cbb
ee7fe829ac
- 探测脚本把认证点当成与特征无关的已知量，据此测出的 共享cum_xf −4.3ms / 合并一次调用 −8.1ms 都不成立：真实顺序是 特征(q)→推理→候选→认证点，同一次规划内既不能共享 xf 也不能合并 gaps 只有轮次34 已落地的 gm_groups 复用同姿态 xf（−3.4ms，22项指标逐位不变）是真的 - 守卫：xf 行数与 q 不符时抛错（之前会静默按别的行做宽相位，实测触发 CUDA device-side assert） - 测试 34→35 项；check_parity 仍 PASS（2000样本，间隙差 2.52e-13 mm、碰撞布尔差 0、特征差 5.96e-08）
718 lines
38 KiB
Python
718 lines
38 KiB
Python
import contextlib
|
||
import io
|
||
import json
|
||
import tempfile
|
||
import unittest
|
||
from pathlib import Path
|
||
from unittest.mock import patch
|
||
|
||
import torch
|
||
|
||
from ml.eval.eval_cl import json_finite, run
|
||
from ml.eval.diag_collision import replay_certificates, replay_dense, replay_first_hits
|
||
from ml.sim.closed_loop import TeacherOracle, rollout
|
||
from ml.common.kin_torch import Kin, seg_seg_dist
|
||
|
||
|
||
class EvalMetricsTest(unittest.TestCase):
|
||
def setUp(self):
|
||
self.tasks = [
|
||
{'q0': [0] * 6, 'T': [0] * 3, 'exec_s': 1, 'travel': 5},
|
||
{'q0': [0] * 6, 'T': [0] * 3, 'exec_s': None, 'travel': None},
|
||
{'q0': [0] * 6, 'T': [0] * 3, 'exec_s': 2, 'travel': 6},
|
||
]
|
||
self.per = {k: torch.tensor(v) for k, v in {
|
||
'ok': [True, False, True], 'done': [True, False, True],
|
||
't_done': [0.5, float('nan'), 2.0], 'col': [1.0, 0.0, 0.0],
|
||
'travel': [6.0, 0.0, 8.0], 'brakes': [2.0, 4.0, 6.0],
|
||
'start_fail': [False, True, False], 'goal_ratio_2s': [0.3, 0.9, 0.1],
|
||
'err': [0.2, 200.0, 0.8], 'dls_it': [4, -1, 20],
|
||
'speed_ratio': [1.66, 0.9, 1.2], 'accel_ratio': [0.3, 0.2, 0.5],
|
||
}.items()}
|
||
|
||
def evaluate(self, tasks, bs=2, **kw):
|
||
offset = 0
|
||
|
||
def fake_rollout(model, kin, batch, device, **kwargs):
|
||
nonlocal offset
|
||
p = {k: v[offset:offset + len(batch)] for k, v in self.per.items()}
|
||
offset += len(batch)
|
||
return {'time_budget': 'mixed'}, p
|
||
|
||
with patch('ml.eval.eval_cl.rollout', side_effect=fake_rollout), contextlib.redirect_stdout(io.StringIO()):
|
||
return run(None, None, tasks, 'cpu', bs=bs, **kw)[0]
|
||
|
||
def test_global_denominators_and_collision_free_arrival(self):
|
||
result = self.evaluate(self.tasks)
|
||
self.assertEqual(result['n_ref'], 2)
|
||
self.assertAlmostEqual(result['success'], 2 / 3, places=6)
|
||
self.assertAlmostEqual(result['collision_free_success'], 1 / 3, places=6)
|
||
self.assertAlmostEqual(result['collision_rate'], 1 / 3, places=6)
|
||
self.assertEqual(result['time_ok_1p3'], 1.0)
|
||
self.assertEqual(result['time_mean_s'], 1.25)
|
||
self.assertAlmostEqual(result['travel_ratio_median'], 1.2, places=6)
|
||
self.assertAlmostEqual(result['goal_ratio_2s'], 0.3, places=6)
|
||
self.assertEqual(result['err_fail_med'], 200.0)
|
||
self.assertEqual(result['brakes_mean'], 4.0)
|
||
# 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
|
||
self.assertAlmostEqual(result['speed_ratio_p50'], 1.2, places=6)
|
||
self.assertAlmostEqual(result['speed_ratio_p95'], 1.614, places=6)
|
||
self.assertAlmostEqual(result['speed_ratio_max'], 1.66, places=5)
