cbb
48cc566c3f
- 四组单变量对照（同256题同seed）：base / inc_cap / gap_margin 0.5 / 两者同开 限幅后 到达 0.5742→0.5234、time_ok 0.3906→0.2656、2s误差比 0.210→0.279、超VMAX 1.0→0.0、刹停几乎不变 → 之前那部分到达率是超速换来的；损失在逼近末段，不在刹停 - 0.5mm 余量门只付 3 题（0.5742→0.5625）；但 plans['gaps'] 在 0.5mm 截顶，5mm 护栏可达性需抬 cap 重算 - 前提更正：eval_cl 不传 steps、rollout 默认 steps=1（policy.py:156 一步生成），先前按 steps=16 得到的 "当前配方 116ms≈8.6Hz" 作废；真实配方 36.68ms P50 / 49.17ms P99 ≈ 27.3Hz - 归因更正：空算子 0.028ms、空 sync 0.005ms、encode 2.27ms → 不是启动/同步地板； 单步推理仅 7.2ms 且 K=1→8 免费，周期里 ~27ms 是 cum_xf+宽相位的重复计算 - certify_uniform 挂上后 36.7→45.3ms（n=8）/49.6ms（n=16） - kin_torch 补 a/b 形状守卫（脚本里广播不一致会算错 view），测试 30→31 项
625 lines
33 KiB
Python
625 lines
33 KiB
Python
import contextlib
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import io
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import json
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import tempfile
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import unittest
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from pathlib import Path
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from unittest.mock import patch
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import torch
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from ml.eval.eval_cl import json_finite, run
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from ml.eval.diag_collision import replay_certificates, replay_dense, replay_first_hits
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from ml.sim.closed_loop import TeacherOracle, rollout
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from ml.common.kin_torch import Kin, seg_seg_dist
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class EvalMetricsTest(unittest.TestCase):
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def setUp(self):
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self.tasks = [
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{'q0': [0] * 6, 'T': [0] * 3, 'exec_s': 1, 'travel': 5},
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{'q0': [0] * 6, 'T': [0] * 3, 'exec_s': None, 'travel': None},
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{'q0': [0] * 6, 'T': [0] * 3, 'exec_s': 2, 'travel': 6},
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]
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self.per = {k: torch.tensor(v) for k, v in {
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'ok': [True, False, True], 'done': [True, False, True],
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't_done': [0.5, float('nan'), 2.0], 'col': [1.0, 0.0, 0.0],
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'travel': [6.0, 0.0, 8.0], 'brakes': [2.0, 4.0, 6.0],
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'start_fail': [False, True, False], 'goal_ratio_2s': [0.3, 0.9, 0.1],
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'err': [0.2, 200.0, 0.8], 'dls_it': [4, -1, 20],
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'speed_ratio': [1.66, 0.9, 1.2], 'accel_ratio': [0.3, 0.2, 0.5],
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}.items()}
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def evaluate(self, tasks, bs=2, **kw):
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offset = 0
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def fake_rollout(model, kin, batch, device, **kwargs):
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nonlocal offset
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p = {k: v[offset:offset + len(batch)] for k, v in self.per.items()}
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offset += len(batch)
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return {'time_budget': 'mixed'}, p
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with patch('ml.eval.eval_cl.rollout', side_effect=fake_rollout), contextlib.redirect_stdout(io.StringIO()):
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return run(None, None, tasks, 'cpu', bs=bs, **kw)[0]
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def test_global_denominators_and_collision_free_arrival(self):
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result = self.evaluate(self.tasks)
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self.assertEqual(result['n_ref'], 2)
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self.assertAlmostEqual(result['success'], 2 / 3, places=6)
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self.assertAlmostEqual(result['collision_free_success'], 1 / 3, places=6)
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self.assertAlmostEqual(result['collision_rate'], 1 / 3, places=6)
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self.assertEqual(result['time_ok_1p3'], 1.0)
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self.assertEqual(result['time_mean_s'], 1.25)
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self.assertAlmostEqual(result['travel_ratio_median'], 1.2, places=6)
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self.assertAlmostEqual(result['goal_ratio_2s'], 0.3, places=6)
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self.assertEqual(result['err_fail_med'], 200.0)
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self.assertEqual(result['brakes_mean'], 4.0)
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# 极值/分位按逐题值跨批汇总，不能进按 n 加权求均值的批聚合
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self.assertAlmostEqual(result['speed_ratio_p50'], 1.2, places=6)
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self.assertAlmostEqual(result['speed_ratio_p95'], 1.614, places=6)
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self.assertAlmostEqual(result['speed_ratio_max'], 1.66, places=5)
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self.assertAlmostEqual(result['over_vmax_rate'], 2 / 3, places=6) # 1.66 与 1.2 都算超 VMAX，0.9 不算
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self.assertAlmostEqual(result['accel_ratio_max'], 0.5, places=6)
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def test_empty_conditional_metrics_and_explicit_limit(self):
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task = {**self.tasks[0], 'exec_s': None, 'travel': None}
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result = self.evaluate([task], max_sec=0.04)
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self.assertIsNone(result['err_fail_med'])
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self.assertIsNone(result['time_ok_1p3'])
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self.assertIsNone(result['travel_ratio_median'])
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self.assertEqual(result['time_budget'], 'fixed 0.040 s')
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self.assertEqual(result['collision_free_success'], 0.0)
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def test_json_replaces_nonfinite_without_changing_zero(self):
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value = {'items': [float('nan'), float('inf'), -float('inf'), 0.0]}
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text = json.dumps(json_finite(value), allow_nan=False)
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self.assertEqual(json.loads(text), {'items': [None, None, None, 0.0]})
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def test_first_hit_probe_detects_collision(self):
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class PlaneKin:
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def __init__(self, **kwargs):
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self.cont = torch.zeros(6, dtype=torch.bool)
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self.lo, self.hi = torch.full((6,), -10.0), torch.full((6,), 10.0)
