Hark
2a0a2e55ad
- M1 数据生成器：采样/老师/距离下界认证/时间律/统计/一致性门禁；确定性老师（碰撞次数计时 + resetSession）、pos 任务冻结 J6、--consensus 邻域共识过滤、Dr 类。 - 全流程代码与运行手册：数据打包/可微运动学与对拍/MeanFlow+WTA 模型/闭环仿真/APG/挖错循环/ONNX 导出；webapp 策略运行时 ik-policy.js、?policy=1 接入、tests/policy_bench.cjs；ik-solver 增 pairList/clusterExport/criticalPairs/pairGap/leverTable。
203 lines
11 KiB
Python
203 lines
11 KiB
Python
"""闭环仿真（验证 / 挖错 / 门禁共用，PyTorch 批量，无梯度）——按部署节奏运行：
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50 Hz 执行；队列 ≤ 3 步时推理一次（已提交前缀 r = 队列长度），提交接下来 3 步；
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冷启动用价值头对候选末态打分（短前瞻），之后按与上一计划的一致性选候选；
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接近目标 < 5 mm 时用局部 DLS 精修；子目标头在新目标时给出途经点（到达 < 5° 或 1.5 s 超时后切下一个）。
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safety=False 时不做任何碰撞拦截（暴露真实错误，挖错用）；safety=True 时近似部署安全层（候选提交步有干涉则换下一条，全不过则停一步）。
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python -m ml.sim.closed_loop --ckpt ml/runs/base/best.pt --samples ml/data/samples --split val --n 1000 [--safety]"""
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import argparse, json, math, os
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import torch
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from ml.common.kin_torch import Kin
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from ml.common.features import features, STEP, H, GAP_CAP, D2R, wrap180
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DT = 0.02
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def tasks_to_tensors(tasks, device):
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q0 = torch.tensor([t['q0'] for t in tasks], dtype=torch.float32, device=device)
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T = torch.tensor([t['T'] for t in tasks], dtype=torch.float32, device=device)
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R = torch.tensor([t['R'] if t.get('R') else [0.] * 9 for t in tasks], dtype=torch.float32, device=device)
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ex = torch.tensor([t.get('exec_s', 3.0) or 3.0 for t in tasks], dtype=torch.float32, device=device)
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tr = torch.tensor([t.get('travel', 0.0) or 0.0 for t in tasks], dtype=torch.float32, device=device)
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return q0, T, R, ex, tr
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def goal_err(kin, q, T, R):
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xf = kin.cum_xf(q)
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p = kin.fk(q, xf); Rq = kin.orient(q, xf).reshape(-1, 9)
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e = (T - p).norm(dim=-1)
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pose = R.abs().sum(-1) > 0
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tr = (Rq * R).sum(-1)
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oe = torch.where(pose, torch.acos(((tr - 1) / 2).clamp(-1, 1)) / D2R, torch.zeros_like(e))
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return e, oe
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def dls_steps(kin, q, T, R, n=3, scale=0.3, lam=0.5):
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"""局部 DLS 精修：n 步增量 [B,n,6]"""
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step = torch.tensor(STEP, device=q.device)
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pose = (R.abs().sum(-1) > 0).float()
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Rt = R.view(-1, 3, 3)
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def err(x):
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xf = kin.cum_xf(x); p = kin.fk(x, xf); Rq = kin.orient(x, xf)
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M = Rt @ Rq.transpose(1, 2)
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rv = 0.5 * torch.stack([M[:, 2, 1] - M[:, 1, 2], M[:, 0, 2] - M[:, 2, 0], M[:, 1, 0] - M[:, 0, 1]], -1) * 100 * pose[:, None]
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return torch.cat([T - p, rv], -1)
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out = []; x = q.clone()
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for _ in range(n):
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e0 = err(x); J = []
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for j in range(6):
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dx = torch.zeros_like(x); dx[:, j] = 1e-3
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J.append(-(err(x + dx) - e0) / 1e-3)
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J = torch.stack(J, -1) # [B,6(err),6(q)]
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A = J @ J.transpose(1, 2) + lam ** 2 * torch.eye(6, device=q.device)
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y = torch.linalg.solve(A, e0.unsqueeze(-1))
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dq = (J.transpose(1, 2) @ y).squeeze(-1)
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sc = (dq.abs() / (step * scale)).max(-1, keepdim=True).values.clamp(min=1)
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dq = dq / sc
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out.append(dq); x = x + dq
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return torch.stack(out, 1)
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@torch.no_grad()
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def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None, return_traj=False):
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g = torch.Generator(device=device); g.manual_seed(seed)
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q0, T, R, ex, tr = tasks_to_tensors(tasks, device)
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B = q0.shape[0]
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step = torch.tensor(STEP, device=device)
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limit = torch.clamp(2 * ex, min=3.0) if max_sec is None else torch.full_like(ex, max_sec)
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nmax = int(math.ceil(limit.max().item() / DT))
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q, qp = q0.clone(), q0.clone()
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queue = torch.zeros(B, 0, 6, device=device)
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last = None