|
||
self.assertAlmostEqual(result['over_vmax_rate'], 2 / 3, places=6) # 1.66 与 1.2 都算超 VMAX，0.9 不算
|
||
self.assertAlmostEqual(result['accel_ratio_max'], 0.5, places=6)
|
||
|
||
def test_empty_conditional_metrics_and_explicit_limit(self):
|
||
task = {**self.tasks[0], 'exec_s': None, 'travel': None}
|
||
result = self.evaluate([task], max_sec=0.04)
|
||
self.assertIsNone(result['err_fail_med'])
|
||
self.assertIsNone(result['time_ok_1p3'])
|
||
self.assertIsNone(result['travel_ratio_median'])
|
||
self.assertEqual(result['time_budget'], 'fixed 0.040 s')
|
||
self.assertEqual(result['collision_free_success'], 0.0)
|
||
|
||
def test_json_replaces_nonfinite_without_changing_zero(self):
|
||
value = {'items': [float('nan'), float('inf'), -float('inf'), 0.0]}
|
||
text = json.dumps(json_finite(value), allow_nan=False)
|
||
self.assertEqual(json.loads(text), {'items': [None, None, None, 0.0]})
|
||
|
||
def test_first_hit_probe_detects_collision(self):
|
||
class PlaneKin:
|
||
def __init__(self, **kwargs):
|
||
self.cont = torch.zeros(6, dtype=torch.bool)
|
||
self.lo, self.hi = torch.full((6,), -10.0), torch.full((6,), 10.0)
|
||
|
||
def gaps(self, q, cap):
|
||
return {'gm': q[:, :1].expand(-1, 49).clamp(max=cap)}
|
||
|
||
traj = torch.zeros(1, 3, 6)
|
||
traj[0, :, 0] = torch.tensor([1.0, 1.0, -1.0])
|
||
per = {'col': torch.tensor([1.0]), 'first_hit': torch.tensor([0.04]),
|
||
'dls_it': torch.tensor([-1]), 'traj': traj}
|
||
with tempfile.TemporaryDirectory() as folder:
|
||
trace = Path(folder) / 'trace.pt'
|
||
torch.save({'tasks': [self.tasks[0]], 'batches': [({}, per, 1)]}, trace)
|
||
with patch('ml.eval.diag_collision.Kin', PlaneKin), contextlib.redirect_stdout(io.StringIO()):
|
||
result = replay_first_hits(trace)
|
||
self.assertEqual(result['reported_collision_tasks'], 1)
|
||
self.assertEqual(result['replayed_first_hits32'], 1)
|
||
self.assertEqual(result['replayed_first_hits64'], 1)
|
||
self.assertEqual(result['rows'][0]['gap64_mm'], [0.5, 0.5, 0.0, -1.0])
|
||
self.assertEqual(result['rows'][0]['dense_gap64_min_mm'], -1.0)
|
||
|
||
def test_dense_probe_detects_between_checkpoints_and_respects_deadline(self):
|
||
class ThinObstacle:
|
||
fp = 'probe'
|
||
|
||
def __init__(self, **kwargs):
|
||
pass
|
||
|
||
def gaps(self, q, cap):
|
||
return {'gm': ((q[:, :1] - 0.25).abs() - 0.05).clamp(max=cap)}
|
||
|
||
traj = torch.zeros(4, 3, 6)
|
||
traj[:, :, 0] = torch.tensor([[0, 1, 1], [0, -0.5, -1], [0, 0, 0.25], [0, 0, 0.25]])
|
||
per = {'traj': traj, 'col': torch.zeros(4), 'limit': torch.tensor([0.04, 0.04, 0.02, 0.04]),
|
||
't_done': torch.tensor([float('nan')] * 3 + [0.02])}
|
||
with tempfile.TemporaryDirectory() as folder:
|
||
trace = Path(folder) / 'trace.pt'
|
||
output = Path(folder) / 'audit.json'
|
||
torch.save({'kin_fp': 'probe', 'tasks': [{}] * 4, 'batches': [({}, per, 4)]}, trace)
|
||
with patch('ml.eval.diag_collision.Kin', ThinObstacle), contextlib.redirect_stdout(io.StringIO()):
|
||
result = replay_dense(trace, [0, 1, 2, 3], 0.01, out=output)
