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https://github.com/huggingface/lerobot.git
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new things
This commit is contained in:
Jade Choghari
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#!/usr/bin/env python3
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"""
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Convert local LeRobot datasets from v2.0 to v2.1 format.
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This script adapts the official converter to work with local datasets.
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"""
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import sys
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import argparse
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import logging
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from pathlib import Path
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# Add lerobot to path
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sys.path.insert(0, '/home/jade_choghari/lerobot/src')
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from lerobot.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
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from lerobot.datasets.utils import EPISODES_STATS_PATH, STATS_PATH, load_stats, write_info
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from lerobot.datasets.v21.convert_stats import check_aggregate_stats, convert_stats
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def convert_local_dataset(
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dataset_path: str,
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num_workers: int = 4,
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skip_if_converted: bool = True
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):
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"""
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Convert a local dataset from v2.0 to v2.1 format.
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Args:
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dataset_path: Path to the local dataset directory
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num_workers: Number of workers for parallel processing
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skip_if_converted: Skip if already has episodes_stats.jsonl
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"""
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dataset_path = Path(dataset_path)
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print(f"🔄 Converting local dataset: {dataset_path}")
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# Check if already converted
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episodes_stats_path = dataset_path / "meta" / "episodes_stats.jsonl"
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if episodes_stats_path.exists() and skip_if_converted:
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# Check if file is empty
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file_size = episodes_stats_path.stat().st_size
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if file_size == 0:
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print(f" ⚠️ episodes_stats.jsonl is empty, will regenerate")
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else:
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# Check if file has content
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with open(episodes_stats_path, 'r') as f:
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content = f.read().strip()
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if not content:
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print(f" ⚠️ episodes_stats.jsonl has no content, will regenerate")
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else:
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print(f" ⏭️ Already has episodes_stats.jsonl, skipping")
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return True
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try:
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# Check if this is a v2.0 dataset that needs conversion
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episodes_stats_path = dataset_path / "meta" / "episodes_stats.jsonl"
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stats_path = dataset_path / "meta" / "stats.json"
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if not episodes_stats_path.exists() and stats_path.exists():
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print(f" 🔄 Detected v2.0 dataset, creating temporary episodes_stats.jsonl...")
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# Create empty episodes_stats.jsonl to allow loading
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episodes_stats_path.touch()
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created_temp_file = True
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else:
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created_temp_file = False
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# Load dataset from local path with pyav video backend
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print(f" 📂 Loading dataset from local path...")
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# Use a dummy repo_id since we're loading locally
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dummy_repo_id = f"{dataset_path.parent.name}/{dataset_path.name}"
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dataset = LeRobotDataset(
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dummy_repo_id,
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root=str(dataset_path),
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# video_backend="pyav",
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# local_files_only=True
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)
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# Remove temporary file if we created it
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if created_temp_file and episodes_stats_path.exists() and episodes_stats_path.stat().st_size == 0:
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episodes_stats_path.unlink()
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print(f" 🗑️ Removed temporary episodes_stats.jsonl")
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# Remove existing episodes_stats if present (ensure clean conversion)
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episodes_stats_path = dataset_path / "meta" / "episodes_stats.jsonl"
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if episodes_stats_path.exists():
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episodes_stats_path.unlink()
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print(f" 🗑️ Removed existing episodes_stats.jsonl")
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# Check if video directory exists before conversion
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videos_dir = dataset_path / "videos"
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if not videos_dir.exists():
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print(f" ⚠️ No videos directory found - will skip video statistics")
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# Convert stats
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print(f" 📊 Computing episode statistics...")
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convert_stats(dataset, num_workers=num_workers)
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# Load reference stats for validation if they exist
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stats_path = dataset.root / STATS_PATH
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if stats_path.exists():
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print(f" ✅ Validating against reference stats...")
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try:
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ref_stats = load_stats(dataset.root)
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check_aggregate_stats(dataset, ref_stats)
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print(f" ✅ Stats validation passed!")
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except AssertionError as e:
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print(f" ⚠️ Stats validation failed with minor differences: {e}")
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print(f" ⚠️ This is likely due to floating-point precision, continuing anyway...")