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def gaps(self, q, cap):
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return {'gm': q[:, :1].expand(-1, 49).clamp(max=cap)}
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traj = torch.zeros(1, 3, 6)
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traj[0, :, 0] = torch.tensor([1.0, 1.0, -1.0])
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per = {'col': torch.tensor([1.0]), 'first_hit': torch.tensor([0.04]),
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'dls_it': torch.tensor([-1]), 'traj': traj}
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with tempfile.TemporaryDirectory() as folder:
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trace = Path(folder) / 'trace.pt'
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torch.save({'tasks': [self.tasks[0]], 'batches': [({}, per, 1)]}, trace)
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with patch('ml.eval.diag_collision.Kin', PlaneKin), contextlib.redirect_stdout(io.StringIO()):
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result = replay_first_hits(trace)
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self.assertEqual(result['reported_collision_tasks'], 1)
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self.assertEqual(result['replayed_first_hits32'], 1)
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self.assertEqual(result['replayed_first_hits64'], 1)
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self.assertEqual(result['rows'][0]['gap64_mm'], [0.5, 0.5, 0.0, -1.0])
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self.assertEqual(result['rows'][0]['dense_gap64_min_mm'], -1.0)
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def test_dense_probe_detects_between_checkpoints_and_respects_deadline(self):
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class ThinObstacle:
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fp = 'probe'
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def __init__(self, **kwargs):
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pass
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def gaps(self, q, cap):
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return {'gm': ((q[:, :1] - 0.25).abs() - 0.05).clamp(max=cap)}
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traj = torch.zeros(4, 3, 6)
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traj[:, :, 0] = torch.tensor([[0, 1, 1], [0, -0.5, -1], [0, 0, 0.25], [0, 0, 0.25]])
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per = {'traj': traj, 'col': torch.zeros(4), 'limit': torch.tensor([0.04, 0.04, 0.02, 0.04]),
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't_done': torch.tensor([float('nan')] * 3 + [0.02])}
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with tempfile.TemporaryDirectory() as folder:
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trace = Path(folder) / 'trace.pt'
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output = Path(folder) / 'audit.json'
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torch.save({'kin_fp': 'probe', 'tasks': [{}] * 4, 'batches': [({}, per, 4)]}, trace)
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with patch('ml.eval.diag_collision.Kin', ThinObstacle), contextlib.redirect_stdout(io.StringIO()):
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result = replay_dense(trace, [0, 1, 2, 3], 0.01, out=output)
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with self.assertRaisesRegex(ValueError, 'valid task indices'):
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replay_dense(trace, [4])
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with self.assertRaisesRegex(ValueError, 'positive max_step_deg'):
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replay_dense(trace, [0], 0.0)
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self.assertEqual(json.loads(output.read_text())['collision_tasks'], 1)
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self.assertEqual(result['collision_tasks'], 1)
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self.assertEqual(result['rows'][0]['collision_segments'], 1)
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self.assertAlmostEqual(result['rows'][0]['min_gap64_mm'], -0.05)
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self.assertGreater(result['rows'][1]['min_gap64_mm'], 0)
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for row in result['rows'][2:]:
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self.assertGreater(row['min_gap64_mm'], 0)
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self.assertEqual(row['steps'], 1)
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def test_certificate_replay_samples_actual_movement_before_deadlines(self):
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kin = SegmentCertificateTest.ThinObstacle()
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kin.fp = 'probe'
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traj = torch.zeros(3, 4, 6)
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traj[:, :, 0] = torch.tensor([[0, 1, 1, 1], [0, -0.5, -1, -1], [0, 0, 1, 1]])
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per = {'traj': traj, 'limit': torch.tensor([0.06, 0.02, 0.02]),
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't_done': torch.tensor([float('nan'), 0.02, float('nan')]),
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'plans': {'it': torch.arange(3), 'commit': torch.zeros(3, 3, 1, 6),
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'certified': torch.ones(3, 3, 1, dtype=torch.bool), 'gaps': torch.full((3, 3, 2), 0.5)}}
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with tempfile.TemporaryDirectory() as folder:
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trace, output = Path(folder) / 'trace.pt', Path(folder) / 'audit.json'
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torch.save({'kin_fp': 'probe', 'tasks': [{}] * 3, 'batches': [({}, per, 3)]}, trace)
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with patch('ml.eval.diag_collision.Kin', return_value=kin), contextlib.redirect_stdout(io.StringIO()):
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result = replay_certificates(trace, [0, 1, 2], out=output, repeats=1, extra_segments=[(0, 0)])
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with self.assertRaisesRegex(ValueError, 'extra segment'):
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replay_certificates(trace, [1], repeats=1, extra_segments=[(1, 1)])
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self.assertEqual(json.loads(output.read_text())['dense_collision_segments'], 1)
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self.assertEqual([(r['i'], r['start_it']) for r in result['rows']], [(0, 0), (1, 0)])
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self.assertEqual(result['budgets']['32']['safe'], 1)
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self.assertEqual(result['budgets']['32']['collision'], 1)
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self.assertEqual(result['accepted_dense_collision_segments'], 0)
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self.assertTrue(result['batch_single_match'])
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self.assertEqual(result['timing']['calls'], 2)
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class DlsSafetyTest(unittest.TestCase):
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def test_rejected_refinement_retains_safe_candidate(self):
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class ObstacleKin:
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def clamp_box(self, q):
