317 lines
20 KiB
Python
317 lines
20 KiB
Python
#!/usr/bin/env python
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"""闭环失败定位（只读，手动运行，n=256 约 35–50 分钟，显存约 3–6 GB）。
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PYTHONUTF8=1 python -m ml.eval.diag_cl --ckpt ml/runs/base/last.pt --n 256 --out ml/runs/diag_cl.json
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任务 = val 集前 n 题；老师轨迹按任务 id 从 ml/data/shard_*.jsonl 取（取不到的题不进 H 段，数量记在 H_tasks_missing_teacher）。
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模型经 ckpt.load_policy 载入（有 EMA 用 EMA；act_scale 随 checkpoint，旧 checkpoint = 1.0）。
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段落：
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A. 特征口径：val 任务起点（非增广、老师状态）上，Python features() 与数据集 X 的逐组最大差 / 平均差。
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B. 同一噪声下两份特征得到的动作差（16 步末端关节差，度）。
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C. 旧部署口径（1 段采样、一致性选择、子目标旧规则 timeout）的失败形态：首撞时刻、碰撞步数、首撞组对、最小目标误差、
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目标误差进度比、开安全层的到达 / 碰撞 / 刹停。到达 = 时限内到达，碰撞只计时限内（2026-10-19 起）。
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H（2026-10-19，三份审查 review_closedloop / review_model_train / review_data_actions 的决定性实验）：
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H1 老师 oracle 走完整 rollout()（closed_loop.TeacherOracle；select='first'、use_sub=False）：
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H1_oracle：额外含 oracle 字段——DLS 接管前与老师轨迹的最大关节偏差、到达 / 碰撞、完成时刻 − 老师时长；
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H1_oracle_noise_<σ>（σ = 0.05 / 0.1 / 0.2，STEP 为单位，每次规划重采）：闭环对残差噪声的敏感度。
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H2 前缀消融：H2_open_loop（r=3 的 val 行，前缀 47:68 原样 vs 置零，执行窗口 3..5 的误差）；
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H2_replan_empty_s{1,4}：队列空了才规划（每次 r=0、提交 3 步），其余同部署。
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H3 子目标：H3_headsub_cross_s{1,4}（子目标头 + 越过顶点规则 = 新部署默认）、H3_headsub_timeout_s{1,4}（旧规则 = 旧部署）、
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H3_teachersub_cross_s{1,4} / H3_teachersub_timeout_s{1,4}（老师真实途经点全表）、H3_nosub_s{1,4}（不用子目标）。
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H4 指标（上面每个闭环变体都输出同一组键，见 metrics()）；H4_open_loop：按前缀长度 r=0..3 的执行窗口误差。
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_s1 / _s4 = MeanFlow 1 段（部署）/ 4 段采样。"""
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import argparse, json, math, os
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import torch
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from ml.common.ckpt import load_policy
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from ml.common.kin_torch import Kin
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from ml.common.features import features, STEP, H
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from ml.data import Samples, load_tasks, read_meta
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from ml.sim.closed_loop import rollout, goal_err, DT, TeacherOracle
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from ml.eval.replay_teacher import load_teacher, pad_traj, oracle_raw, oracle_summary
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# 83 维特征分组（与 features.py 拼接顺序一致）
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GROUPS = [('sincos', 0, 12), ('joint_norm', 12, 15), ('dq', 15, 21), ('p', 21, 24), ('Rq', 24, 30), ('T', 30, 33),
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('R', 33, 39), ('T-p', 39, 42), ('T-p_clamp', 42, 45), ('mode', 45, 47), ('prefix', 47, 65), ('mask', 65, 68),
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('sub', 68, 74), ('sub_flag', 74, 75), ('gaps', 75, 83)]
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PROG_T = (0.5, 1.0, 2.0) # 目标误差 / 起点误差 的取样时刻（s）
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K1, K2 = int(round(1.0 / DT)), int(round(2.0 / DT)) # 1 s / 2 s 对应的步数
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def pct(v, p):
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v = sorted(x for x in v if x is not None and x == x)
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return v[min(len(v) - 1, int(len(v) * p))] if v else None
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def qt(t, p):
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t = t[torch.isfinite(t)]
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return round(float(t.kthvalue(max(1, min(len(t), int(math.ceil(p * len(t)))))).values), 4) if len(t) else None
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def rnd(x, n=4):
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if isinstance(x, list): return [rnd(v, n) for v in x]