|
||
with self.assertRaisesRegex(ValueError, 'valid task indices'):
|
||
replay_dense(trace, [4])
|
||
with self.assertRaisesRegex(ValueError, 'positive max_step_deg'):
|
||
replay_dense(trace, [0], 0.0)
|
||
self.assertEqual(json.loads(output.read_text())['collision_tasks'], 1)
|
||
self.assertEqual(result['collision_tasks'], 1)
|
||
self.assertEqual(result['rows'][0]['collision_segments'], 1)
|
||
self.assertAlmostEqual(result['rows'][0]['min_gap64_mm'], -0.05)
|
||
self.assertGreater(result['rows'][1]['min_gap64_mm'], 0)
|
||
for row in result['rows'][2:]:
|
||
self.assertGreater(row['min_gap64_mm'], 0)
|
||
self.assertEqual(row['steps'], 1)
|
||
|
||
def test_certificate_replay_samples_actual_movement_before_deadlines(self):
|
||
kin = SegmentCertificateTest.ThinObstacle()
|
||
kin.fp = 'probe'
|
||
traj = torch.zeros(3, 4, 6)
|
||
traj[:, :, 0] = torch.tensor([[0, 1, 1, 1], [0, -0.5, -1, -1], [0, 0, 1, 1]])
|
||
per = {'traj': traj, 'limit': torch.tensor([0.06, 0.02, 0.02]),
|
||
't_done': torch.tensor([float('nan'), 0.02, float('nan')]),
|
||
'plans': {'it': torch.arange(3), 'commit': torch.zeros(3, 3, 1, 6),
|
||
'certified': torch.ones(3, 3, 1, dtype=torch.bool), 'gaps': torch.full((3, 3, 2), 0.5)}}
|
||
with tempfile.TemporaryDirectory() as folder:
|
||
trace, output = Path(folder) / 'trace.pt', Path(folder) / 'audit.json'
|
||
torch.save({'kin_fp': 'probe', 'tasks': [{}] * 3, 'batches': [({}, per, 3)]}, trace)
|
||
with patch('ml.eval.diag_collision.Kin', return_value=kin), contextlib.redirect_stdout(io.StringIO()):
|
||
result = replay_certificates(trace, [0, 1, 2], out=output, repeats=1, extra_segments=[(0, 0)])
|
||
with self.assertRaisesRegex(ValueError, 'extra segment'):
|
||
replay_certificates(trace, [1], repeats=1, extra_segments=[(1, 1)])
|
||
self.assertEqual(json.loads(output.read_text())['dense_collision_segments'], 1)
|
||
self.assertEqual([(r['i'], r['start_it']) for r in result['rows']], [(0, 0), (1, 0)])
|
||
self.assertEqual(result['budgets']['32']['safe'], 1)
|
||
self.assertEqual(result['budgets']['32']['collision'], 1)
|
||
self.assertEqual(result['accepted_dense_collision_segments'], 0)
|
||
self.assertTrue(result['batch_single_match'])
|
||
self.assertEqual(result['timing']['calls'], 2)
|
||
|
||
|
||
class DlsSafetyTest(unittest.TestCase):
|
||
def test_rejected_refinement_retains_safe_candidate(self):
|
||
class ObstacleKin:
|
||
def clamp_box(self, q):
|
||
return q
|
||
|
||
def gaps(self, q, cap):
|
||
return {'gm': torch.where(q[:, :1] > 0.3, -torch.ones_like(q[:, :1]), torch.ones_like(q[:, :1]))}
|
||
|
||
task = {'q0': [0] * 6, 'T': [2, 0, 0]}
|
||
traj = torch.zeros(1, 2, 6)
|
||
traj[0, 1, 0] = 0.1
|
||
ref = torch.zeros(1, 1, 6)
|
||
ref[0, 0, 0] = 1.0
|
||
patches = {
|
||
'features': lambda *args: torch.zeros(1, 83),
|
||
'goal_err': lambda kin, q, T, R: (torch.full((q.shape[0],), 2.0), torch.zeros(q.shape[0])),
|
||
'dls_steps': lambda *args, **kwargs: ref,
|
||
}
|
||
results = []
|