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return q
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def gaps(self, q, cap):
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return {'gm': torch.where(q[:, :1] > 0.3, -torch.ones_like(q[:, :1]), torch.ones_like(q[:, :1]))}
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task = {'q0': [0] * 6, 'T': [2, 0, 0]}
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traj = torch.zeros(1, 2, 6)
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traj[0, 1, 0] = 0.1
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ref = torch.zeros(1, 1, 6)
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ref[0, 0, 0] = 1.0
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patches = {
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'features': lambda *args: torch.zeros(1, 83),
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'goal_err': lambda kin, q, T, R: (torch.full((q.shape[0],), 2.0), torch.zeros(q.shape[0])),
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'dls_steps': lambda *args, **kwargs: ref,
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}
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results = []
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with patch.multiple('ml.sim.closed_loop', **patches):
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for gate in (0, 1):
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_, per = rollout(None, ObstacleKin(), [task], 'cpu', K=1, safety=True,
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max_sec=0.02, return_traj=True, use_sub=False, select='first',
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oracle=TeacherOracle(traj), replan='empty', commit_n=1,
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safety_partial=1, dls_safety=gate)
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results.append(per)
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self.assertEqual(results[0]['col'].item(), 1.0)
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self.assertEqual(results[0]['dls_it'].item(), 0)
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self.assertEqual(results[1]['col'].item(), 0.0)
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self.assertEqual(results[1]['dls_it'].item(), -1)
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self.assertAlmostEqual(results[1]['traj'][0, 1, 0].item(), 0.1)
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def test_feedback_is_reduced_only_after_full_rejection(self):
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class ObstacleKin:
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def clamp_box(self, q):
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return q
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def gaps(self, q, cap):
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return {'gm': torch.where(q[:, :1] > 0.3, -torch.ones_like(q[:, :1]), torch.ones_like(q[:, :1]))}
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task = {'q0': [0] * 6, 'T': [10, 0, 0]}
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traj = torch.zeros(1, 17, 6)
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traj[0, :, 0] = -0.2 * torch.arange(17)
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ref = torch.zeros(1, 1, 6)
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ref[0, 0, 0] = 1.0
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patches = {
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'features': lambda *args: torch.zeros(1, 83),
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'goal_err': lambda kin, q, T, R: (torch.full((q.shape[0],), 10.0), torch.zeros(q.shape[0])),
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'dls_steps': lambda *args, **kwargs: ref,
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}
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results = []
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with patch.multiple('ml.sim.closed_loop', **patches):
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for gain in (1.0, 0.5):
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_, per = rollout(None, ObstacleKin(), [task], 'cpu', K=1, safety=True,
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max_sec=0.04, return_traj=True, use_sub=False, select='first',
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oracle=TeacherOracle(traj), replan='empty', commit_n=1,
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safety_partial=1, fb_gain=0.6, fb_block_gain=gain)
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results.append(per)
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self.assertEqual(results[0]['brakes'].item(), 2.0)
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self.assertEqual(results[1]['brakes'].item(), 1.0)
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self.assertEqual(results[1]['traj'][0, 1, 0].item(), 0.0)
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self.assertAlmostEqual(results[1]['traj'][0, 2, 0].item(), 0.1)
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self.assertEqual(results[1]['col'].item(), 0.0)
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class ClampedPathSafetyTest(unittest.TestCase):
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def rollout_candidate(self, increments, safety_partial, goal_error=10.0, **kwargs):
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class ObstacleKin:
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def clamp_box(self, q):
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clamped = q.clone()
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clamped[..., 4] = clamped[..., 4].clamp(max=0.0)
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return clamped
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def gaps(self, q, cap):
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collision = (q[:, :1] > 0.1) & (q[:, 4:5] < 0.2)
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return {'gm': torch.where(collision, -torch.ones_like(q[:, :1]), torch.ones_like(q[:, :1]))}
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task = {'q0': [0] * 6, 'T': [goal_error, 0, 0]}
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commit_n = len(increments)
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traj = torch.zeros(1, commit_n + 1, 6)
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traj[0, 1:] = torch.tensor(increments).cumsum(0)
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patches = {
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'features': lambda *args: torch.zeros(1, 83),
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'goal_err': lambda kin, q, T, R: (torch.full((q.shape[0],), goal_error), torch.zeros(q.shape[0])),
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}
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with patch.multiple('ml.sim.closed_loop', **patches):
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_, per = rollout(None, ObstacleKin(), [task], 'cpu', K=1, safety=True,
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max_sec=0.02 * commit_n, return_traj=True, use_sub=False, select='first',
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oracle=TeacherOracle(traj), replan='empty', commit_n=commit_n,
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safety_partial=safety_partial, **kwargs)
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return per
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def test_full_safety_rejects_collision_after_joint_clamp(self):
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# Raw endpoint/midpoint are safe, but the J5 upper limit puts the endpoint at (0.2, 0).