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return None if x is None or x != x else round(float(x), n)
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def pad_len(tr, n):
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"""轨迹 [B,T,6] 末帧重复补到至少 n 帧（到达后冻结，补帧与冻结等价）。"""
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if tr.shape[1] >= n: return tr
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return torch.cat([tr, tr[:, -1:].expand(-1, n - tr.shape[1], -1)], 1)
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def batch_stats(kin, ch, per, TR):
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"""一批的逐题量（CPU）。窗口：幅度 / 净位移 / TCP 用前 1 s，变号率用前 2 s 的 3 步块。"""
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dev = TR.device
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stp = torch.tensor(STEP, device=dev)
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tr = pad_len(per['traj'].to(dev), K2 + 1); te = pad_len(TR, K2 + 1)
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B = tr.shape[0]
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amp = lambda x: (x[:, 1:K1 + 1] - x[:, :K1]).abs().sum(1) # [B,6] 前 1 s 关节行程
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net = lambda x: (x[:, K1] - x[:, 0]).abs() # [B,6] 前 1 s 净位移
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def tcp(x):
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p = kin.fk(x[:, :K1 + 1].reshape(-1, 6)).view(B, K1 + 1, 3)
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return (p[:, 1:] - p[:, :-1]).norm(dim=-1).sum(1), (p[:, -1] - p[:, 0]).norm(dim=-1)
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def flips(x):
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idx = torch.arange(0, K2 + 1, 3, device=dev)
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b = x[:, idx[1:]] - x[:, idx[:-1]] # [B,nb,6] 3 步块增量
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big = b.abs() > 0.1 * stp
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v = big[:, 1:] & big[:, :-1]
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f = v & (torch.sign(b[:, 1:]) != torch.sign(b[:, :-1]))
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return f.sum(1).float(), v.sum(1).float()
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tl_p, tn_p = tcp(tr); tl_t, tn_t = tcp(te)
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fp_, vp_ = flips(tr); ft_, vt_ = flips(te)
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ex = torch.tensor([t.get('exec_s') or float('nan') for t in ch])
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return {'ok': per['ok'], 'col': per['col'] > 0, 'isD': torch.tensor([t.get('cat') == 'D' for t in ch]),
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'pose': torch.tensor([bool(t.get('R')) for t in ch]), 'key': [t.get('cat', '?') + '/' + t.get('mode', '?') for t in ch],
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'r2': per['goal_ratio_2s'], 'err': per['err'], 'first_hit': per['first_hit'], 'brakes': per['brakes'],
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'tratio': per['t_done'] / ex,
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'amp_p': amp(tr).cpu(), 'amp_t': amp(te).cpu(), 'net_p': net(tr).cpu(), 'net_t': net(te).cpu(),
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'tl_p': tl_p.cpu(), 'tl_t': tl_t.cpu(), 'tn_p': tn_p.cpu(), 'tn_t': tn_t.cpu(),
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'fl_p': fp_.cpu(), 'fv_p': vp_.cpu(), 'fl_t': ft_.cpu(), 'fv_t': vt_.cpu()}
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def metrics(stats):
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"""H4 指标（每个闭环变体同一组键）：
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reach / collision：cat≠D、时限内（D 类目标不可达，单列在 D）；
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goal_ratio_2s_p50：2 s 时目标误差 / 起点误差；end_goal_err_mm_p50；first_hit_s_p50（撞了的题）；t_done_over_teacher_p50（到达的题）；
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joint_amp_ratio[6]：前 1 s 各轴关节行程 Σ|Δq| 策略 / 老师（全体题合计；J6 只算 pose 题，pos 题老师冻结 J6）；
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joint_net_ratio[6]：前 1 s 各轴净位移 |q(1s) − q(0)| 策略 / 老师（同上）；j6_pos_amp_deg_p50：pos 题策略 J6 前 1 s 行程（度，老师为 0）；
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tcp_path_ratio_p50 / tcp_net_ratio_p50：前 1 s TCP 路程 / 净位移 策略 / 老师（逐题比，老师 > 1 mm 的题）；
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sign_flip_rate[6] / sign_flip_rate_teacher[6]：前 2 s 相邻 3 步块增量变号的比例（两块都 > 0.1·STEP 才计）；
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by_cat_mode：各 cat/mode 的 n、reach、collision、goal_ratio_2s_p50（不含 D）；D：n、collision、end_goal_err_mm_p50、goal_ratio_2s_p50。"""
