)
if cfg.hub.hub_repo_id:
cfg.hub.hub_repo_id = username + "/" + cfg.hub.hub_repo_id
# Import the appropriate dataset loader and trajectory creator
if "libero" in cfg.dataset.dataset_name:
from dataset_upload.dataset_loaders.libero_loader import load_libero_dataset
# Load the trajectories using the loader
task_data = load_libero_dataset(cfg.dataset.dataset_path)
trajectories = flatten_task_data(task_data)
elif "agibotworld" in (cfg.dataset.dataset_name or "").lower():
# Stream + convert directly inside the AgiBotWorld loader
from dataset_upload.dataset_loaders.agibotworld_loader import (
convert_agibotworld_streaming_to_hf,
)
dataset = convert_agibotworld_streaming_to_hf(
dataset_name=cfg.dataset.dataset_path,
output_dir=cfg.output.output_dir,
dataset_label=cfg.dataset.dataset_name or "agibotworld",
max_trajectories=cfg.output.max_trajectories,
max_frames=cfg.output.max_frames,
fps=cfg.output.fps,
num_workers=cfg.output.num_workers,
)
# Handle pushing/saving consistently
if cfg.hub.push_to_hub and cfg.hub.hub_repo_id:
print(f"\nPushing dataset to HuggingFace Hub: {cfg.hub.hub_repo_id}")
try:
# Push the arrow table
dataset.push_to_hub(
cfg.hub.hub_repo_id,
config_name=(cfg.dataset.dataset_name or "agibotworld").lower(),
token=cfg.hub.hub_token,
private=False,
commit_message=f"Add {cfg.dataset.dataset_name} dataset for Robometer training",
)
print(f"✅ Successfully pushed dataset to: https://huggingface.co/datasets/{cfg.hub.hub_repo_id}")
# Push the large video folder(s)
print("\nPushing video files to HuggingFace Hub...")
from huggingface_hub import HfApi
api = HfApi(token=cfg.hub.hub_token)
api.upload_large_folder(
folder_path=cfg.output.output_dir,
repo_id=cfg.hub.hub_repo_id,
repo_type="dataset",
)
print(f"✅ Successfully pushed video files to: https://huggingface.co/datasets/{cfg.hub.hub_repo_id}")
except Exception as e:
print(f"❌ Error pushing to hub: {e}")
print("Dataset was created locally but failed to push videos and/or metadata to hub")
else:
dataset_path = os.path.join(cfg.output.output_dir, (cfg.dataset.dataset_name or "agibotworld").lower())
dataset.save_to_disk(dataset_path)
print(f"Dataset saved locally to: {dataset_path}")
print("Dataset conversion complete!")
return
elif "egodex" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.egodex_loader import load_egodex_dataset
# Load the trajectories using the loader with max_trajectories limit
print(f"Loading EgoDex dataset from: {cfg.dataset.dataset_path}")
task_data = load_egodex_dataset(
cfg.dataset.dataset_path,
cfg.output.max_trajectories,
)
trajectories = flatten_task_data(task_data)
elif cfg.dataset.dataset_name.lower().startswith("oxe_"):
# Treat OXE like AgiBotWorld: create videos and HF entries directly in the loader
os.environ.setdefault("CUDA_VISIBLE_DEVICES", "")
from dataset_upload.dataset_loaders.oxe_loader import convert_oxe_dataset_to_hf
print(f"Converting OXE dataset directly to HF from: {cfg.dataset.dataset_path}")
dataset = convert_oxe_dataset_to_hf(
dataset_path=cfg.dataset.dataset_path,
dataset_name=cfg.dataset.dataset_name,
output_dir=cfg.output.output_dir,
max_trajectories=cfg.output.max_trajectories,
max_frames=cfg.output.max_frames,
fps=cfg.output.fps,
num_workers=cfg.output.num_workers,
)
# Handle pushing/saving consistently
if cfg.hub.push_to_hub and cfg.hub.hub_repo_id:
print(f"\nPushing dataset to HuggingFace Hub: {cfg.hub.hub_repo_id}")
try:
push_hf_dataset_and_video_files_to_hub(
dataset, cfg.hub.hub_repo_id, cfg.hub.hub_token, cfg.dataset.dataset_name, cfg.output.output_dir
)
except Exception as e:
print(f"❌ Error pushing to hub: {e}")
print("Dataset was created locally but failed to push videos and/or metadata to hub")
else:
dataset_path = os.path.join(cfg.output.output_dir, (cfg.dataset.dataset_name).lower())
dataset.save_to_disk(dataset_path)
print(f"Dataset saved locally to: {dataset_path}")
print("Dataset conversion complete!")