||
with patch.multiple('ml.sim.closed_loop', **patches):
|
||
for gate in (0, 1):
|
||
_, per = rollout(None, ObstacleKin(), [task], 'cpu', K=1, safety=True,
|
||
max_sec=0.02, return_traj=True, use_sub=False, select='first',
|
||
oracle=TeacherOracle(traj), replan='empty', commit_n=1,
|
||
safety_partial=1, dls_safety=gate)
|
||
results.append(per)
|
||
self.assertEqual(results[0]['col'].item(), 1.0)
|
||
self.assertEqual(results[0]['dls_it'].item(), 0)
|
||
self.assertEqual(results[1]['col'].item(), 0.0)
|
||
self.assertEqual(results[1]['dls_it'].item(), -1)
|
||
self.assertAlmostEqual(results[1]['traj'][0, 1, 0].item(), 0.1)
|
||
|
||
def test_feedback_is_reduced_only_after_full_rejection(self):
|
||
class ObstacleKin:
|
||
def clamp_box(self, q):
|
||
return q
|
||
|
||
def gaps(self, q, cap):
|
||
return {'gm': torch.where(q[:, :1] > 0.3, -torch.ones_like(q[:, :1]), torch.ones_like(q[:, :1]))}
|
||
|
||
task = {'q0': [0] * 6, 'T': [10, 0, 0]}
|
||
traj = torch.zeros(1, 17, 6)
|
||
traj[0, :, 0] = -0.2 * torch.arange(17)
|
||
ref = torch.zeros(1, 1, 6)
|
||
ref[0, 0, 0] = 1.0
|
||
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,
|
||
}
|
||
results = []
|
||
with patch.multiple('ml.sim.closed_loop', **patches):
|
||
for gain in (1.0, 0.5):
|
||
_, per = rollout(None, ObstacleKin(), [task], 'cpu', K=1, safety=True,
|
||
max_sec=0.04, return_traj=True, use_sub=False, select='first',
|
||
oracle=TeacherOracle(traj), replan='empty', commit_n=1,
|
||
safety_partial=1, fb_gain=0.6, fb_block_gain=gain)
|
||
results.append(per)
|
||
self.assertEqual(results[0]['brakes'].item(), 2.0)
|
||
self.assertEqual(results[1]['brakes'].item(), 1.0)
|
||
self.assertEqual(results[1]['traj'][0, 1, 0].item(), 0.0)
|
||
self.assertAlmostEqual(results[1]['traj'][0, 2, 0].item(), 0.1)
|
||
self.assertEqual(results[1]['col'].item(), 0.0)
|
||
|
||
|
||
class ClampedPathSafetyTest(unittest.TestCase):
|
||
def rollout_candidate(self, increments, safety_partial, goal_error=10.0, **kwargs):
|
||
class ObstacleKin:
|
||
def clamp_box(self, q):
|
||
clamped = q.clone()
|
||
clamped[..., 4] = clamped[..., 4].clamp(max=0.0)
|
||
return clamped
|
||
|
||
def gaps(self, q, cap):
|
||
collision = (q[:, :1] > 0.1) & (q[:, 4:5] < 0.2)
|
||
return {'gm': torch.where(collision, -torch.ones_like(q[:, :1]), torch.ones_like(q[:, :1]))}
|
||
|
||
task = {'q0': [0] * 6, 'T': [goal_error, 0, 0]}
|
||
commit_n = len(increments)
|
||
traj = torch.zeros(1, commit_n + 1, 6)
|
||
traj[0, 1:] = torch.tensor(increments).cumsum(0)
|
||
patches = {
|
||
'features': lambda *args: torch.zeros(1, 83),
|
||
'goal_err': lambda kin, q, T, R: (torch.full((q.shape[0],), goal_error), torch.zeros(q.shape[0])),
|
||
}
|
||
with patch.multiple('ml.sim.closed_loop', **patches):
|
||
_, per = rollout(None, ObstacleKin(), [task], '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,
|
||
safety_partial=safety_partial, **kwargs)
|
||
return per
|
||
|
||
def test_full_safety_rejects_collision_after_joint_clamp(self):
