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。
59 lines
3.2 KiB
Python
59 lines
3.2 KiB
Python
"""对拍：python -m ml.eval.check_parity [--kin ml/assets/kin.json] [--data ml/data/parity.json]
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检查 PyTorch 版 fk / orient / 组对间隙 / 83 维特征 与 JS 一致，以及间隙梯度与有限差分一致。"""
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import argparse, json
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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, GAP_CAP
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument('--kin', default=None); ap.add_argument('--data', default='ml/data/parity.json')
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a = ap.parse_args()
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kin = Kin(a.kin) if a.kin else Kin(dtype=torch.float64)
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D = json.load(open(a.data, encoding='utf-8'))
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assert D['fp'] == kin.fp, f"指纹不符：parity {D['fp']} vs kin {kin.fp}（重跑 export_kin.cjs）"
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R = D['recs']; f64 = torch.float64
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q = torch.tensor([r['q'] for r in R], dtype=f64)
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qp = torch.tensor([r['qPrev'] for r in R], dtype=f64)
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T = torch.tensor([r['T'] for r in R], dtype=f64)
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Rt = torch.tensor([r['R'] if r['R'] else [0.] * 9 for r in R], dtype=f64)
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pre = torch.zeros(len(R), 3, 6, dtype=f64); pl = torch.zeros(len(R), dtype=torch.long)
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for n, r in enumerate(R):
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for k, d in enumerate(r['prefix']): pre[n, k] = torch.tensor(d, dtype=f64)
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pl[n] = len(r['prefix'])
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sub = torch.tensor([r['sub'] if r['sub'] else [0.] * 6 for r in R], dtype=f64)
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sf = torch.tensor([1. if r['sub'] else 0. for r in R], dtype=f64)
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kin = Kin(a.kin, dtype=f64) if a.kin else kin
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xf = kin.cum_xf(q)
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e_fk = (kin.fk(q, xf) - torch.tensor([r['fk'] for r in R], dtype=f64)).abs().max().item()
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e_or = (kin.orient(q, xf).reshape(-1, 9) - torch.tensor([r['orient'] for r in R], dtype=f64)).abs().max().item()
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G = kin.gaps(q, GAP_CAP, xf)
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gm_js = torch.tensor([r['gm'] for r in R], dtype=f64)
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e_gm = (G['gm'] - gm_js.clamp(max=GAP_CAP)).abs().max().item()
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col_js = gm_js.min(-1).values <= 0; col_py = G['gm'].min(-1).values <= 0
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f = features(kin, q, qp, T, Rt, pre, pl, sub, sf, gm=G['gm'], xf=xf)
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e_f = (f - torch.tensor([r['feat'] for r in R], dtype=f64)).abs().max().item()
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# 梯度：最小间隙对 q 的梯度 vs 有限差分（取 50 个无干涉且间隙 < 20 的样本）
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sel = ((G['gm'].min(-1).values > 0.5) & (G['gm'].min(-1).values < 20)).nonzero().flatten()[:50]
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rel = []
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if len(sel):
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qs = q[sel].clone().requires_grad_(True)
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g = kin.gaps(qs, GAP_CAP)['gm'].min(-1).values
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gr, = torch.autograd.grad(g.sum(), qs)
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h = 1e-4
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for j in range(6):
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dq = torch.zeros_like(qs); dq[:, j] = h
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fd = (kin.gaps(qs.detach() + dq, GAP_CAP)['gm'].min(-1).values - kin.gaps(qs.detach() - dq, GAP_CAP)['gm'].min(-1).values) / (2 * h)
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rel.append(((gr[:, j] - fd).abs() / (fd.abs() + 1e-3)).max().item())
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res = {'n': len(R), 'fk_max_abs_mm': e_fk, 'orient_max_abs': e_or, 'gap_max_abs_mm': e_gm,
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'collide_bool_mismatch': int((col_js != col_py).sum()), 'feat_max_abs': e_f,
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'grad_rel_err_max': max(rel) if rel else None}
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ok = e_fk < 1e-6 and e_or < 1e-9 and e_gm < 1e-4 and res['collide_bool_mismatch'] == 0 and e_f < 1e-4 and (not rel or max(rel) < 1e-3)
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res['PASS'] = bool(ok)
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print(json.dumps(res, indent=1))
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if __name__ == '__main__':
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main()
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