return
elif "robofail" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.robofail_loader import load_robofail_dataset
# Load the trajectories using the loader with max_trajectories limit
print(f"Loading RoboFail dataset from: {cfg.dataset.dataset_path}")
task_data = load_robofail_dataset(
cfg.dataset.dataset_path,
cfg.output.max_trajectories,
)
trajectories = flatten_task_data(task_data)
elif "metaworld" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.mw_collected_loader import load_metaworld_dataset
# Load the trajectories using the loader with max_trajectories limit
print(f"Loading metaworld dataset from: {cfg.dataset.dataset_path}")
task_data = load_metaworld_dataset(
cfg.dataset.dataset_path,
dataset_name=cfg.dataset.dataset_name,
)
trajectories = flatten_task_data(task_data)
elif "h2r" in cfg.dataset.dataset_name.lower():
# Stream + convert directly inside the H2R loader (OXE-style)
from dataset_upload.dataset_loaders.h2r_loader import convert_h2r_dataset_to_hf
print(f"Converting H2R dataset directly to HF from: {cfg.dataset.dataset_path}")
dataset = convert_h2r_dataset_to_hf(
dataset_path=cfg.dataset.dataset_path,
dataset_name=cfg.dataset.dataset_name,
output_dir=cfg.output.output_dir,
max_trajectories=cfg.output.max_trajectories,
max_frames=cfg.output.max_frames,
fps=cfg.output.fps,
num_workers=cfg.output.num_workers,
)
# Handle pushing/saving consistently
if cfg.hub.push_to_hub and cfg.hub.hub_repo_id:
print(f"\nPushing dataset to HuggingFace Hub: {cfg.hub.hub_repo_id}")
try:
push_hf_dataset_and_video_files_to_hub(
dataset, cfg.hub.hub_repo_id, cfg.hub.hub_token, cfg.dataset.dataset_name, cfg.output.output_dir
)
except Exception as e:
print(f"❌ Error pushing to hub: {e}")
print("Dataset was created locally but failed to push videos and/or metadata to hub")
else:
dataset_path = os.path.join(cfg.output.output_dir, (cfg.dataset.dataset_name).lower())
dataset.save_to_disk(dataset_path)
print(f"Dataset saved locally to: {dataset_path}")
print("Dataset conversion complete!")
return
elif "fino_net" in cfg.dataset.dataset_name.lower() or "fino-net" in cfg.dataset.dataset_name.lower():
# Stream + convert directly inside the FinoNet loader (H2R/OXE-style)
from dataset_upload.dataset_loaders.fino_net_loader import convert_fino_net_dataset_to_hf
print(f"Converting FinoNet dataset directly to HF from: {cfg.dataset.dataset_path}")
dataset = convert_fino_net_dataset_to_hf(
dataset_path=cfg.dataset.dataset_path,
dataset_name=cfg.dataset.dataset_name,
output_dir=cfg.output.output_dir,
max_trajectories=cfg.output.max_trajectories,
max_frames=cfg.output.max_frames,
fps=cfg.output.fps,
num_workers=cfg.output.num_workers,
)
# Handle pushing/saving consistently
if cfg.hub.push_to_hub and cfg.hub.hub_repo_id:
print(f"\nPushing dataset to HuggingFace Hub: {cfg.hub.hub_repo_id}")
try:
push_hf_dataset_and_video_files_to_hub(
dataset, cfg.hub.hub_repo_id, cfg.hub.hub_token, cfg.dataset.dataset_name, cfg.output.output_dir
)
except Exception as e:
print(f"❌ Error pushing to hub: {e}")
print("Dataset was created locally but failed to push videos and/or metadata to hub")
else:
dataset_path = os.path.join(cfg.output.output_dir, (cfg.dataset.dataset_name).lower())
dataset.save_to_disk(dataset_path)
print(f"Dataset saved locally to: {dataset_path}")
print("Dataset conversion complete!")