|
||
# Raw endpoint/midpoint are safe, but the J5 upper limit puts the endpoint at (0.2, 0).
|
||
per = self.rollout_candidate([[0.2, 0, 0, 0, 0.6, 0]], safety_partial=0)
|
||
self.assertEqual(per['col'].item(), 0.0)
|
||
torch.testing.assert_close(per['traj'], torch.zeros(1, 2, 6), rtol=0, atol=0)
|
||
|
||
def test_partial_safety_rejects_collision_after_joint_clamp(self):
|
||
per = self.rollout_candidate([[0.2, 0, 0, 0, 0.6, 0]], safety_partial=1)
|
||
self.assertEqual(per['col'].item(), 0.0)
|
||
torch.testing.assert_close(per['traj'], torch.zeros(1, 2, 6), rtol=0, atol=0)
|
||
|
||
def test_partial_safety_keeps_only_clamped_safe_prefix(self):
|
||
per = self.rollout_candidate([
|
||
[0.05, 0, 0, 0, 0.6, 0],
|
||
[0.2, 0, 0, 0, 0.6, 0],
|
||
], safety_partial=1)
|
||
self.assertAlmostEqual(per['traj'][0, -1, 0].item(), 0.05)
|
||
self.assertEqual(per['traj'][0, -1, 4].item(), 0.0)
|
||
self.assertEqual(per['col'].item(), 0.0)
|
||
expected = torch.zeros(1, 3, 6)
|
||
expected[0, 1:, 0] = 0.05
|
||
torch.testing.assert_close(per['traj'], expected)
|
||
torch.testing.assert_close(per['plans']['commit'][0, 0, 1], torch.zeros(6), rtol=0, atol=0)
|
||
|
||
def test_near_dls_rejects_clamped_collision_and_keeps_candidate(self):
|
||
candidate = [[0.05, 0, 0, 0, 0.6, 0]]
|
||
ref = torch.tensor([[[0.2, 0, 0, 0, 0.6, 0]]])
|
||
for safety_partial in (0, 1):
|
||
with self.subTest(safety_partial=safety_partial):
|
||
with patch('ml.sim.closed_loop.dls_steps', return_value=ref) as dls:
|
||
per = self.rollout_candidate(candidate, safety_partial, goal_error=2.0, dls_safety=1)
|
||
dls.assert_called_once()
|
||
self.assertEqual(per['col'].item(), 0.0)
|
||
self.assertEqual(per['dls_it'].item(), -1)
|
||
self.assertEqual(per['brakes'].item(), 0.0)
|
||
expected = torch.zeros(1, 2, 6)
|
||
expected[0, 1, 0] = 0.05
|
||
torch.testing.assert_close(per['traj'], expected)
|
||
torch.testing.assert_close(per['plans']['commit'], torch.tensor(candidate)[None, None])
|
||
self.assertTrue(per['plans']['certified'].all().item())
|
||
|
||
def test_r3_clamped_queue_origin_prefix_and_certificates_match_execution(self):
|
||
class SafeKin:
|
||
def clamp_box(self, q):
|
||
clamped = q.clone()
|
||
clamped[..., 4] = clamped[..., 4].clamp(max=0.0)
|
||
return clamped
|
||
|
||
def gaps(self, q, cap):
|
||
return {'gm': torch.ones_like(q[:, :1])}
|
||
|
||
prefixes = []
|
||
|
||
def capture_features(kin, q, qp, T, R, pre, r, sub, sf):
|
||
prefixes.append((pre.clone(), int(r.item())))
|
||
return torch.zeros(1, 83)
|
||
|
||
teacher = torch.zeros(1, 25, 6)
|
||
teacher[0, :, 0] = 0.02 * torch.arange(25)
