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ab10f3f | 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 | """Turn RoboQuest-Tool-Call trajectories into per-step training rows with a chosen image horizon.
python make_rows.py --trajectories puzzle_box/trajectories.jsonl --out rows_h2.jsonl --image-horizon 2
Each trajectory is one full episode conversation (system, then per decision: observation, assistant tool call,
tool result) with the three camera images on every observation. For decision k the row holds the conversation up
to observation k as `messages` and the k-th assistant tool call as `target`. Only the newest `--image-horizon`
observations keep their image parts (the camera label texts stay); `all` keeps every image. The RoboQuest eval
harness uses 2, and `--image-horizon 2` reproduces the published rows.jsonl exactly.
Standard library only.
"""
import argparse
import json
def rows_from_trajectory(t, horizon):
msgs, out = t['messages'], []
users = [i for i, m in enumerate(msgs) if m['role'] == 'user']
calls = [i for i, m in enumerate(msgs) if m['role'] == 'assistant']
assert len(users) == len(calls) == t['n_decisions']
for k, (u, a) in enumerate(zip(users, calls), start=1):
keep = set(users[:k] if horizon is None else users[max(0, k - horizon):k])
messages = []
for i in range(u + 1):
m = msgs[i]
if m['role'] == 'user' and i not in keep:
m = {**m, 'content': [p for p in m['content'] if p.get('type') != 'image']}
messages.append(m)
turn = t['turns'][k - 1]
out.append({'id': f"{t['task']}__{t['instance_id']}__{k:03d}", 'task': t['task'],
'instance_id': t['instance_id'], 'k': k, 'messages': messages, 'tools': t['tools'],
'target': msgs[a], 'tier': turn['tier'], 'subtask': turn['subtask'],
'subtask_kind': turn['subtask_kind']})
return out
def main():
ap = argparse.ArgumentParser()
ap.add_argument('--trajectories', required=True)
ap.add_argument('--out', required=True)
ap.add_argument('--image-horizon', default='2', help="observations that keep images (an integer, or 'all')")
args = ap.parse_args()
horizon = None if args.image_horizon == 'all' else int(args.image_horizon)
n = 0
with open(args.trajectories) as f, open(args.out, 'w') as out:
for line in f:
for row in rows_from_trajectory(json.loads(line), horizon):
out.write(json.dumps(row, separators=(',', ':')) + '\n')
n += 1
print('rows', n)
if __name__ == '__main__':
main()
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