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c = {k: (sum((s[k] for s in stats), []) if isinstance(stats[0][k], list) else torch.cat([s[k] for s in stats])) for k in stats[0]}
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keys = c['key']
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m = ~c['isD']; pose = c['pose']
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def jr(a, b):
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out = []
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for j in range(6):
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sel = m & pose if j == 5 else m
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den = float(b[sel, j].sum()); out.append(float(a[sel, j].sum()) / den if den > 1e-6 else None)
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return rnd(out)
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tl_ok = m & (c['tl_t'] > 1); tn_ok = m & (c['tn_t'] > 1)
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out = {'n': int(m.sum()),
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'reach': rnd(c['ok'][m].float().mean()) if m.any() else None,
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'collision': rnd(c['col'][m].float().mean()) if m.any() else None,
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'goal_ratio_2s_p50': qt(c['r2'][m], .5), 'end_goal_err_mm_p50': qt(c['err'][m], .5),
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'first_hit_s_p50': qt(c['first_hit'][m], .5), 't_done_over_teacher_p50': qt(c['tratio'][m & c['ok']], .5),
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'brakes_p50': qt(c['brakes'][m], .5),
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'joint_amp_ratio': jr(c['amp_p'], c['amp_t']), 'joint_net_ratio': jr(c['net_p'], c['net_t']),
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'j6_pos_amp_deg_p50': qt(c['amp_p'][m & ~pose, 5], .5),
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'tcp_path_ratio_p50': qt(c['tl_p'][tl_ok] / c['tl_t'][tl_ok], .5), 'tcp_net_ratio_p50': qt(c['tn_p'][tn_ok] / c['tn_t'][tn_ok], .5),
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'sign_flip_rate': rnd((c['fl_p'][m].sum(0) / c['fv_p'][m].sum(0).clamp(min=1)).tolist()),
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'sign_flip_rate_teacher': rnd((c['fl_t'][m].sum(0) / c['fv_t'][m].sum(0).clamp(min=1)).tolist())}
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by = {}
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for i, k in enumerate(keys):
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if not bool(m[i]): continue
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by.setdefault(k, []).append(i)
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out['by_cat_mode'] = {k: {'n': len(ix), 'reach': rnd(c['ok'][ix].float().mean()), 'collision': rnd(c['col'][ix].float().mean()),
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'goal_ratio_2s_p50': qt(c['r2'][ix], .5)} for k, ix in sorted(by.items())}
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d = c['isD']
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out['D'] = {'n': int(d.sum()), 'collision': rnd(c['col'][d].float().mean()) if d.any() else None,
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'end_goal_err_mm_p50': qt(c['err'][d], .5), 'goal_ratio_2s_p50': qt(c['r2'][d], .5)}
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return out
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def teacher_subs(recs, device):
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"""老师真实途经点全表（path 去掉起终点）→ (subs [B,M,6], n [B])，供 rollout(sub_override=...)。"""
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vs = [r['path'][1:-1] for r in recs]
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M = max(1, max(len(v) for v in vs))
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subs = torch.zeros(len(vs), M, 6, device=device); n = torch.zeros(len(vs), dtype=torch.long, device=device)
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for i, v in enumerate(vs):
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if v:
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x = torch.tensor(v, dtype=torch.float32, device=device); subs[i, :len(v)] = x; subs[i, len(v):] = x[-1]; n[i] = len(v)
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return subs, n
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument('--ckpt', required=True); ap.add_argument('--samples', default='ml/data/samples')
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ap.add_argument('--shards', default='ml/data/shard_*.jsonl')
