File size: 3,613 Bytes
a0ed151 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | """Read supplemental simulation One-Shot labels beside a LeRobot v3.0 dataset.
The sidecar labels the active semantic subtask, not reward or success. Semantic
boundary adjustment bands are independent of LeRobot video-loading tolerance.
Requires only the Python standard library and PyArrow.
"""
from pathlib import Path, PurePosixPath
import hashlib
import json
import pyarrow.parquet as pq
class SubtaskAnnotations:
def __init__(self, dataset_root, verify_manifest=True):
self.dataset_root = Path(dataset_root)
self.annotation_root = self.dataset_root / "annotations" / "subtasks"
self.metadata = json.loads((self.annotation_root / "metadata.json").read_text())
if verify_manifest:
manifest = json.loads((self.annotation_root / "manifest.json").read_text())
for entry in manifest["files"]:
name = PurePosixPath(entry["path"])
if name.is_absolute() or ".." in name.parts:
raise ValueError("Invalid manifest-relative file path")
payload = (self.annotation_root / name).read_bytes()
if len(payload) != entry["bytes"] or hashlib.sha256(payload).hexdigest() != entry["sha256"]:
raise ValueError("Annotation manifest checksum mismatch")
info_path = self.dataset_root / "meta" / "info.json"
if info_path.exists():
info = json.loads(info_path.read_text())
for key in ("fps", "total_episodes", "total_frames"):
if info.get(key) != self.metadata["dataset"][key]:
raise ValueError("Dataset/annotation metadata mismatch: " + key)
if info.get("codebase_version") != "v3.0":
raise ValueError("Expected a LeRobot v3.0 dataset")
self.table = pq.read_table(self.annotation_root / "frame_labels.parquet")
self.by_frame = {}
global_indices = set()
for row in self.table.to_pylist():
key = (row["episode_index"], row["frame_index"])
if key in self.by_frame or row["global_index"] in global_indices:
raise ValueError("Duplicate annotation frame/global index")
self.by_frame[key] = row
global_indices.add(row["global_index"])
if len(self.by_frame) != self.metadata["dataset"]["total_frames"]:
raise ValueError("Annotation frame count mismatch")
if global_indices != set(range(self.metadata["dataset"]["total_frames"])):
raise ValueError("Annotation global index grid mismatch")
for episode in self.metadata["episodes"]:
ep = episode["episode_index"]
for frame in range(episode["length"]):
row = self.by_frame.get((ep, frame))
if row is None or row["global_index"] != episode["dataset_from_index"] + frame:
raise ValueError("Annotation episode/frame/global index mismatch")
def label(self, episode_index, frame_index, global_index=None):
row = self.by_frame[(int(episode_index), int(frame_index))]
if global_index is not None and row["global_index"] != int(global_index):
raise ValueError("LeRobot sample index does not match annotation")
return dict(row)
def for_sample(self, sample):
"""Attach explicitly to a scalar LeRobotDataset sample, before collation."""
return self.label(sample["episode_index"], sample["frame_index"], sample["index"])
def load_subtasks(dataset_root, verify_manifest=True):
return SubtaskAnnotations(dataset_root, verify_manifest=verify_manifest)
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