return
elif "epic" in cfg.dataset.dataset_name.lower():
# Stream + convert directly (H2R/OXE-style)
from dataset_upload.dataset_loaders.epic_loader import convert_epic_dataset_to_hf
print(f"Converting EPIC-KITCHENS dataset directly to HF from: {cfg.dataset.dataset_path}")
dataset = convert_epic_dataset_to_hf(
dataset_path=cfg.dataset.dataset_path,
dataset_name=cfg.dataset.dataset_name,
output_dir=cfg.output.output_dir,
max_trajectories=cfg.output.max_trajectories,
max_frames=cfg.output.max_frames,
fps=cfg.output.fps,
num_workers=cfg.output.num_workers,
shortest_edge_size=cfg.output.shortest_edge_size,
center_crop=cfg.output.center_crop,
)
# Handle pushing/saving consistently
if cfg.hub.push_to_hub and cfg.hub.hub_repo_id:
print(f"\nPushing dataset to HuggingFace Hub: {cfg.hub.hub_repo_id}")
try:
push_hf_dataset_and_video_files_to_hub(
dataset, cfg.hub.hub_repo_id, cfg.hub.hub_token, cfg.dataset.dataset_name, cfg.output.output_dir
)
except Exception as e:
print(f"❌ Error pushing to hub: {e}")
print("Dataset was created locally but failed to push videos and/or metadata to hub")
else:
dataset_path_local = os.path.join(cfg.output.output_dir, (cfg.dataset.dataset_name).lower())
dataset.save_to_disk(dataset_path_local)
print(f"Dataset saved locally to: {dataset_path_local}")
print("Dataset conversion complete!")
return
elif "roboarena" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.roboarena_loader import load_roboarena_dataset
# Load the trajectories using the loader with max_trajectories limit
print(f"Loading RoboArena dataset from: {cfg.dataset.dataset_path}")
task_data = load_roboarena_dataset(cfg.dataset.dataset_path)
trajectories = flatten_task_data(task_data)
elif "ph2d" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.ph2d_loader import load_ph2d_dataset
print(f"Loading Ph2d dataset from: {cfg.dataset.dataset_path}")
task_data = load_ph2d_dataset(cfg.dataset.dataset_path)
trajectories = flatten_task_data(task_data)
elif "galaxea" in cfg.dataset.dataset_name.lower():
# Stream + convert directly (OXE-style, multi-dataset)
from dataset_upload.dataset_loaders.galaxea_loader import convert_galaxea_dataset_to_hf
rlds_datasets = getattr(cfg.dataset, "rlds_datasets", []) or []
print(f"Converting Galaxea RLDS to HF from: {cfg.dataset.dataset_path} | datasets={rlds_datasets}")
dataset = convert_galaxea_dataset_to_hf(
dataset_path=cfg.dataset.dataset_path,
dataset_name=cfg.dataset.dataset_name,
output_dir=cfg.output.output_dir,
max_trajectories=cfg.output.max_trajectories,
max_frames=cfg.output.max_frames,
fps=cfg.output.fps,
num_workers=cfg.output.num_workers,
)
# Handle pushing/saving consistently
if cfg.hub.push_to_hub and cfg.hub.hub_repo_id:
print(f"\nPushing dataset to HuggingFace Hub: {cfg.hub.hub_repo_id}")
try:
# remove the galaxea_rfm prefix from the dataset name because otherwise it won't match the video folder name
# don't need to do this for OXE or others because I processed it in their loaders but forgot for this.
push_hf_dataset_and_video_files_to_hub(
dataset, cfg.hub.hub_repo_id, cfg.hub.hub_token, cfg.dataset.dataset_name, cfg.output.output_dir
)
except Exception as e:
print(f"❌ Error pushing to hub: {e}")
print("Dataset was created locally but failed to push metadata to hub")
else:
dataset_path_local = os.path.join(cfg.output.output_dir, (cfg.dataset.dataset_name).lower())
dataset.save_to_disk(dataset_path_local)
print(f"Dataset saved locally to: {dataset_path_local}")
print("Dataset conversion complete!")
