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feat: 评估加分层读数——success_reachable(排除不可达D类)/by_cat 到达率/brake_rate_p90/stuck_share/n_plans_mean；CPU 冒烟全键产出、43 项测试绿

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cbb
2026-10-20 23:44:59 +08:00
parent 1a6840fc66
commit a982b0755d
1 changed files with 10 additions and 2 deletions
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ml/eval/eval_cl.py
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@@ -54,9 +54,17 @@ def run(model, kin, tasks, device, bs=256, **kw):
out.update({'reverse_p50': median(rr), 'reverse_mean': rr.mean().item(), out.update({'reverse_p50': median(rr), 'reverse_mean': rr.mean().item(),
'reverse_moving_p50': median(flat['reverse_rate_moving']), # 只在动了的步上算，不被刹停稀释 'reverse_moving_p50': median(flat['reverse_rate_moving']), # 只在动了的步上算，不被刹停稀释
'moving_share_p50': median(flat['moving_share'])}) # 活动窗内动了的步占比（=1−站住比例） 'moving_share_p50': median(flat['moving_share'])}) # 活动窗内动了的步占比（=1−站住比例）
if 'brake_rate' in flat: # §9.2 判据是"刹停 <2% 推理周期"，所以要按规划次数归一，不能只报绝对次数 if 'brake_rate' in flat: # §9.2 要"刹停 <2% 推理周期"。均值会被长尾拖着（实测 p50=0.5% 而 mean=10%），
out.update({'brake_rate_mean': flat['brake_rate'].mean().item(), 'brake_rate_p50': median(flat['brake_rate']), rr = flat['brake_rate'] # 所以必须分层报：p90 与"卡住题占比"才是能区分杠杆的读数。
out.update({'brake_rate_mean': rr.mean().item(), 'brake_rate_p50': median(rr), 'brake_rate_p90': rr.quantile(0.9).item(),
'stuck_share': (rr > 0.2).float().mean().item(),
'n_plans_mean': flat['n_plans'].mean().item()}) 'n_plans_mean': flat['n_plans'].mean().item()})
cats = [t.get('cat') for t in tasks] # 分层读数：D 类按定义不可达，混在分母里会让"可达任务到达率"失真
reach = torch.tensor([c != 'D' for c in cats])
if reach.any():
out['success_reachable'] = (ok[reach].float().mean().item())
out['by_cat'] = {c: [int(sum(1 for x in cats if x == c)), int(sum(1 for i, x in enumerate(cats) if x == c and ok[i]))]
for c in sorted({c for c in cats if c})}
if 'backoff' in flat and flat['backoff'].any(): # backoff=缩步顶上的次数；brakes 现在只记连最小档都不安全的 if 'backoff' in flat and flat['backoff'].any(): # backoff=缩步顶上的次数；brakes 现在只记连最小档都不安全的
out['backoff_mean'] = flat['backoff'].mean().item() # ⇒ 原来的"全拒"事件数 = 两者之和 out['backoff_mean'] = flat['backoff'].mean().item() # ⇒ 原来的"全拒"事件数 = 两者之和
out['reject_all_mean'] = (flat['backoff'] + flat['brakes']).mean().item() out['reject_all_mean'] = (flat['backoff'] + flat['brakes']).mean().item()