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subs = torch.zeros(B, 2, 6, device=device); nsub = torch.zeros(B, dtype=torch.long, device=device); sub_t = torch.zeros(B, device=device)
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done = torch.zeros(B, dtype=torch.bool, device=device); t_done = torch.full((B,), float('nan'), device=device)
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col = torch.zeros(B, device=device); travel = torch.zeros(B, device=device); brakes = torch.zeros(B, device=device)
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traj = [q.clone()] if return_traj else None
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cold = True
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for it in range(nmax):
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tnow = it * DT
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if queue.shape[1] <= 3:
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r = queue.shape[1]
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pre = torch.zeros(B, 3, 6, device=device); pre[:, :r] = queue[:, :r]
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qe = q + queue.sum(1)
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e, oe = goal_err(kin, qe, T, R)
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near = (e < 5) & ((R.abs().sum(-1) == 0) | (oe < 5))
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# 子目标推进
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if (nsub > 0).any():
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cur = subs[:, 0]
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dd = torch.where(kin.cont, wrap180(cur - qe), cur - qe).abs().max(-1).values
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adv = (nsub > 0) & ((dd < 5) | (tnow - sub_t > 1.5))
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subs = torch.where(adv[:, None, None], torch.cat([subs[:, 1:], subs[:, 1:]], 1), subs)
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nsub = torch.where(adv, nsub - 1, nsub); sub_t = torch.where(adv, torch.full_like(sub_t, tnow), sub_t)
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sf = (nsub > 0).float()
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feat = features(kin, q, qp, T, R, pre, torch.full((B,), r, device=device), subs[:, 0], sf)
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z = torch.randn(B * K, H, 6, device=device, generator=g)
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act, val, sub = model.sample_k(feat, K, z)
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inc = act.clamp(-1, 1) * step
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if cold:
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sub0 = sub
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need = sub0[:, 12] > 0.5
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subs = q[:, None] + sub0[:, :12].view(B, 2, 6) * 180
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nsub = torch.where(need, torch.full_like(nsub, 2), torch.zeros_like(nsub)); sub_t.fill_(tnow)
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fut = qe[:, None, None] + torch.cumsum(inc[:, :, r:], 2) # [B,K,H-r,6]
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if last is None:
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# 短前瞻：候选末态的价值
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qa = fut[:, :, -1].reshape(B * K, 6); qb = fut[:, :, -2].reshape(B * K, 6)
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f2 = features(kin, qa, qb, T.repeat_interleave(K, 0), R.repeat_interleave(K, 0), torch.zeros(B * K, 3, 6, device=device),
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torch.zeros(B * K, device=device), qa, torch.zeros(B * K, device=device))
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v2, _ = model.heads(model.encode(f2))
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score = -v2.view(B, K)
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else:
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n = min(fut.shape[2], last.shape[1] - 3)
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w = 0.85 ** torch.arange(n, device=device, dtype=torch.float32)
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d2 = (((fut[:, :, :n] - last[:, None, 3:3 + n]) / step) ** 2).sum(-1)
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score = -(d2 * w).sum(-1)
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order = score.argsort(-1, descending=True)
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pick = order[:, 0]
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commit = inc[torch.arange(B), pick, r:r + 3]
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if safety:
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ok_any = torch.zeros(B, dtype=torch.bool, device=device)
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chosen = torch.zeros(B, 3, 6, device=device)
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for kk in range(K):
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ck = inc[torch.arange(B), order[:, kk], r:r + 3]
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pts = qe[:, None] + torch.cumsum(ck, 1)
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mids = torch.cat([qe[:, None], pts[:, :-1]], 1) + 0.5 * ck
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allq = torch.cat([pts, mids], 1).reshape(-1, 6)
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okk = (kin.gaps(allq, 0.5)['gm'].min(-1).values > 0).view(B, 6).all(-1)
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take = okk & ~ok_any
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chosen = torch.where(take[:, None, None], ck, chosen); ok_any |= okk
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commit = torch.where(ok_any[:, None, None], chosen, torch.zeros_like(chosen))
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brakes += (~ok_any).float()
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if near.any():
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ref = dls_steps(kin, qe, T, R)
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commit = torch.where(near[:, None, None], ref, commit)
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last = torch.cat([qe[:, None], fut[torch.arange(B), pick]], 1)