|
||
teacher[0, :, 4] = 0.6 * torch.arange(25)
|
||
task = {'q0': [0] * 6, 'T': [10, 0, 0]}
|
||
patches = {
|
||
'features': capture_features,
|
||
'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):
|
||
_, per = rollout(None, SafeKin(), [task], 'cpu', K=1, safety=True,
|
||
max_sec=0.16, return_traj=True, use_sub=False, select='first',
|
||
oracle=TeacherOracle(teacher), replan='r3', commit_n=3, safety_partial=1)
|
||
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())
|
||
|
||
|
||
class FeatureGeometryReuseTest(unittest.TestCase):
|
||
"""#16 第①条：gm_groups 复用调用者已算好的 cum_xf，省一次 FK，但结果必须与原口径逐位一致。"""
|
||
def test_gm_groups_with_supplied_xf_is_bitwise_identical(self):
|
||
from ml.common.features import features, GP, GAP_CAP
|
||
kin = Kin()
|
||
q = torch.tensor([[-129.9, 46.68, 43.49, -97.46, -85.1, -139.7],
|
||
[10.0, -20.0, 30.0, -40.0, 50.0, -60.0]])
|
||
xf = kin.cum_xf(q)
|
||
torch.testing.assert_close(kin.gm_groups(q, GP, GAP_CAP, xf=xf), kin.gm_groups(q, GP, GAP_CAP),
|
||
rtol=0, atol=0)
|
||
idx = [a * 7 + b for a, b in GP]
|
||
table = kin.gaps(q, GAP_CAP, xf)['gm'][:, idx]
|
||
torch.testing.assert_close(kin.gm_groups(q, GP, GAP_CAP), table, rtol=0, atol=0)
|
||
z = torch.zeros(len(q), 6)
|
||
f = features(kin, q, z, q[:, :3], torch.zeros(len(q), 9), z[:, None, :].expand(len(q), 3, 6),
|
||
torch.zeros(len(q)), z, torch.zeros(len(q)))
|
||
cols = 12 + 3 + 6 + 3 + 6 + 3 + 6 + 3 + 3 + 2 + 18 + 3 + 6 + 1 # 间隙块之前的特征数 = 75
|
||
self.assertEqual(cols + 8, 83)
|
||
torch.testing.assert_close(f[:, cols:cols + 8], table / GAP_CAP, rtol=0, atol=0)
|
||
|
||
|
||
class CertBatchEquivalenceTest(unittest.TestCase):
|
||
"""#16 第②条：把 K 条候选的认证并成一次 gaps 调用，选择结果必须与串行循环逐位一致。"""
|
||
class StubModel:
|
||
act_scale = 1.0
|
||
|
||
def __init__(self, act):
|
||
self.act = act
|
||
|
||
def sample_k(self, feat, K, z, steps=1):
|
||
B = feat.shape[0]
|
||
return (self.act.expand(B, K, 16, 6).clone(), torch.zeros(B, device=feat.device),
|
||
torch.zeros(B, 13, device=feat.device))
|
||
|
||
def run_both(self, acts, reject_first):
|
||
class StepKin:
|
||
def clamp_box(self, q):
|
||
return q
|
||
|
||
def gaps(self, q, cap):
|
||
bad = (q[:, 0] > 0.5) if reject_first else torch.zeros(len(q), dtype=torch.bool, device=q.device)
|
||
return {'gm': torch.where(bad[:, None], -torch.ones(len(q), 49, device=q.device),
|
||
torch.full((len(q), 49), 1.0, device=q.device))}
|
||
|
||
acts = torch.tensor(acts, dtype=torch.float32) # [K,16,6] 动作（±1 归一化）
|
||
model = self.StubModel(acts)
|
||
tasks = [{'q0': [0] * 6, 'T': [10, 0, 0]}] * 2 # B=2：批量索引形状必须在多行下也成立
|
||
out = []
|
||