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per = self.rollout_candidate([[0.2, 0, 0, 0, 0.6, 0]], safety_partial=0)
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self.assertEqual(per['col'].item(), 0.0)
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torch.testing.assert_close(per['traj'], torch.zeros(1, 2, 6), rtol=0, atol=0)
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def test_partial_safety_rejects_collision_after_joint_clamp(self):
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per = self.rollout_candidate([[0.2, 0, 0, 0, 0.6, 0]], safety_partial=1)
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self.assertEqual(per['col'].item(), 0.0)
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torch.testing.assert_close(per['traj'], torch.zeros(1, 2, 6), rtol=0, atol=0)
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def test_partial_safety_keeps_only_clamped_safe_prefix(self):
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per = self.rollout_candidate([
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[0.05, 0, 0, 0, 0.6, 0],
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[0.2, 0, 0, 0, 0.6, 0],
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], safety_partial=1)
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self.assertAlmostEqual(per['traj'][0, -1, 0].item(), 0.05)
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self.assertEqual(per['traj'][0, -1, 4].item(), 0.0)
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self.assertEqual(per['col'].item(), 0.0)
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expected = torch.zeros(1, 3, 6)
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expected[0, 1:, 0] = 0.05
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torch.testing.assert_close(per['traj'], expected)
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torch.testing.assert_close(per['plans']['commit'][0, 0, 1], torch.zeros(6), rtol=0, atol=0)
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def test_near_dls_rejects_clamped_collision_and_keeps_candidate(self):
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candidate = [[0.05, 0, 0, 0, 0.6, 0]]
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ref = torch.tensor([[[0.2, 0, 0, 0, 0.6, 0]]])
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for safety_partial in (0, 1):
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with self.subTest(safety_partial=safety_partial):
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with patch('ml.sim.closed_loop.dls_steps', return_value=ref) as dls:
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per = self.rollout_candidate(candidate, safety_partial, goal_error=2.0, dls_safety=1)
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dls.assert_called_once()
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self.assertEqual(per['col'].item(), 0.0)
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self.assertEqual(per['dls_it'].item(), -1)
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self.assertEqual(per['brakes'].item(), 0.0)
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expected = torch.zeros(1, 2, 6)
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expected[0, 1, 0] = 0.05
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torch.testing.assert_close(per['traj'], expected)
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torch.testing.assert_close(per['plans']['commit'], torch.tensor(candidate)[None, None])
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self.assertTrue(per['plans']['certified'].all().item())
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def test_r3_clamped_queue_origin_prefix_and_certificates_match_execution(self):
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class SafeKin:
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def clamp_box(self, q):
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clamped = q.clone()
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clamped[..., 4] = clamped[..., 4].clamp(max=0.0)
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return clamped
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def gaps(self, q, cap):