return
elif "molmoact" in cfg.dataset.dataset_name.lower():
# Stream + convert directly (LeRobot parquet)
from dataset_upload.dataset_loaders.molmoact_loader import convert_molmoact_dataset_to_hf
print(f"Converting MolmoAct dataset directly to HF from: {cfg.dataset.dataset_path}")
dataset = convert_molmoact_dataset_to_hf(
dataset_path=cfg.dataset.dataset_path,
dataset_name=cfg.dataset.dataset_name,
output_dir=cfg.output.output_dir,
max_trajectories=cfg.output.max_trajectories,
max_frames=cfg.output.max_frames,
fps=cfg.output.fps,
)
if cfg.hub.push_to_hub and cfg.hub.hub_repo_id:
try:
push_hf_dataset_and_video_files_to_hub(
dataset, cfg.hub.hub_repo_id, cfg.hub.hub_token, cfg.dataset.dataset_name, cfg.output.output_dir
)
except Exception as e:
print(f"❌ Error pushing to hub: {e}")
print("Dataset was created locally but failed to push metadata to hub")
else:
dataset_path_local = os.path.join(cfg.output.output_dir, (cfg.dataset.dataset_name).lower())
dataset.save_to_disk(dataset_path_local)
print(f"Dataset saved locally to: {dataset_path_local}")
print("Dataset conversion complete!")
return
elif "auto_eval" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.autoeval_loader import load_autoeval_dataset
print(f"Loading AutoEval dataset from: {cfg.dataset.dataset_path}")
task_data = load_autoeval_dataset(cfg.dataset.dataset_path)
trajectories = flatten_task_data(task_data)
elif "usc_xarm_policy_ranking" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.usc_xarm_policy_ranking_loader import (
load_usc_xarm_policy_ranking_dataset,
)
print(f"Loading USC xArm Policy Ranking dataset from: {cfg.dataset.dataset_path}")
task_data = load_usc_xarm_policy_ranking_dataset(
cfg.dataset.dataset_path,
max_trajectories=cfg.output.max_trajectories,
)
trajectories = flatten_task_data(task_data)
elif "usc_franka_policy_ranking" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.usc_franka_policy_ranking_loader import (
load_usc_franka_policy_ranking_dataset,
)
print(f"Loading USC Franka Policy Ranking dataset from: {cfg.dataset.dataset_path}")
task_data = load_usc_franka_policy_ranking_dataset(
cfg.dataset.dataset_path,
max_trajectories=cfg.output.max_trajectories,
)
trajectories = flatten_task_data(task_data)
elif "utd_so101_policy_ranking" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.utd_so101_loader import (
load_utd_so101_dataset,
)
print(f"Loading UTD SO101 robot dataset from: {cfg.dataset.dataset_path}")
task_data = load_utd_so101_dataset(
cfg.dataset.dataset_path,
max_trajectories=cfg.output.max_trajectories,
is_robot=True,
data_source="utd_so101",
)
trajectories = flatten_task_data(task_data)
elif "utd_so101_human" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.utd_so101_loader import (
load_utd_so101_dataset,
)
print(f"Loading UTD SO101 human dataset from: {cfg.dataset.dataset_path}")
task_data = load_utd_so101_dataset(
cfg.dataset.dataset_path,
max_trajectories=cfg.output.max_trajectories,
is_robot=False,
data_source="utd_so101_human",
)
trajectories = flatten_task_data(task_data)
elif "soar" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.soar_loader import convert_soar_dataset_to_hf
print(f"Converting SOAR RLDS (local) to HF from: {cfg.dataset.dataset_path} ")
dataset = convert_soar_dataset_to_hf(
dataset_path=cfg.dataset.dataset_path,
dataset_name=cfg.dataset.dataset_name,
output_dir=cfg.output.output_dir,
max_trajectories=cfg.output.max_trajectories,
max_frames=cfg.output.max_frames,
fps=cfg.output.fps,
num_workers=cfg.output.num_workers,
)
# Handle pushing/saving consistently
if cfg.hub.push_to_hub and cfg.hub.hub_repo_id:
print(f"\nPushing dataset to HuggingFace Hub: {cfg.hub.hub_repo_id}")
try:
push_hf_dataset_and_video_files_to_hub(
dataset, cfg.hub.hub_repo_id, cfg.hub.hub_token, cfg.dataset.dataset_name, cfg.output.output_dir
)
except Exception as e:
print(f"❌ Error pushing to hub: {e}")
print("Dataset was created locally but failed to push metadata to hub")
else:
dataset_path_local = os.path.join(cfg.output.output_dir, (cfg.dataset.dataset_name).lower())
dataset.save_to_disk(dataset_path_local)
print(f"Dataset saved locally to: {dataset_path_local}")
print("Dataset conversion complete!")