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queue = torch.cat([queue, commit], 1)
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cold = False
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d = queue[:, 0]; queue = queue[:, 1:]
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d = torch.where(done[:, None], torch.zeros_like(d), d)
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qp = q; q = kin.clamp_box(q + d)
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travel += d.abs().sum(-1)
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if return_traj: traj.append(q.clone())
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mid = qp + 0.5 * (q - qp)
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gm = kin.gaps(torch.cat([q, mid], 0), 0.5)['gm'].min(-1).values
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hit = ((gm[:B] <= 0) | (gm[B:] <= 0)) & ~done
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col += hit.float()
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e, oe = goal_err(kin, q, T, R)
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newly = (~done) & (e < 1) & (oe < 0.5)
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t_done = torch.where(newly, torch.full_like(t_done, (it + 1) * DT), t_done); done |= newly
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if done.all(): break
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ok = done & (t_done <= limit)
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res = {'n': B, 'success': ok.float().mean().item(),
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'time_ok_1p3': (ok & (t_done <= 1.3 * ex)).float().mean().item(),
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'collision_rate': (col > 0).float().mean().item(), 'collision_steps_mean': col.mean().item(),
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'travel_ratio_median': (travel[ok] / tr[ok].clamp(min=1)).median().item() if ok.any() and (tr > 0).any() else None,
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'time_mean_s': t_done[ok].mean().item() if ok.any() else None, 'brakes_mean': brakes.mean().item()}
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per = {'done': done.cpu(), 't_done': t_done.cpu(), 'col': col.cpu(), 'travel': travel.cpu()}
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if return_traj: per['traj'] = torch.stack(traj, 1).cpu()
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return res, per
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def evaluate(model, kin, tasks, device, K=8, safety=False, bs=256, seed=0):
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agg, pers = {}, []
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for i in range(0, len(tasks), bs):
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r, p = rollout(model, kin, tasks[i:i + bs], device, K=K, safety=safety, seed=seed + i)
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w = r['n']; pers.append((r, p))
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for k, v in r.items():
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if k == 'n' or v is None: continue
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agg[k] = agg.get(k, 0.0) + v * w
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n = sum(r['n'] for r, _ in pers)
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out = {k: v / n for k, v in agg.items()}; out['n'] = n
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return out, pers
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def main():
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from ml.model.policy import build
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from ml.data import load_tasks
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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('--tasks', default=None, help='任务 jsonl（覆盖 --samples/--split）')
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ap.add_argument('--split', default='val'); ap.add_argument('--n', type=int, default=1000)
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ap.add_argument('--safety', action='store_true'); ap.add_argument('--K', type=int, default=8)
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ap.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu'); ap.add_argument('--out', default=None)
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a = ap.parse_args()
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ck = torch.load(a.ckpt, map_location=a.device)
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model = build(ck['cfg']).to(a.device); model.load_state_dict(ck['model']); model.eval()
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kin = Kin(device=a.device)
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assert ck.get('fp') in (None, kin.fp), '碰撞模型指纹与 checkpoint 不符'
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if a.tasks: tasks = [json.loads(l) for l in open(a.tasks, encoding='utf-8') if l.strip()]
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else: tasks = [t for t in load_tasks([a.samples]) if t['split'] == a.split]
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tasks = tasks[:a.n]
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res, pers = evaluate(model, kin, tasks, a.device, K=a.K, safety=a.safety)
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by = {}
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flat_done = torch.cat([p['done'] for _, p in pers]); flat_col = torch.cat([p['col'] for _, p in pers])
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for t, d, c in zip(tasks, flat_done.tolist(), flat_col.tolist()):
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k = t.get('cat', '?') + '/' + t.get('mode', '?'); b = by.setdefault(k, [0, 0, 0]); b[0] += 1; b[1] += int(d); b[2] += int(c > 0)
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res['by_cat_mode'] = {k: {'n': v[0], 'success': v[1] / v[0], 'collision': v[2] / v[0]} for k, v in by.items()}
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s = json.dumps(res, indent=1); print(s)
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if a.out: open(a.out, 'w').write(s)
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if __name__ == '__main__':
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main()
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