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return {'gm': torch.ones_like(q[:, :1])}
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prefixes = []
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def capture_features(kin, q, qp, T, R, pre, r, sub, sf):
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prefixes.append((pre.clone(), int(r.item())))
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return torch.zeros(1, 83)
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teacher = torch.zeros(1, 25, 6)
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teacher[0, :, 0] = 0.02 * torch.arange(25)
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teacher[0, :, 4] = 0.6 * torch.arange(25)
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task = {'q0': [0] * 6, 'T': [10, 0, 0]}
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patches = {
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'features': capture_features,
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'goal_err': lambda kin, q, T, R: (torch.full((q.shape[0],), 10.0), torch.zeros(q.shape[0])),
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}
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with patch.multiple('ml.sim.closed_loop', **patches):
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_, per = rollout(None, SafeKin(), [task], 'cpu', K=1, safety=True,
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max_sec=0.16, return_traj=True, use_sub=False, select='first',
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oracle=TeacherOracle(teacher), replan='r3', commit_n=3, safety_partial=1)
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self.assertEqual([r for _, r in prefixes], [0, 2, 3, 3])
|
||
for pre, r in prefixes[1:]:
|
||
torch.testing.assert_close(pre[..., 4], torch.zeros(1, 3), rtol=0, atol=0)
|
||
torch.testing.assert_close(pre[:, :r, 0], torch.full((1, r), 0.02))
|
||
self.assertEqual(per['col'].item(), 0.0)
|
||
expected = torch.zeros(1, 9, 6)
|
||
expected[0, :, 0] = 0.02 * torch.arange(9)
|
||
torch.testing.assert_close(per['traj'], expected)
|
||
plans = per['plans']
|
||
self.assertEqual(plans['it'].tolist(), [0, 3, 6, 9])
|
||
torch.testing.assert_close(plans['commit'][..., 4], torch.full((1, 4, 3), 0.6))
|
||
self.assertTrue(plans['certified'].all().item())
|
||
self.assertTrue((plans['gaps'] > 0).all().item())
|
||
checked = 0
|
||
for i, start in enumerate(plans['it'].tolist()):
|
||
n = min(3, 8 - start)
|
||
if n <= 0:
|
||
continue
|
||
torch.testing.assert_close(plans['qe'][:, i], per['traj'][:, start], rtol=0, atol=0)
|
||
before = per['traj'][:, start:start + n]
|
||
after = per['traj'][:, start + 1:start + n + 1]
|
||
torch.testing.assert_close(plans['points'][:, i, :n], after, rtol=0, atol=0)
|
||
torch.testing.assert_close(plans['midpoints'][:, i, :n], before + 0.5 * (after - before),
|
||
rtol=0, atol=0)
|
||
checked += n
|
||
self.assertEqual(checked, 8)
|
||
|
||
def rollout_fallback(self, safety_partial, midpoint_collision=False):
|
||
class ObstacleKin:
|
||
def clamp_box(self, q):
|
||
return q
|
||
|
||
def gaps(self, q, cap):
|
||
collision = q[:, :1] > 0.3
|
||
if midpoint_collision:
|
||
collision |= (q[:, :1] > 0.09) & (q[:, :1] < 0.11)
|
||
return {'gm': torch.where(collision, -torch.ones_like(q[:, :1]), torch.ones_like(q[:, :1]))}
|
||
|
||
teacher = torch.zeros(1, 4, 6)
|
||
teacher[0, :, 0] = torch.arange(4)
|
||
ref = torch.zeros(1, 1, 6)
|
||
ref[0, 0, 0] = 0.2
|
||
task = {'q0': [0] * 6, 'T': [10, 0, 0]}
|
||
patches = {
|
||
'features': lambda *args: torch.zeros(1, 83),
|
||
'goal_err': lambda kin, q, T, R: (torch.full((q.shape[0],), 10.0), torch.zeros(q.shape[0])),
|
||
'dls_steps': lambda *args, **kwargs: ref,
|
||
}
|
||
with patch.multiple('ml.sim.closed_loop', **patches):
|
||
_, per = rollout(None, ObstacleKin(), [task], 'cpu', K=1, safety=True,
|
||
max_sec=0.06, return_traj=True, use_sub=False, select='first',
|
||
oracle=TeacherOracle(teacher), replan='empty', commit_n=3,
|
||
safety_partial=safety_partial, fb_gain=1.0, fb_fallback=1.0)
|
||
return per
|
||
|
||
def test_fallback_executes_one_safe_dls_step_then_certified_zeros(self):
|
||
for safety_partial in (0, 1):
|
||
with self.subTest(safety_partial=safety_partial):