return
elif "mit_franka_p-rank" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.mit_franka_prank_loader import convert_mit_franka_prank_dataset_to_hf
print(f"Converting MIT-Franka-Prank dataset to HF from: {cfg.dataset.dataset_path}")
dataset = convert_mit_franka_prank_dataset_to_hf(
dataset_path=cfg.dataset.dataset_path,
dataset_name=cfg.dataset.dataset_name,
output_dir=cfg.output.output_dir,
max_trajectories=cfg.output.max_trajectories,
max_frames=cfg.output.max_frames,
fps=cfg.output.fps,
num_workers=cfg.output.num_workers,
)
# Handle pushing/saving consistently
if cfg.hub.push_to_hub and cfg.hub.hub_repo_id:
print(f"\nPushing dataset to HuggingFace Hub: {cfg.hub.hub_repo_id}")
try:
push_hf_dataset_and_video_files_to_hub(
dataset, cfg.hub.hub_repo_id, cfg.hub.hub_token, cfg.dataset.dataset_name, cfg.output.output_dir
)
except Exception as e:
print(f"❌ Error pushing to hub: {e}")
print("Dataset was created locally but failed to push metadata to hub")
else:
dataset_path_local = os.path.join(cfg.output.output_dir, (cfg.dataset.dataset_name).lower())
dataset.save_to_disk(dataset_path_local)
print(f"Dataset saved locally to: {dataset_path_local}")
print("Dataset conversion complete!")
return
elif "rfm_new_mit_franka" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.new_mit_franka_loader import convert_new_mit_franka_dataset_to_hf
print(f"Converting New MIT Franka dataset to HF from: {cfg.dataset.dataset_path}")
dataset = convert_new_mit_franka_dataset_to_hf(
dataset_path=cfg.dataset.dataset_path,
dataset_name=cfg.dataset.dataset_name,
output_dir=cfg.output.output_dir,
max_trajectories=cfg.output.max_trajectories,
max_frames=cfg.output.max_frames,
fps=cfg.output.fps,
num_workers=cfg.output.num_workers,
exclude_wrist_cam=cfg.dataset.exclude_wrist_cam,
)
# Handle pushing/saving consistently
if cfg.hub.push_to_hub and cfg.hub.hub_repo_id:
print(f"\nPushing dataset to HuggingFace Hub: {cfg.hub.hub_repo_id}")
try:
push_hf_dataset_and_video_files_to_hub(
dataset, cfg.hub.hub_repo_id, cfg.hub.hub_token, cfg.dataset.dataset_name, cfg.output.output_dir
)
except Exception as e:
print(f"❌ Error pushing to hub: {e}")
print("Dataset was created locally but failed to push metadata to hub")
else:
dataset_path_local = os.path.join(cfg.output.output_dir, (cfg.dataset.dataset_name).lower())
dataset.save_to_disk(dataset_path_local)
print(f"Dataset saved locally to: {dataset_path_local}")
print("Dataset conversion complete!")
return
elif "utd_so101_clean_policy_ranking" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.utd_so101_clean_policy_ranking_loader import (
convert_utd_so101_clean_policy_ranking_to_hf,
)
# Determine view from dataset name
if "wrist" in cfg.dataset.dataset_name.lower():
view = "wrist"
elif "top" in cfg.dataset.dataset_name.lower():
view = "top"
else:
raise ValueError(f"Dataset name must specify view (wrist or top): {cfg.dataset.dataset_name}")
print(f"Converting UTD SO101 Clean Policy Ranking ({view} view) to HF from: {cfg.dataset.dataset_path}")
dataset = convert_utd_so101_clean_policy_ranking_to_hf(
dataset_path=cfg.dataset.dataset_path,
dataset_name=cfg.dataset.dataset_name,
output_dir=cfg.output.output_dir,
view=view,
max_trajectories=cfg.output.max_trajectories,
max_frames=cfg.output.max_frames,
fps=cfg.output.fps,
num_workers=cfg.output.num_workers,
)
# Handle pushing/saving consistently
if cfg.hub.push_to_hub and cfg.hub.hub_repo_id:
print(f"\nPushing dataset to HuggingFace Hub: {cfg.hub.hub_repo_id}")
try:
push_hf_dataset_and_video_files_to_hub(
dataset, cfg.hub.hub_repo_id, cfg.hub.hub_token, cfg.dataset.dataset_name, cfg.output.output_dir
)
except Exception as e:
print(f"❌ Error pushing to hub: {e}")
print("Dataset was created locally but failed to push metadata to hub")
else:
dataset_path_local = os.path.join(cfg.output.output_dir, (cfg.dataset.dataset_name).lower())
dataset.save_to_disk(dataset_path_local)
print(f"Dataset saved locally to: {dataset_path_local}")
print("Dataset conversion complete!")
