Hark
5591adb39a
- B-1 运行时投影护栏恢复 5 mm；老师轨迹按认证最小间隙 < 3 mm 剔除（stats/build_dataset，gapDrop） - B-2 APG 端点+中点 hinge 取均值；B-3 check_r2 空匹配保护、5000×50000、分块 256，pass=false 时 prep 停下 - B-4 R7 按全部 B/C 题统计；B-5 r8 起点贴柱；B-7 pipeline r4（export_onnx --random，训练前测延迟） - B-8 起步失败 ≤0.2%、column_gap 分块、pipefail、门禁结果先写 .tmp - 训练控制台：ml/console.bat|sh → ml/monitor.py（开始/停止/进度/日志接口）+ monitor.html； pipeline.sh all 支持 FROM/TO，阶段状态写 ml/runs/pipeline.json（ml/pstate.py）；训练/APG 日志加 total、t - 文档：README 一键运行与训练控制台；审查文档追加第三轮处置；方案余量决策与 R4 时点
69 lines
3.9 KiB
Python
69 lines
3.9 KiB
Python
"""门禁（方案 §7.2 第 6 步）：新模型须 ①门禁集到达率不降 ②A 类降幅 ≤ 0.5 个百分点 ③上一轮修好的错题 95% 仍对
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④R7：B/C 类（需绕行）左右比例变化 ≤ 5 个百分点 ⑤R7：两者都到达的题行程中位数比 ≤ 1.05。
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门禁集为无老师题，到达按定长 6 s 口径（见 closed_loop.NOREF_SEC）。
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python -m ml.mining.gate --old cur.pt --new cand.pt --tasks ml/runs/gate_tasks.jsonl --out gate.json [--fixed prev_fixed.jsonl]
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通过则把 cand 复制为 --current（默认 ml/runs/current.pt）。gate.json 含 accept / gain_pp，loop.sh 据此决定继续或停止。"""
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import argparse, json, shutil
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import torch
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from ml.common.kin_torch import Kin
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from ml.model.policy import build
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from ml.sim.closed_loop import evaluate
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def load(path, dev):
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ck = torch.load(path, map_location=dev)
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m = build(ck['cfg']).to(dev); m.load_state_dict(ck['model']); return m.eval()
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def per_task_success(pers):
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return torch.cat([p['done'] & (p['col'] == 0) for _, p in pers]).tolist()
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument('--old', required=True); ap.add_argument('--new', required=True); ap.add_argument('--tasks', required=True)
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ap.add_argument('--fixed', default=None); ap.add_argument('--out', required=True); ap.add_argument('--current', default='ml/runs/current.pt')
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ap.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu')
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a = ap.parse_args()
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kin = Kin(device=a.device)
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tasks = [json.loads(l) for l in open(a.tasks) if l.strip()]
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ro, po = evaluate(load(a.old, a.device), kin, tasks, a.device)
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rn, pn = evaluate(load(a.new, a.device), kin, tasks, a.device)
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so, sn = per_task_success(po), per_task_success(pn)
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acc = lambda s, c: (sum(x for x, t in zip(s, tasks) if t['cat'] == c) / max(1, sum(t['cat'] == c for t in tasks)))
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gain = (sum(sn) - sum(so)) / len(tasks) * 100
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a_drop = (acc(so, 'A') - acc(sn, 'A')) * 100
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keep = None
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if a.fixed:
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ft = [json.loads(l) for l in open(a.fixed) if l.strip()]
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if ft:
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r2, p2 = evaluate(load(a.new, a.device), kin, ft, a.device)
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keep = sum(per_task_success(p2)) / len(ft)
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# R7：左右比例与行程
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cat = [t['cat'] for t in tasks]
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so_side = torch.cat([p['side'] for _, p in po]); sn_side = torch.cat([p['side'] for _, p in pn])
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det = torch.tensor([c in ('B', 'C') for c in cat])
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# 2026-10-18：左右比例按全部 B/C 题的首次 J1 方向统计，不以到达为条件（APG 把一侧绕行全做失败时，到达子集比例可纹丝不动）
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left = lambda sd, ok: (((sd > 0) & det).sum() / det.sum().clamp(min=1)).item()
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ok_o, ok_n = torch.tensor(so, dtype=torch.bool), torch.tensor(sn, dtype=torch.bool)
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lr_shift = abs(left(sn_side, ok_n) - left(so_side, ok_o)) * 100
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both = ok_o & ok_n
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tro = torch.cat([p['travel'] for _, p in po]); trn = torch.cat([p['travel'] for _, p in pn])
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travel_ratio = (trn[both].median() / tro[both].median().clamp(min=1e-6)).item() if both.any() else None
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r7 = lr_shift <= 5.0 and (travel_ratio is None or travel_ratio <= 1.05)
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accept = gain >= 0 and a_drop <= 0.5 and (keep is None or keep >= 0.95) and r7
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out = {'old': ro, 'new': rn, 'gain_pp': gain, 'A_drop_pp': a_drop, 'fixed_kept': keep,
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'R7_left_share_shift_pp': lr_shift, 'R7_travel_median_ratio': travel_ratio, 'R7_ok': r7, 'accept': accept}
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# 本轮新修好的题（旧错新对）留给下一轮做"仍对"检查
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fixed_now = [t for t, x, y in zip(tasks, so, sn) if (not x) and y]
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with open(a.out.replace('.json', '_fixed.jsonl'), 'w') as f:
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for t in fixed_now: f.write(json.dumps(t) + '\n')
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json.dump(out, open(a.out, 'w'), indent=1)
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if accept: shutil.copy(a.new, a.current)
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print(json.dumps({k: out[k] for k in ('gain_pp', 'A_drop_pp', 'fixed_kept', 'R7_left_share_shift_pp', 'R7_travel_median_ratio', 'accept')}))
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if __name__ == '__main__':
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main()
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