|
||
per = self.rollout_fallback(safety_partial)
|
||
self.assertEqual(per['col'].item(), 0.0)
|
||
self.assertEqual(per['brakes'].item(), 1.0)
|
||
expected = torch.zeros(1, 4, 6)
|
||
expected[0, 1:, 0] = 0.2
|
||
torch.testing.assert_close(per['traj'], expected)
|
||
commit = torch.zeros(1, 1, 3, 6)
|
||
commit[0, 0, 0, 0] = 0.2
|
||
plans = per['plans']
|
||
self.assertEqual(plans['it'].tolist(), [0])
|
||
torch.testing.assert_close(plans['commit'], commit, rtol=0, atol=0)
|
||
self.assertTrue(plans['certified'].all().item())
|
||
self.assertTrue((plans['gaps'] > 0).all().item())
|
||
torch.testing.assert_close(plans['points'][:, 0], expected[:, 1:])
|
||
torch.testing.assert_close(plans['midpoints'][:, 0],
|
||
expected[:, :-1] + 0.5 * (expected[:, 1:] - expected[:, :-1]))
|
||
|
||
def test_fallback_rejects_safe_endpoint_with_colliding_midpoint(self):
|
||
for safety_partial in (0, 1):
|
||
with self.subTest(safety_partial=safety_partial):
|
||
per = self.rollout_fallback(safety_partial, midpoint_collision=True)
|
||
self.assertEqual(per['col'].item(), 0.0)
|
||
self.assertEqual(per['brakes'].item(), 1.0)
|
||
torch.testing.assert_close(per['traj'], torch.zeros(1, 4, 6), rtol=0, atol=0)
|
||
torch.testing.assert_close(per['plans']['commit'], torch.zeros(1, 1, 3, 6), rtol=0, atol=0)
|
||
self.assertFalse(per['plans']['certified'].any().item())
|
||
|
||
|
||
class SegmentCertificateTest(unittest.TestCase):
|
||
class ThinObstacle(Kin):
|
||
def __init__(self):
|
||
self.dtype = torch.float64
|
||
self.lo, self.hi = torch.full((6,), -5.0), torch.full((6,), 5.0)
|
||
self.cont = torch.zeros(6, dtype=torch.bool)
|
||
self.cert_groups = torch.tensor([0])
|
||
self.cert_lever = torch.tensor([[1.0, 0, 0, 0, 0, 0]], dtype=self.dtype)
|
||
|
||
def gaps(self, q, cap):
|
||
return {'gm': ((q[:, :1] - 0.25).abs() - 0.05).clamp(max=cap)}
|
||
|
||
def test_bound_detects_hidden_collision_and_accepts_clear_segment(self):
|
||
a, b = torch.zeros(3, 6, dtype=torch.float64), torch.zeros(3, 6, dtype=torch.float64)
|
||
a[:, 0] = torch.tensor([0.0, -1.0, 0.25])
|
||
b[:, 0] = torch.tensor([1.0, 0.0, 0.25])
|
||
result = self.ThinObstacle().certify_segments(a, b)
|
||
self.assertEqual(result['safe'].tolist(), [False, True, False])
|
||
self.assertEqual(result['collision'].tolist(), [True, False, True])
|
||
self.assertEqual(result['queries'].tolist(), [4, 2, 2])
|
||
|
||
def test_budget_and_depth_exhaustion_do_not_accept_unknown_segment(self):
|
||
a, b = torch.zeros(1, 6, dtype=torch.float64), torch.zeros(1, 6, dtype=torch.float64)
|
||
b[:, 0] = 1.0
|
||
kin = self.ThinObstacle()
|
||
for kwargs in ({'max_queries': 2}, {'max_depth': 0}):
|
||
with self.subTest(**kwargs):
|
||
result = kin.certify_segments(a, b, **kwargs)
|
||
self.assertFalse(result['safe'].item())
|
||
self.assertTrue(result['unresolved'].item())
|
||
self.assertEqual(result['queries'].item(), 2)
|
||
|
||
def test_shape_mismatch_between_endpoints_is_rejected(self):
|
||
a = torch.zeros(1, 6, dtype=torch.float64)
|
||
b = torch.zeros(2, 6, dtype=torch.float64)
|
||
kin = self.ThinObstacle()
|
||
for method in (kin.certify_segments, kin.certify_uniform):
|
||
with self.subTest(method=method.__name__):
|
||
with self.assertRaisesRegex(ValueError, 'identical shape'):
|
||
method(a, b)
|
||
|
||
def test_budget_exhaustion_scales_with_gap_and_bound(self):
|
||
class ConstantGap(self.ThinObstacle):
|
||
def gaps(self, q, cap):
|
||
return {'gm': torch.full((q.shape[0], 1), 0.1, dtype=q.dtype, device=q.device)}
|
||
|
||
# 界取 1 mm/度：1° 段要证明无碰撞，必须细分到位移界 < 两端间隙之和(0.2 mm)。
|
||
kin = ConstantGap()
|
||
kin.cert_lever = torch.ones(1, 6, dtype=torch.float64)
|
||
a, b = torch.zeros(1, 6, dtype=torch.float64), torch.zeros(1, 6, dtype=torch.float64)
|
||
b[:, 0] = 1.0
|
||
for budget, safe, queries in ((4, False, 3), (8, False, 5), (9, True, 9)):
|
||
with self.subTest(budget=budget):
|
||
result = kin.certify_segments(a, b, max_queries=budget)
|
||
self.assertEqual(bool(result['safe'].item()), safe)
|
||
self.assertEqual(int(result['queries'].item()), queries)
|
||
|
||
def test_tensor_budget_cannot_wrap_depth(self):
|
||
a, b = torch.zeros(1, 6, dtype=torch.float64), torch.zeros(1, 6, dtype=torch.float64)