return
elif "usc_koch_human_robot_paired" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.usc_koch_human_robot_paired_loader import (
convert_usc_koch_human_robot_paired_to_hf,
)
# Determine trajectory type from dataset name
if "usc_koch_human_robot_paired_human" in cfg.dataset.dataset_name.lower():
trajectory_type = "human"
elif "usc_koch_human_robot_paired_robot" in cfg.dataset.dataset_name.lower():
trajectory_type = "robot"
else:
raise ValueError(
f"Dataset name must specify either 'usc_koch_human_robot_paired_human' or 'usc_koch_human_robot_paired_robot': {cfg.dataset.dataset_name}. "
)
print(f"Converting USC Koch Human-Robot Paired ({trajectory_type}) to HF from: {cfg.dataset.dataset_path}")
dataset = convert_usc_koch_human_robot_paired_to_hf(
dataset_path=cfg.dataset.dataset_path,
dataset_name=cfg.dataset.dataset_name,
output_dir=cfg.output.output_dir,
trajectory_type=trajectory_type,
max_trajectories=cfg.output.max_trajectories,
max_frames=cfg.output.max_frames,
fps=cfg.output.fps,
num_workers=cfg.output.num_workers,
)
# Handle pushing/saving consistently
if cfg.hub.push_to_hub and cfg.hub.hub_repo_id:
updated_repo_id = cfg.hub.hub_repo_id.replace("usc_koch_human_robot_paired_", "")
print(f"\nPushing dataset to HuggingFace Hub: {updated_repo_id}")
try:
push_hf_dataset_and_video_files_to_hub(
dataset, updated_repo_id, cfg.hub.hub_token, cfg.dataset.dataset_name, cfg.output.output_dir
)
except Exception as e:
print(f"❌ Error pushing to hub: {e}")
print("Dataset was created locally but failed to push metadata to hub")
else:
dataset_path_local = os.path.join(output_dir_override, (cfg.dataset.dataset_name).lower())
dataset.save_to_disk(dataset_path_local)
print(f"Dataset saved locally to: {dataset_path_local}")
print("Dataset conversion complete!")
return
elif "usc_koch_p_ranking" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.usc_koch_p_ranking_loader import ( # type: ignore
convert_usc_koch_p_ranking_to_hf,
)
output_dir_override = os.path.join(os.path.dirname(cfg.output.output_dir), cfg.dataset.dataset_name.lower())
print(f"Converting USC Koch P-Ranking to HF from: {cfg.dataset.dataset_path}")
dataset = convert_usc_koch_p_ranking_to_hf(
dataset_path=cfg.dataset.dataset_path,
dataset_name=cfg.dataset.dataset_name,
output_dir=output_dir_override,
max_trajectories=cfg.output.max_trajectories,
max_frames=cfg.output.max_frames,
fps=cfg.output.fps,
num_workers=cfg.output.num_workers,
)
# Handle pushing/saving consistently
if cfg.hub.push_to_hub and cfg.hub.hub_repo_id:
updated_repo_id = cfg.hub.hub_repo_id.replace("usc_koch_p_ranking_rfm", "")
print(f"\nPushing dataset to HuggingFace Hub: {updated_repo_id}")
try:
push_hf_dataset_and_video_files_to_hub(
dataset, updated_repo_id, cfg.hub.hub_token, cfg.dataset.dataset_name, output_dir_override
)
except Exception as e:
print(f"❌ Error pushing to hub: {e}")
print("Dataset was created locally but failed to push metadata to hub")
else:
dataset_path_local = os.path.join(output_dir_override, (cfg.dataset.dataset_name).lower())
dataset.save_to_disk(dataset_path_local)
print(f"Dataset saved locally to: {dataset_path_local}")
print("Dataset conversion complete!")
