Datasets:
File size: 3,002 Bytes
e2e9f8a | 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 | """Check that every counted leaderboard attempt has its run on HF (works without a token).
usage: python3 check_coverage.py [trials.csv] [--revision REV]
For each row of trials.csv (one row per counted attempt), it checks in the dataset MLL-Lab/embodied-exam-runs:
- runs/<h2>/<slug>/run.json and trajectory.jsonl.gz exist, and replay.mp4 exists when has_video = 1;
- the slug has a row in index/<suite>.json whose run and outcome equal the leaderboard's.
It lists the runs/<h2>/ folders through the HF API (at most 256 calls) and reads the index files. Without a token,
HF's rate limit may make it wait a few minutes; with HF_TOKEN set it runs in about a minute.
Exit status 0 only at 100% coverage.
"""
import argparse, collections, csv, hashlib, json, sys, time, urllib.request
from huggingface_hub import HfApi
REPO = 'MLL-Lab/embodied-exam-runs'
ap = argparse.ArgumentParser(); ap.add_argument('trials', nargs='?', default='trials.csv'); ap.add_argument('--revision', default='main')
a = ap.parse_args()
api = HfApi()
rev = api.dataset_info(REPO, revision=a.revision).sha
rows = list(csv.DictReader(open(a.trials)))
h2 = lambda s: hashlib.sha1(s.encode()).hexdigest()[:2]
files = set()
def tree(d): # anonymous calls are rate limited (a few hundred per 5 min): wait and retry
for k in range(20):
try:
return [e.path for e in api.list_repo_tree(REPO, path_in_repo=f'runs/{d}', recursive=True, repo_type='dataset', revision=rev)]
except Exception as e:
if '429' not in str(e) and 'rate limit' not in str(e).lower() or k == 19: raise
print('rate limited, waiting 60 s', file=sys.stderr, flush=True); time.sleep(60)
for d in sorted({h2(r['hf_slug']) for r in rows}):
files.update(tree(d))
def get(path): # plain urllib: no compressed transfer
return json.loads(urllib.request.urlopen(f'https://huggingface.co/datasets/{REPO}/resolve/{rev}/{path}', timeout=300).read())
idx = get('index.json')
irow = {}
for s in idx['suites']:
for x in get(f'index/{s}.json')['runs']:
irow[x['slug']] = x
c = collections.Counter(); bad = []
for r in rows:
s = r['hf_slug']; base = f'runs/{h2(s)}/{s}/'
need = ['run.json', 'trajectory.jsonl.gz'] + (['replay.mp4'] if r['has_video'] == '1' else [])
miss = [f for f in need if base + f not in files]
x = irow.get(s)
ok_idx = bool(x) and x['run'] == r['run_id'] and ((x.get('success') == 1 or x.get('state') == 'success') == (r['verdict'] == 'success'))
if miss or not ok_idx:
bad.append((r['run_id'], r['task_dir'], s, miss, 'index ok' if ok_idx else ('index outcome differs' if x else 'not in index')))
else:
c['covered'] += 1
c['with video'] += r['has_video'] == '1'
print(f'{REPO}@{rev[:10]}: {c["covered"]} of {len(rows)} counted attempts covered '
f'(run.json + trajectory + index row with the same outcome; replay.mp4 for the {c["with video"]} with a video)')
for b in bad: print('MISSING', *b)
sys.exit(0 if not bad else 1)
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