for batched in (0, 1):
|
||
with patch('ml.sim.closed_loop.features', return_value=torch.zeros(len(tasks), 83)), \
|
||
patch('ml.sim.closed_loop.goal_err',
|
||
side_effect=lambda kin, q, T, R: (torch.full((q.shape[0],), 10.0), torch.zeros(q.shape[0]))):
|
||
_, per = rollout(model, StepKin(), tasks, 'cpu', K=acts.shape[0], safety=True, max_sec=0.04,
|
||
return_traj=True, use_sub=False, select='first', replan='empty', commit_n=2,
|
||
safety_partial=1, cert_batch=batched)
|
||
out.append(per)
|
||
return out
|
||
|
||
def test_batched_certification_selects_the_same_candidate(self):
|
||
# 候选 0 第 2 步撞上 q0>0.5 的障碍 ⇒ 只有前缀 1 步；候选 1 全程安全 ⇒ 应被选中
|
||
acts = [[[0.2, 0, 0, 0, 0, 0], [0.5, 0, 0, 0, 0, 0]] + [[0, 0, 0, 0, 0, 0]] * 14,
|
||
[[0.1, 0, 0, 0, 0, 0], [0.1, 0, 0, 0, 0, 0]] + [[0, 0, 0, 0, 0, 0]] * 14]
|
||
seq, batched = self.run_both(acts, reject_first=True)
|
||
torch.testing.assert_close(batched['traj'], seq['traj'], rtol=0, atol=0)
|
||
torch.testing.assert_close(batched['plans']['commit'], seq['plans']['commit'], rtol=0, atol=0)
|
||
torch.testing.assert_close(batched['plans']['certified'], seq['plans']['certified'], rtol=0, atol=0)
|
||
self.assertEqual(batched['brakes'].tolist(), seq['brakes'].tolist())
|
||
self.assertAlmostEqual(seq['traj'][0, -1, 0].item(), 0.24, places=5) # 候选 1 的两步 0.1×STEP=0.12°
|
||
|
||
def test_batched_certification_keeps_partial_prefix_choice(self):
|
||
# 所有候选都只走得了第 1 步 ⇒ 两条路径都取第 1 条候选、前缀长度 1
|
||
acts = [[[0.2, 0, 0, 0, 0, 0], [0.5, 0, 0, 0, 0, 0]] + [[0, 0, 0, 0, 0, 0]] * 14,
|
||
[[0.3, 0, 0, 0, 0, 0], [0.6, 0, 0, 0, 0, 0]] + [[0, 0, 0, 0, 0, 0]] * 14]
|
||
seq, batched = self.run_both(acts, reject_first=True)
|
||
torch.testing.assert_close(batched['traj'], seq['traj'], rtol=0, atol=0)
|
||
torch.testing.assert_close(batched['plans']['commit'], seq['plans']['commit'], rtol=0, atol=0)
|
||
self.assertAlmostEqual(seq['traj'][0, 1, 0].item(), 0.24, places=5)
|
||
self.assertAlmostEqual(seq['traj'][0, 2, 0].item(), 0.24, places=5) # 第 2 步被拒后停在原地
|
||
|
||
|
||
def test_mismatched_xf_row_count_is_rejected(self):
|
||
kin = Kin()
|
||
q1 = torch.zeros(1, 6); q5 = torch.zeros(5, 6)
|
||
xf5 = kin.cum_xf(q5)
|
||
for name, fn in (('gaps', lambda: kin.gaps(q1, 40.0, xf5)),
|
||
('gm_groups', lambda: kin.gm_groups(q1, [(0, 2)], 40.0, xf=xf5))):
|
||
with self.subTest(fn=name):
|
||
with self.assertRaisesRegex(ValueError, '行数'):
|
||
fn()
|
||
self.assertEqual(kin.gaps(q5, 40.0, xf5)['gm'].shape, (5, 49))
|
||
self.assertEqual(kin.gm_groups(q5, [(0, 2)], 40.0, xf=xf5).shape, (5, 1))
|
||
|
||
|
||
if __name__ == '__main__':
|
||
unittest.main()
|