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ap.add_argument('--n', type=int, default=256); ap.add_argument('--bs', type=int, default=64)
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ap.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu'); ap.add_argument('--out', default='ml/runs/diag_cl.json')
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a = ap.parse_args()
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dev = a.device
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model, ck = load_policy(a.ckpt, dev)
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kin = Kin(device=dev)
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astep = torch.tensor(STEP, device=dev) * model.act_scale # 动作 → 度
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data_scale = float(read_meta(a.samples).get('act_scale', 1.0))
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out = {'ckpt': a.ckpt, 'step': ck.get('step'), 'weights': 'ema' if ck.get('ema') is not None else 'model',
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'act_scale_ckpt': model.act_scale, 'act_scale_data': data_scale}
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tasks = [t for t in load_tasks([a.samples]) if t['split'] == 'val'][:a.n]
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by_tid = {t['tid']: t for t in tasks}
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def save():
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s = json.dumps(out, indent=1, ensure_ascii=False)
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os.makedirs(os.path.dirname(a.out) or '.', exist_ok=True)
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open(a.out + '.tmp', 'w', encoding='utf-8').write(s); os.replace(a.out + '.tmp', a.out)
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return s
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# ---------- A / B：起点特征口径 ----------
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vd = Samples(a.samples, device=dev, split='val')
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tid = vd.TID.long()
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first = torch.ones_like(tid, dtype=torch.bool); first[1:] = tid[1:] != tid[:-1] # 每题第一行 = t=0 样本
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idx = [i for i in first.nonzero().flatten().tolist() if int(tid[i]) in by_tid]
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X0 = vd.X[idx].float()
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tk = [by_tid[int(tid[i])] for i in idx]
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q0 = torch.tensor([t['q0'] for t in tk], dtype=torch.float32, device=dev)
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qx = torch.atan2(X0[:, 0:12:2], X0[:, 1:12:2]) * 180 / torch.pi
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dqx = torch.remainder(qx - q0 + 180, 360) - 180
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clean = dqx.abs().max(-1).values < 1e-2 # 未被噪声增广的起点
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out['A_rows'] = int(clean.sum()); out['A_rows_aug_skipped'] = int((~clean).sum())
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if clean.any():
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Xc = X0[clean]; qc = q0[clean]; tc = [t for t, c in zip(tk, clean.tolist()) if c]
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T = torch.tensor([t['T'] for t in tc], dtype=torch.float32, device=dev)
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R = torch.tensor([t['R'] if t.get('R') else [0.] * 9 for t in tc], dtype=torch.float32, device=dev)
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B = qc.shape[0]
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qpc = torch.tensor([t.get('qp0') or t['q0'] for t in tc], dtype=torch.float32, device=dev)
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pre = Xc[:, 47:65].view(B, 3, 6) * torch.tensor(STEP, device=dev) # 前缀 / 子目标直接取数据集里的，只比状态相关部分
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pl = Xc[:, 65:68].sum(-1).round()
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sf = Xc[:, 74]
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sub = qc + Xc[:, 68:74] * 180
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Fp = features(kin, qc, qpc, T, R, pre, pl, sub, sf)
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out['A_maxdiff'] = {g: (Fp[:, s:e] - Xc[:, s:e]).abs().max().item() for g, s, e in GROUPS}
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out['A_meandiff'] = {g: (Fp[:, s:e] - Xc[:, s:e]).abs().mean().item() for g, s, e in GROUPS}
