File size: 7,513 Bytes
68eed61 | 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 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | """Read-only, CPU audit of the current full-window WidowX data contract."""
import argparse
from collections import Counter
from concurrent.futures import ThreadPoolExecutor
import json
from pathlib import Path
import numpy as np
import pyarrow.parquet as pq
def audit(root, workers):
info = json.loads((root / 'meta/info.json').read_text())
episodes = [json.loads(line) for line in (root / 'meta/episodes.jsonl').open()]
tasks = {row['task_index']: row['task'] for row in map(json.loads, (root / 'meta/tasks.jsonl').open())}
counts = Counter()
starts_histogram = Counter()
task_windows = Counter()
lag_sum = {lag: np.zeros(6, np.float64) for lag in (-2, -1, 0, 1, 2)}
lag_count = Counter()
per_offset_closed = np.zeros(32, np.int64)
target_tasks = {
task: {'episodes': 0, 'retained_episodes': 0, 'windows': 0,
'closed_proprio_windows': 0, 'starts': Counter(),
'closed_per_offset': np.zeros(32, np.int64)}
for task in ('put carrot on plate', 'take carrot off plate')
}
q01 = np.array([-0.028752606511116028, -0.041702210046350954, -0.026096698231995105,
-0.08052822157740593, -0.09249736212193965, -0.20738411962985992])
q99 = np.array([0.02830690816044803, 0.04089860741049051, 0.04018005654215808,
0.08173405691981314, 0.07760896608233431, 0.20384809583425495])
clipped = np.zeros(6, np.int64)
invalid = []
def read(ep):
idx = ep['episode_index']
path = root / info['data_path'].format(episode_chunk=idx // info['chunks_size'], episode_index=idx)
rows = pq.read_table(path, columns=['observation.state', 'action', 'task_index'], use_threads=False).to_pydict()
return ep, np.asarray(rows['observation.state']), np.asarray(rows['action']), np.asarray(rows['task_index'])
with ThreadPoolExecutor(max_workers=workers) as pool:
def bounded_rows():
for offset in range(0, len(episodes), workers * 16):
yield from pool.map(read, episodes[offset:offset + workers * 16])
for number, (ep, state, action, task_ids) in enumerate(bounded_rows(), 1):
idx, length = ep['episode_index'], ep['length']
if len(action) != length or state.shape != (length, 8) or action.shape != (length, 7):
invalid.append({'episode': idx, 'reason': 'shape_or_length'})
continue
if not np.isfinite(state).all() or not np.isfinite(action).all():
invalid.append({'episode': idx, 'reason': 'nonfinite'})
continue
starts = np.arange(0, max(0, length - 32), 4)
closed = action[:, 6] <= .5
empty = np.array([not tasks[int(t)].strip() for t in task_ids])
task_name = tasks[int(task_ids[0])]
if task_name in target_tasks:
target = target_tasks[task_name]
target['episodes'] += 1
target['retained_episodes'] += int(len(starts) > 0)
target['windows'] += len(starts)
target['closed_proprio_windows'] += int((state[starts, 7] <= .5).sum())
target['starts'].update(map(int, starts))
for start in starts:
target['closed_per_offset'] += closed[start:start + 32]
counts['episodes'] += 1
counts['frames'] += length
counts['closed_action_frames'] += int(closed.sum())
counts['empty_instruction_frames'] += int(empty.sum())
counts['episodes_with_empty_instruction'] += int(empty.any())
counts['episodes_with_multiple_task_ids'] += int(len(set(task_ids.tolist())) > 1)
counts['nonbinary_gripper_action_frames'] += int(((action[:, 6] != 0) & (action[:, 6] != 1)).sum())
counts['nonzero_state_pad_frames'] += int((state[:, 6] != 0).sum())
clipped += ((action[:, :6] < q01) | (action[:, :6] > q99)).sum(0)
if len(starts):
counts['retained_episodes'] += 1
counts['windows'] += len(starts)
counts['empty_instruction_windows'] += int(empty[starts].sum())
counts['closed_proprio_windows'] += int((state[starts, 7] <= .5).sum())
counts['windows_at_or_after_halfway'] += int((starts >= (length - 1) / 2).sum())
counts['windows_with_completed_effect_history'] += int((starts >= 16).sum())
counts['windows_with_full_four_effect_history'] += int((starts >= 64).sum())
starts_histogram.update(map(int, starts))
task_windows.update(tasks[int(task_ids[s])] for s in starts)
for s in starts:
per_offset_closed += closed[s:s + 32]
first4_ids = (starts[:, None] + np.arange(4)).ravel()
counts['first4_closed_action_presentations'] += int(closed[first4_ids].sum())
counts['first4_action_presentations'] += len(first4_ids)
covered_end = int(starts[-1]) + 32
counts['unique_action_frames_supervised'] += covered_end
counts['first4_unique_action_frames'] += len(first4_ids)
if 'carrot' in tasks[int(task_ids[0])].lower() and 'plate' in tasks[int(task_ids[0])].lower():
counts['carrot_plate_retained_episodes'] += 1
counts['carrot_plate_windows'] += len(starts)
if 'carrot' in tasks[int(task_ids[0])].lower() and 'plate' in tasks[int(task_ids[0])].lower():
counts['carrot_plate_all_episodes'] += 1
delta = np.diff(state[:, :6], axis=0)
for lag in lag_sum:
t = np.arange(len(delta))
valid = (t + lag >= 0) & (t + lag < length)
error = np.abs(action[t[valid] + lag, :6] - delta[t[valid]])
lag_sum[lag] += error.sum(0)
lag_count[lag] += len(error)
if number % 5000 == 0:
print(json.dumps({'scanned_episodes': number, 'total': len(episodes)}), flush=True)
for target in target_tasks.values():
target['starts'] = dict(sorted(target['starts'].items()))
target['closed_action_fraction_by_prediction_offset'] = (
target.pop('closed_per_offset') / max(1, target['windows'])
).tolist()
return dict(
root=str(root), scope='all_episode_parquets_no_video_decode', counts=dict(counts),
starts_histogram=dict(sorted(starts_histogram.items())),
closed_action_fraction_by_prediction_offset=(per_offset_closed / counts['windows']).tolist(),
action_clipped_fraction_by_dim=(clipped / counts['frames']).tolist(),
action_vs_state_delta_mae_by_action_lag={str(k): (v / lag_count[k]).tolist() for k, v in lag_sum.items()},
target_tasks=target_tasks,
largest_window_tasks=task_windows.most_common(20), invalid=invalid,
)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--root', type=Path, required=True)
parser.add_argument('--output', type=Path, required=True)
parser.add_argument('--workers', type=int, default=4)
args = parser.parse_args()
report = audit(args.root, args.workers)
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(json.dumps(report, indent=2, ensure_ascii=False))
print(json.dumps(report, indent=2, ensure_ascii=False), flush=True)
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