return
elif "egocot" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.egocot_loader import load_egocot_dataset
# Load the trajectories using the loader
print(f"Loading EgoCoT dataset from: {cfg.dataset.dataset_path}")
task_data = load_egocot_dataset(
cfg.dataset.dataset_path,
)
trajectories = flatten_task_data(task_data)
elif "humanoid_everyday" in cfg.dataset.dataset_name.lower():
# Stream + convert directly (OXE-style)
from dataset_upload.dataset_loaders.humanoid_everyday_loader import convert_humanoid_everyday_dataset_to_hf
print(f"Converting Humanoid Everyday dataset directly to HF from: {cfg.dataset.dataset_path}")
dataset = convert_humanoid_everyday_dataset_to_hf(
dataset_path=cfg.dataset.dataset_path,
dataset_name=cfg.dataset.dataset_name,
output_dir=cfg.output.output_dir,
max_trajectories=cfg.output.max_trajectories,
max_frames=cfg.output.max_frames,
fps=cfg.output.fps,
num_workers=cfg.output.num_workers,
)
# Handle pushing/saving consistently
if cfg.hub.push_to_hub and cfg.hub.hub_repo_id:
print(f"\nPushing dataset to HuggingFace Hub: {cfg.hub.hub_repo_id}")
try:
push_hf_dataset_and_video_files_to_hub(
dataset, cfg.hub.hub_repo_id, cfg.hub.hub_token, cfg.dataset.dataset_name, cfg.output.output_dir
)
except Exception as e:
print(f"❌ Error pushing to hub: {e}")
print("Dataset was created locally but failed to push videos and/or metadata to hub")
else:
dataset_path = os.path.join(cfg.output.output_dir, (cfg.dataset.dataset_name).lower())
dataset.save_to_disk(dataset_path)
print(f"Dataset saved locally to: {dataset_path}")
print("Dataset conversion complete!")
return
elif "motif" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.motif_loader import load_motif_dataset
print(f"Loading MotIF dataset from: {cfg.dataset.dataset_path}")
task_data = load_motif_dataset(cfg.dataset.dataset_path)
trajectories = flatten_task_data(task_data)
elif "failsafe" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.failsafe_loader import load_failsafe_dataset
print(f"Loading FailSafe dataset from: {cfg.dataset.dataset_path}")
task_data = load_failsafe_dataset(cfg.dataset.dataset_path)
trajectories = flatten_task_data(task_data)
elif "racer" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.racer_loader import load_racer_dataset
print(f"Loading RACER dataset from: {cfg.dataset.dataset_path}")
task_data = load_racer_dataset(cfg.dataset.dataset_path, cfg.dataset.dataset_name)
trajectories = flatten_task_data(task_data)
elif "hand_paired" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.hand_paired_loader import load_hand_paired_dataset
print(f"Loading HAND_paired dataset from: {cfg.dataset.dataset_path}")
task_data = load_hand_paired_dataset(cfg.dataset.dataset_path, cfg.dataset.dataset_name)
trajectories = flatten_task_data(task_data)
elif "roboreward" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.roboreward_loader import load_roboreward_dataset
print(f"Loading RoboReward dataset from: {cfg.dataset.dataset_path}")
task_data = load_roboreward_dataset(cfg.dataset.dataset_path, cfg.dataset.dataset_name)
trajectories = flatten_task_data(task_data)
elif "robofac" in cfg.dataset.dataset_name.lower():
from dataset_upload.dataset_loaders.robofac_loader import load_robofac_dataset
print(f"Loading RoboFAC dataset from: {cfg.dataset.dataset_path}")
task_data = load_robofac_dataset(
cfg.dataset.dataset_path,
max_trajectories=cfg.output.max_trajectories,
)
trajectories = flatten_task_data(task_data)
else:
raise ValueError(f"Unknown dataset type: {cfg.dataset.dataset_name}")
# Convert dataset (non-streaming datasets)
convert_dataset_to_hf_format(
trajectories=trajectories,
hf_creator_fn=partial(
create_hf_trajectory,
dataset_name=cfg.dataset.dataset_name,
use_video=cfg.output.use_video,
fps=cfg.output.fps,
shortest_edge_size=cfg.output.shortest_edge_size,
center_crop=cfg.output.center_crop,
hub_repo_id=cfg.hub.hub_repo_id,
),
output_dir=cfg.output.output_dir,
dataset_name=cfg.dataset.dataset_name,
max_trajectories=cfg.output.max_trajectories,
max_frames=cfg.output.max_frames,
use_video=cfg.output.use_video,
fps=cfg.output.fps,
num_workers=cfg.output.num_workers,
push_to_hub=cfg.hub.push_to_hub,
hub_repo_id=cfg.hub.hub_repo_id,
hub_token=cfg.hub.hub_token,
)
print("Dataset conversion complete!")
if __name__ == "__main__":
main()
|