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with torch.no_grad():
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z = torch.randn(B * 8, H, 6, device=dev, generator=torch.Generator(device=dev).manual_seed(7))
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a1, _, _ = model.sample_k(Xc, 8, z); a2, _, _ = model.sample_k(Fp, 8, z)
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d = ((a1.clamp(-1, 1) - a2.clamp(-1, 1)) * astep).sum(2).abs().max(-1).values # [B,K] 16 步末端关节差（度）
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mag = (a1.clamp(-1, 1) * astep).sum(2).abs().max(-1).values
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out['B_act_diff_deg'] = {'mean': d.mean().item(), 'p90': d.flatten().kthvalue(max(1, int(0.9 * d.numel()))).values.item(),
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'ref_motion_deg_mean': mag.mean().item()}
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# ---------- H4 / H2 开环：执行窗口误差（val 行，老师状态） ----------
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# 闭环稳态 r=3，真正执行的是 act[r:r+3]；原来的 ol_fk_mm 是 16 步终点误差，前缀那几步几乎不会错，看不出执行窗口。
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with torch.no_grad():
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pl_all = vd.X[:, 65:68].sum(-1).round().long()
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gsel = torch.Generator(device=dev); gsel.manual_seed(321)
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def rows_with(r, n=1024):
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ix = (pl_all == r).nonzero().flatten()
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return ix[torch.randperm(len(ix), device=dev, generator=gsel)[:n]] if len(ix) else ix
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def win_err(X, Y, r, steps, k0=None):
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k0 = r if k0 is None else k0
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z = torch.randn(X.shape[0] * 8, H, 6, device=dev, generator=torch.Generator(device=dev).manual_seed(11))
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act, _, _ = model.sample_k(X, 8, z, steps=steps); act = act.float().clamp(-1, 1)
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e = ((act[:, :, k0:k0 + 3] - Y[:, None, k0:k0 + 3]) * astep).sum(2).abs() # [N,K,6] 窗口累计误差（度）
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emax = e.max(-1).values.flatten()
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res = {'n_rows': int(X.shape[0]), 'deg_mean': rnd(emax.mean()), 'deg_p90': qt(emax, .9), 'per_joint_deg_mean': rnd(e.mean((0, 1)).tolist())}
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if r > 0 and k0 == r:
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ce = ((act[:, :, :r] - Y[:, None, :r]) * astep).abs().max(-1).values.max(-1).values.flatten()
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res['prefix_copy_err_deg_mean'] = rnd(ce.mean())
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return res
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ol4, ol2 = {}, {}
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for steps in (1, 4):
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for r in range(4):
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ix = rows_with(r)
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if len(ix): ol4['s%d_r%d' % (steps, r)] = win_err(vd.X[ix].float(), vd.Y[ix].float(), r, steps)
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ix = rows_with(3)
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if len(ix):
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X3 = vd.X[ix].float(); Y3 = vd.Y[ix].float(); X3z = X3.clone(); X3z[:, 47:68] = 0
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ol2['s%d_prefix_orig' % steps] = win_err(X3, Y3, 3, steps)
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ol2['s%d_prefix_zeroed' % steps] = win_err(X3z, Y3, 0, steps, k0=3)
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out['H4_open_loop'] = ol4; out['H2_open_loop'] = ol2
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del vd
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if dev.startswith('cuda'): torch.cuda.empty_cache()
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# ---------- C：旧部署口径的闭环失败形态（子目标旧规则，便于与前几轮对比） ----------
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first_t, col_steps, pairs, min_err, reach = [], [], {}, [], 0
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prog = {ts: [] for ts in PROG_T}
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reach_s, brakes_s, col_s = 0, [], 0
