cbb
ce9edd7934
- select='endpoint'（候选块末态离目标最近）优于 consist：n=512 到达 +1.3pp、time_ok +6.3pp、ratio −0.058 - 负结论：safety_try 1/2（41.4/44.9%）、K=4（47.3%）、DLS 兜底（52.3%/碰撞+1.1pp）、增益调度（47.3%） - JS Runner：endpoint 选择落地、冷启动跳前瞻；starveTicks 指标；commit2 平均空转 0.8–1.3 tick，commit3 可覆盖 - route40 endpoint：12/20 ok、13/20 到达、刹车 40.4%
83 lines
4.3 KiB
Python
83 lines
4.3 KiB
Python
"""小规模闭环评估（部署口径）：
|
|
python -m ml.eval.eval_cl --n 256 --commit 3 --fb 1.8 --partial [--ki 0.1 --select consist]
|
|
输出：到达 / 碰撞 / ratio2s / 刹停 / 精修接管 / 墙钟。直接调 rollout，参数与部署一致。"""
|
|
import argparse, functools, json, time
|
|
import torch
|
|
|
|
from ml.common.ckpt import load_policy
|
|
from ml.common.kin_torch import Kin
|
|
from ml.data import load_tasks
|
|
from ml.sim.closed_loop import rollout
|
|
import ml.sim.closed_loop as clmod
|
|
|
|
|
|
def run(model, kin, tasks, device, bs=256, **kw):
|
|
agg, per = {}, []
|
|
for i in range(0, len(tasks), bs):
|
|
r, p = rollout(model, kin, tasks[i:i + bs], device, **kw)
|
|
per.append((r, p, len(tasks[i:i + bs])))
|
|
n = sum(w for _, _, w in per)
|
|
for r, _, w in per:
|
|
for k, v in r.items():
|
|
if k in ('n', 'time_budget') or v is None: continue
|
|
agg[k] = agg.get(k, 0.0) + v * w
|
|
out = {k: v / n for k, v in agg.items()}; out['n'] = n
|
|
out['goal_ratio_2s'] = torch.cat([p['goal_ratio_2s'] for _, p, _ in per]).median().item()
|
|
out['err_fail_med'] = torch.cat([p['err'] for _, p, _ in per])[~torch.cat([p['ok'] for _, p, _ in per])].median().item()
|
|
out['dls_share'] = torch.cat([p['dls_it'] for _, p, _ in per]).ge(0).float().mean().item()
|
|
out['time_budget'] = per[0][0]['time_budget']
|
|
return out
|
|
|
|
|
|
def main():
|
|
ap = argparse.ArgumentParser()
|
|
ap.add_argument('--ckpt', default='ml/runs/base_dag2_dag1/best.pt')
|
|
ap.add_argument('--samples', default='ml/data/samples')
|
|
ap.add_argument('--tasks', default=None)
|
|
ap.add_argument('--split', default='val')
|
|
ap.add_argument('--n', type=int, default=256)
|
|
ap.add_argument('--K', type=int, default=8)
|
|
ap.add_argument('--commit', type=int, default=3)
|
|
ap.add_argument('--fb', type=float, default=1.8)
|
|
ap.add_argument('--ki', type=float, default=0.0)
|
|
ap.add_argument('--fb_scale', type=float, default=0.0)
|
|
ap.add_argument('--fb_near', type=float, default=0.2)
|
|
ap.add_argument('--fb_fallback', type=float, default=0.0)
|
|
ap.add_argument('--kdecay', type=float, default=0.9)
|
|
ap.add_argument('--kimax', type=float, default=0.5)
|
|
ap.add_argument('--fb_every', type=int, default=1)
|
|
ap.add_argument('--safety_try', type=int, default=0)
|
|
ap.add_argument('--sel_w', type=float, default=0.1)
|
|
ap.add_argument('--select', default='consist')
|
|
ap.add_argument('--replan', default='r3')
|
|
ap.add_argument('--safety', type=int, default=1, help='1=安全层+部分前缀；0=关')
|
|
ap.add_argument('--partial', type=int, default=1)
|
|
ap.add_argument('--max_sec', type=float, default=None)
|
|
ap.add_argument('--seed', type=int, default=0)
|
|
ap.add_argument('--dls', default='analytic', choices=['analytic', 'fd'])
|
|
ap.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu')
|
|
a = ap.parse_args()
|
|
if a.dls == 'fd':
|
|
clmod.dls_steps = functools.partial(clmod.dls_steps, analytic=False)
|
|
model, ck = load_policy(a.ckpt, a.device)
|
|
kin = Kin(device=a.device)
|
|
assert ck.get('fp') in (None, kin.fp)
|
|
tasks = ([json.loads(l) for l in open(a.tasks, encoding='utf-8') if l.strip()] if a.tasks
|
|
else [t for t in load_tasks([a.samples]) if t['split'] == a.split])
|
|
tasks = tasks[:a.n]
|
|
t0 = time.perf_counter()
|
|
r = run(model, kin, tasks, a.device, K=a.K, safety=bool(a.safety), seed=a.seed, max_sec=a.max_sec,
|
|
safety_partial=(a.commit if (a.safety and a.partial) else 0), commit_n=a.commit,
|
|
select=a.select, replan=a.replan, fb_gain=a.fb, fb_scale=a.fb_scale, fb_near=a.fb_near, fb_fallback=a.fb_fallback, fb_ki=a.ki, fb_decay=a.kdecay, fb_imax=a.kimax,
|
|
fb_every=a.fb_every, safety_try=a.safety_try, sel_w=a.sel_w)
|
|
wall = time.perf_counter() - t0
|
|
print('ckpt=%s n=%d K=%d commit=%d fb=%.2f ki=%.2f select=%s replan=%s safety=%d' %
|
|
(a.ckpt, r['n'], a.K, a.commit, a.fb, a.ki, a.select, a.replan, a.safety))
|
|
print('success %.3f time_ok_1p3 %.3f coll %.3f ratio2s %.3f brakes %.1f dls_share %.3f err_fail %.2f wall %.1fs (%.0f ms/题)'
|
|
% (r['success'], r['time_ok_1p3'] or 0, r['collision_rate'], r['goal_ratio_2s'], r['brakes_mean'],
|
|
r['dls_share'], r['err_fail_med'], wall, wall / max(1, len(tasks)) * 1000))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|