|
||
a[:, 0] = b[:, 0] = 0.25
|
||
for tensor in (torch.tensor(255, dtype=torch.uint8), torch.tensor(255)):
|
||
with self.subTest(dtype=tensor.dtype):
|
||
with self.assertRaisesRegex(ValueError, 'plain ints'):
|
||
self.ThinObstacle().certify_segments(a, b, max_depth=tensor)
|
||
|
||
def test_batch_matches_per_root_and_ignores_order(self):
|
||
kin = self.ThinObstacle()
|
||
a = torch.tensor([[0.0, 0, 0, 0, 0, 0], [0.25, 0, 0, 0, 0, 0], [-1.0, 0, 0, 0, 0, 0]], dtype=torch.float64)
|
||
b = torch.tensor([[1.0, 0, 0, 0, 0, 0], [0.25, 0, 0, 0, 0, 0], [0.0, 0, 0, 0, 0, 0]], dtype=torch.float64)
|
||
batch = kin.certify_segments(a, b, max_queries=16)
|
||
self.assertEqual(batch['collision'].tolist(), [True, True, False])
|
||
self.assertEqual(batch['safe'].tolist(), [False, False, True])
|
||
for perm in ([0, 1, 2], [2, 1, 0], [1, 0, 2]):
|
||
index = torch.tensor(perm)
|
||
moved = kin.certify_segments(a[index], b[index], max_queries=16)
|
||
for key in ('safe', 'collision', 'unresolved', 'queries'):
|
||
torch.testing.assert_close(moved[key], batch[key][index], rtol=0, atol=0)
|
||
|
||
def test_empty_batch_and_all_rejected_frontier(self):
|
||
kin = self.ThinObstacle()
|
||
empty = kin.certify_segments(torch.zeros(0, 6, dtype=torch.float64), torch.zeros(0, 6, dtype=torch.float64))
|
||
self.assertEqual(empty['safe'].shape, (0,))
|
||
a = torch.tensor([[0.25, 0, 0, 0, 0, 0], [6.0, 0, 0, 0, 0, 0]], dtype=torch.float64)
|
||
result = kin.certify_segments(a, a.clone(), max_queries=2)
|
||
self.assertEqual(result['safe'].tolist(), [False, False])
|
||
self.assertEqual(result['collision'].tolist(), [True, False])
|
||
self.assertEqual(result['outside_box'].tolist(), [False, True])
|
||
|
||
def test_uniform_matches_bound_formula_and_needs_no_round_trips(self):
|
||
class ConstantGap(self.ThinObstacle):
|
||
def gaps(self, q, cap):
|
||
return {'gm': torch.full((q.shape[0], 1), 0.1, dtype=q.dtype, device=q.device)}
|
||
|
||
# 界 1 mm/度、间隙 0.1 mm：子段判据 0.2 > 1/n 需要 n ≥ 6，n=5 必须落 unproven。
|
||
kin = ConstantGap()
|
||
kin.cert_lever = torch.ones(1, 6, dtype=torch.float64)
|
||
a, b = torch.zeros(1, 6, dtype=torch.float64), torch.zeros(1, 6, dtype=torch.float64)
|
||
b[:, 0] = 1.0
|
||
for n, safe in ((5, False), (6, True)):
|
||
with self.subTest(n=n):
|
||
result = kin.certify_uniform(a, b, n=n)
|
||
self.assertEqual(bool(result['safe'].item()), safe)
|
||
self.assertEqual(result['points'], n + 1)
|
||
self.assertFalse(bool(result['collision'].item()))
|
||
self.assertEqual(bool(result['unproven'].item()), not safe)
|
||
self.assertAlmostEqual(result['cap_required_mm'], 1.0 / n + 2e-4, places=9)
|
||
|
||
def test_uniform_catches_hidden_collision_and_rejects_short_grids(self):
|
||
a = torch.tensor([[0.0, 0, 0, 0, 0, 0], [-1.0, 0, 0, 0, 0, 0], [6.0, 0, 0, 0, 0, 0]], dtype=torch.float64)
|
||
b = torch.tensor([[1.0, 0, 0, 0, 0, 0], [0.0, 0, 0, 0, 0, 0], [6.0, 0, 0, 0, 0, 0]], dtype=torch.float64)
|
||
kin = self.ThinObstacle()
|
||
short = kin.certify_uniform(a, b, n=2)
|
||
self.assertEqual(short['collision'].tolist(), [False, False, False])
|
||
self.assertEqual(short['unproven'].tolist(), [True, False, False])
|
||
self.assertEqual(short['safe'].tolist(), [False, True, False])
|
||
for n in (4, 8, 64):
|
||
with self.subTest(n=n):
|
||
result = kin.certify_uniform(a, b, n=n)
|
||
self.assertEqual(result['collision'].tolist(), [True, False, False])
|
||
self.assertEqual(result['safe'].tolist(), [False, True, False])
|
||
self.assertEqual(result['outside_box'].tolist(), [False, False, True])
|
||
|
||
def test_uniform_requires_plain_int_count(self):
|
||
a = torch.zeros(1, 6, dtype=torch.float64)
|
||
for tensor in (torch.tensor(8, dtype=torch.uint8), torch.tensor(8)):
|
||
with self.subTest(dtype=tensor.dtype):
|
||
with self.assertRaisesRegex(ValueError, 'subdivision count'):
|
||
self.ThinObstacle().certify_uniform(a, a.clone(), n=tensor)
|
||
|
||
def test_nonfinite_and_outside_box_are_rejected(self):
|
||
a, b = torch.zeros(1, 6, dtype=torch.float64), torch.zeros(1, 6, dtype=torch.float64)
|
||
kin = self.ThinObstacle()
|
||
with patch.object(kin, 'gaps', side_effect=lambda q, cap: {'gm': torch.full((q.shape[0], 1), float('nan'))}):
|
||