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# Check if the error is just a small numerical difference
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if "Max absolute difference:" in str(e) and "Max relative difference:" in str(e):
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print(f" ✅ Treating as acceptable numerical precision difference")
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else:
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raise e
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# Remove old stats.json file
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print(f" 🗑️ Removing old stats.json")
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stats_path.unlink()
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else:
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print(f" ⚠️ No reference stats found, skipping validation")
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# Update codebase version
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dataset.meta.info["codebase_version"] = CODEBASE_VERSION
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write_info(dataset.meta.info, dataset.root)
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print(f" ✅ Successfully converted to v2.1")
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return True
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except Exception as e:
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print(f" ❌ Failed to convert: {e}")
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logger.exception("Conversion failed")
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return False
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def convert_multiple_datasets(
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base_dirs: list[str],
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max_datasets: int = None,
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num_workers: int = 4
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):
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"""Convert multiple datasets from base directories."""
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datasets_to_convert = []
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# Scan for datasets needing conversion
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for base_dir in base_dirs:
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base_path = Path(base_dir)
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if not base_path.exists():
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print(f"⚠️ Directory not found: {base_dir}")
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continue
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print(f"🔍 Scanning: {base_dir}")
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# Walk through author/dataset structure
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for author_dir in sorted(base_path.iterdir()):
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if not author_dir.is_dir():
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continue
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for dataset_dir in sorted(author_dir.iterdir()):
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if not dataset_dir.is_dir():
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continue
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# Check if needs conversion
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episodes_stats_path = dataset_dir / "meta" / "episodes_stats.jsonl"
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info_path = dataset_dir / "meta" / "info.json"
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needs_conversion = False
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if info_path.exists():
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if not episodes_stats_path.exists():
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needs_conversion = True
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print(f" 📝 Found (missing): {author_dir.name}/{dataset_dir.name}")
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else:
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# Check if episodes_stats file is empty
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try:
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file_size = episodes_stats_path.stat().st_size
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if file_size == 0:
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needs_conversion = True
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print(f" 📝 Found (empty): {author_dir.name}/{dataset_dir.name}")
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else:
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# Check if file has content
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with open(episodes_stats_path, 'r') as f:
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content = f.read().strip()
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if not content:
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needs_conversion = True
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print(f" 📝 Found (no content): {author_dir.name}/{dataset_dir.name}")
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except Exception as e:
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# If we can't read the file, consider it needs conversion
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needs_conversion = True
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print(f" 📝 Found (read error): {author_dir.name}/{dataset_dir.name}")
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if needs_conversion:
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datasets_to_convert.append(dataset_dir)
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if not datasets_to_convert:
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print("🎉 No datasets need conversion!")
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return
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if max_datasets:
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datasets_to_convert = datasets_to_convert[:max_datasets]
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print(f"\n🚀 Converting {len(datasets_to_convert)} datasets...")
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successful = 0
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failed = 0
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for i, dataset_path in enumerate(datasets_to_convert, 1):
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print(f"\n[{i}/{len(datasets_to_convert)}] {dataset_path.parent.name}/{dataset_path.name}")
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success = convert_local_dataset(dataset_path, num_workers=num_workers)
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if success:
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successful += 1
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else:
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failed += 1
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print(f"\n📊 Conversion Summary:")
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print(f" ✅ Successful: {successful}")
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print(f" ❌ Failed: {failed}")
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print(f" 📈 Success rate: {successful}/{len(datasets_to_convert)} ({100*successful/len(datasets_to_convert):.1f}%)")
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def main():
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parser = argparse.ArgumentParser(description="Convert local LeRobot datasets to v2.1 format")
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parser.add_argument("--dataset", type=str, help="Single dataset path to convert")
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parser.add_argument("--base-dirs", nargs="+",
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default=["/fsx/dana_aubakirova/vla/community_dataset_v1"],
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help="Base directories to scan for datasets")
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parser.add_argument("--max-datasets", type=int, help="Maximum number of datasets to convert")
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parser.add_argument("--num-workers", type=int, default=4, help="Number of workers for stats computation")
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parser.add_argument("--all", action="store_true", help="Convert all datasets in base directories")
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args = parser.parse_args()
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if args.dataset:
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# Convert single dataset
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success = convert_local_dataset(args.dataset, num_workers=args.num_workers)
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if success:
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print(f"\n🎉 Successfully converted: {args.dataset}")
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else:
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print(f"\n💥 Failed to convert: {args.dataset}")
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sys.exit(1)
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elif args.all:
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# Convert all datasets
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convert_multiple_datasets(
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args.base_dirs,
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max_datasets=args.max_datasets,
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num_workers=args.num_workers
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)
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else:
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parser.print_help()
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if __name__ == "__main__":
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main()
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