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for i in range(0, len(tasks), a.bs):
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ch = tasks[i:i + a.bs]
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with torch.no_grad():
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_, per = rollout(model, kin, ch, dev, K=8, safety=False, seed=i, return_traj=True, sub_rule='timeout')
|
||
tr = per['traj'].to(dev) # [B,Tn,6]
|
||
B, Tn, _ = tr.shape
|
||
flat = tr.reshape(-1, 6) # 分块 ≤1024 点（同 dagger/collect.py 的显存修复）
|
||
gm = torch.cat([kin.gaps(flat[s:s + 1024], 0.5)['gm'] for s in range(0, flat.shape[0], 1024)]).view(B, Tn, 49)
|
||
inlim = (torch.arange(Tn, device=dev)[None] * DT) <= per['limit'].to(dev)[:, None] + 1e-6
|
||
hit = (gm.min(-1).values <= 0) & inlim # [B,Tn] 只计时限内
|
||
Tt = torch.tensor([t['T'] for t in ch], dtype=torch.float32, device=dev)
|
||
Rt = torch.tensor([t['R'] if t.get('R') else [0.] * 9 for t in ch], dtype=torch.float32, device=dev)
|
||
e, _ = goal_err(kin, tr.reshape(-1, 6), Tt.repeat_interleave(Tn, 0), Rt.repeat_interleave(Tn, 0))
|
||
e = e.view(B, Tn)
|
||
for b in range(B):
|
||
for ts in PROG_T:
|
||
k = min(Tn - 1, int(round(ts / DT))); prog[ts].append((e[b, k] / e[b, 0].clamp(min=1e-3)).item())
|
||
for b in range(B):
|
||
if per['ok'][b]: reach += 1
|
||
min_err.append(e[b].min().item()); col_steps.append(int(per['col'][b]))
|
||
h = hit[b].nonzero().flatten()
|
||
if len(h):
|
||
k = int(h[0]); first_t.append(k * DT)
|
||
pr = int(gm[b, k].argmin()); key = '%d-%d' % (pr // 7, pr % 7); pairs[key] = pairs.get(key, 0) + 1
|
||
rs, ps = rollout(model, kin, ch, dev, K=8, safety=True, seed=i, sub_rule='timeout')
|
||
reach_s += int(ps['ok'].sum()); col_s += int((ps['col'] > 0).sum()); brakes_s += ps['brakes'].tolist()
|
||
n = len(tasks)
|
||
out['C'] = {'n': n, 'reach': reach / n, 'collided': len(first_t) / n,
|
||
'first_hit_s': {'p10': pct(first_t, .1), 'p50': pct(first_t, .5), 'p90': pct(first_t, .9),
|
||
'within_0.1s': sum(t <= 0.1 for t in first_t) / max(1, len(first_t))},
|
||
'col_steps': {'p50': pct(col_steps, .5), 'p90': pct(col_steps, .9)},
|
||
'first_hit_pair(group a-b)': dict(sorted(pairs.items(), key=lambda kv: -kv[1])[:8]),
|
||
'min_goal_err_mm': {'p10': pct(min_err, .1), 'p50': pct(min_err, .5), 'p90': pct(min_err, .9)},
|
||
'safety_on': {'reach': reach_s / n, 'collided': col_s / n, 'brakes_p50': pct(brakes_s, .5)}}
|
||
out['C']['goal_err_ratio_p50'] = {'%.1fs' % ts: pct(v, .5) for ts, v in prog.items()} # <1 = 在靠近目标
|
||
print(save(), flush=True)
|
||
|
||
# ---------- H：闭环变体（同一批 val 题，老师轨迹按 id 取） ----------
|
||
teach = load_teacher(a.shards, [t['id'] for t in tasks])
|
||
tH = [t for t in tasks if t['id'] in teach]
|
||
out['H_tasks'] = len(tH); out['H_tasks_missing_teacher'] = len(tasks) - len(tH)
|
||
|
||
def variant(oracle_sigma=None, teacher_sub=False, **kw):
|
||
stats, raws = [], []
|
||
for i in range(0, len(tH), a.bs):
|
||
ch = tH[i:i + a.bs]; recs = [teach[t['id']] for t in ch]
|
||
TR = pad_traj([r['traj'] for r in recs], dev)
|
||
kw2 = dict(kw)
|
||
if oracle_sigma is not None:
|
||
kw2.update(oracle=TeacherOracle(TR, act_scale=data_scale, sigma=oracle_sigma), select='first', use_sub=False)
|
||
if teacher_sub: kw2['sub_override'] = teacher_subs(recs, dev)
|
||
with torch.no_grad():
|
||
_, per = rollout(None if oracle_sigma is not None else model, kin, ch, dev, K=8, seed=i, return_traj=True, **kw2)
|
||
stats.append(batch_stats(kin, ch, per, TR))
|
||
if oracle_sigma is not None: raws.append(oracle_raw(per, TR, [len(r['traj']) for r in recs], ch))
|
||
del per
|
||
res = metrics(stats)
|
||
if oracle_sigma is not None and oracle_sigma == 0: res['oracle'] = oracle_summary(raws)
|
||
if dev.startswith('cuda'): torch.cuda.empty_cache()
|
||
return res
|
||
|
||
plan = [('H1_oracle', dict(oracle_sigma=0.0))]
|
||
plan += [('H1_oracle_noise_%g' % s, dict(oracle_sigma=s)) for s in (0.05, 0.1, 0.2)]
|
||
for st in (1, 4):
|
||
plan += [('H3_headsub_cross_s%d' % st, dict(steps=st, sub_rule='cross')),
|
||
('H3_headsub_timeout_s%d' % st, dict(steps=st, sub_rule='timeout')),
|
||
('H3_teachersub_cross_s%d' % st, dict(steps=st, sub_rule='cross', teacher_sub=True)),
|
||
('H3_teachersub_timeout_s%d' % st, dict(steps=st, sub_rule='timeout', teacher_sub=True)),
|
||
('H3_nosub_s%d' % st, dict(steps=st, use_sub=False)),
|
||
('H2_replan_empty_s%d' % st, dict(steps=st, sub_rule='cross', replan='empty'))]
|
||
for name, kw in plan:
|
||
out[name] = variant(**kw)
|
||
save(); print('[diag] %s 完成' % name, flush=True)
|
||
print(save(), flush=True)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|