result = kin.certify_segments(a, b)
|
||
self.assertFalse(result['safe'].item())
|
||
self.assertTrue(result['unresolved'].item())
|
||
for value in (float('inf'), -float('inf')):
|
||
with self.subTest(value=value):
|
||
with patch.object(kin, 'gaps',
|
||
side_effect=lambda q, cap, v=value: {'gm': torch.full((q.shape[0], 1), v)}):
|
||
result = kin.certify_segments(a, b)
|
||
self.assertFalse(result['safe'].item())
|
||
a[:, 0] = b[:, 0] = 6.0
|
||
result = kin.certify_segments(a, b)
|
||
self.assertFalse(result['safe'].item())
|
||
self.assertTrue(result['outside_box'].item())
|
||
|
||
def test_point_segment_distance_is_symmetric_and_differentiable(self):
|
||
p = torch.tensor([[0.0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], dtype=torch.float64, requires_grad=True)
|
||
q = torch.tensor([[2.0, 0, 0]] * 4, dtype=torch.float64)
|
||
point = torch.tensor([[1.0, 0, 0], [1, 3, 0], [4, 0, 0], [-1, 0, 0]], dtype=torch.float64)
|
||
forward = seg_seg_dist(p, q, point, point)
|
||
reverse = seg_seg_dist(point, point, p, q)
|
||
torch.testing.assert_close(forward, torch.tensor([0.0, 3, 2, 1], dtype=torch.float64), atol=1e-8, rtol=0)
|
||
torch.testing.assert_close(forward, reverse, atol=1e-8, rtol=0)
|
||
forward.sum().backward()
|
||
self.assertTrue(torch.isfinite(p.grad).all())
|
||
torch.testing.assert_close(seg_seg_dist(p.detach(), p.detach(), point, point),
|
||
(p.detach() - point).norm(dim=-1), atol=1e-8, rtol=0)
|
||
|
||
|
||
class SpeedAndMarginTest(unittest.TestCase):
|
||
"""轮次33：反馈叠加后的每轴速度限幅，以及安全层的正余量门限（含 cap 截顶坑）。"""
|
||
STEP = [1.2, 1.2, 1.8, 1.8, 2.4, 3.6]
|
||
|
||
def rollout_one(self, teacher_step, gap_value, fb=None, **kwargs):
|
||
class FlatKin:
|
||
def clamp_box(self, q):
|
||
return q
|
||
|
||
def gaps(self, q, cap):
|
||
return {'gm': torch.full((q.shape[0], 49), float(gap_value))}
|
||
|
||
commit_n = 2
|
||
traj = torch.zeros(1, commit_n + 1, 6)
|
||
traj[0, 1:] = torch.tensor(teacher_step)[None].repeat(commit_n, 1).cumsum(0) # teacher_step = 每步各轴的度数增量
|
||
patches = {
|
||
'features': lambda *args: torch.zeros(1, 83),
|
||
'goal_err': lambda kin, q, T, R: (torch.full((q.shape[0],), 10.0), torch.zeros(q.shape[0])),
|
||
}
|
||
with patch.multiple('ml.sim.closed_loop', **patches):
|
||
with patch('ml.sim.closed_loop.dls_steps', return_value=torch.tensor([[[1.0, 0, 0, 0, 0, 0]]])):
|
||
_, per = rollout(None, FlatKin(), [{'q0': [0] * 6, 'T': [10, 0, 0]}], 'cpu', K=1, safety=True,
|
||
max_sec=0.02 * commit_n, return_traj=True, use_sub=False, select='first',
|
||
oracle=TeacherOracle(traj), replan='empty', commit_n=commit_n,
|
||
**kwargs)
|
||
return per
|
||
|
||
def test_feedback_over_speed_is_measured_not_assumed(self):
|
||
# 策略给满 STEP（J1 +1.2°）+ 2.2×1.0° 反馈 = 3.4° > VMAX·DT
|
||
per = self.rollout_one([1.2, 0, 0, 0, 0, 0], 5.0, fb_gain=2.2)
|
||
self.assertAlmostEqual(per['traj'][0, 1, 0].item(), 3.4, places=5)
|
||
self.assertAlmostEqual(per['speed_ratio'][0].item(), 3.4 / 1.2, places=5)
|
||
|
||
def test_inc_cap_limits_actual_step_to_vmax(self):
|
||
per = self.rollout_one([1.2, 0, 0, 0, 0, 0], 5.0, fb_gain=2.2, inc_cap=1)
|
||
self.assertAlmostEqual(per['traj'][0, 1, 0].item(), 1.2, places=5)
|
||
self.assertLessEqual(per['speed_ratio'][0].item(), 1.0 + 1e-6)
|
||
|
||
def test_margin_rejects_near_obstacle_step_that_passes_at_zero(self):
|
||
moved = self.rollout_one([0.5, 0, 0, 0, 0, 0], 0.3, safety_partial=1)
|
||
self.assertAlmostEqual(moved['traj'][0, -1, 0].item(), 1.0, places=5)
|
||
self.assertEqual(moved['brakes'].item(), 0.0)
|
||
braked = self.rollout_one([0.5, 0, 0, 0, 0, 0], 0.3, gap_margin=0.5, safety_partial=1)
|
||
self.assertEqual(braked['brakes'].item(), 1.0)
|
||
torch.testing.assert_close(braked['traj'], torch.zeros(1, 3, 6), rtol=0, atol=0)
|
||
|
||
def test_margin_below_cap_still_accepts_clear_step(self):
|
||
# 余量 0.5 必须仍能通过 5 mm 间隙：若宽相位 cap 没抬到 margin+0.5，截顶值 0.5 会让门限恒假
|
||
per = self.rollout_one([0.5, 0, 0, 0, 0, 0], 5.0, gap_margin=0.5, safety_partial=1)
|
||
self.assertEqual(per['brakes'].item(), 0.0)
|
||
self.assertAlmostEqual(per['traj'][0, -1, 0].item(), 1.0, places=5)
|
||
self.assertTrue((per['plans']['gaps'] > 0.5).all().item())
|
||
|
||
|
||
if __name__ == '__main__':
|
||
unittest.main()
|