def test_symmetry(self) -> None:
attrs = {
"simulated_ego_states": torch.ones(20, 7),
"recorded_ego_states": torch.full((20, 7), 2.0),
}
sim_output = mock.Mock(**attrs)
metric = metrics.DistanceToRefTrajectoryMetric()
result = metric.compute(sim_output)
attrs = {
"simulated_ego_states": torch.full((20, 7), 2.0),
"recorded_ego_states": torch.ones(20, 7),
}
sim_output = mock.Mock(**attrs)
metric = metrics.DistanceToRefTrajectoryMetric()
result_switch = metric.compute(sim_output)
self.assertEqual(result.sum(), result_switch.sum())
def build_metrics(self) -> List[metrics.SupportsMetricCompute]:
"""Returns a list of metrics that will be computed.
"""
return [
metrics.DisplacementErrorL2Metric(),
metrics.DistanceToRefTrajectoryMetric(),
metrics.CollisionFrontMetric(),
metrics.CollisionRearMetric(),
metrics.CollisionSideMetric()
]
def test_more_simulation_than_observation(self) -> None:
timesteps = 20
attrs = {
"simulated_ego_states": torch.ones(timesteps + 20, 7),
"recorded_ego_states": torch.ones(timesteps, 7),
}
sim_output = mock.Mock(**attrs)
metric = metrics.DistanceToRefTrajectoryMetric()
with self.assertRaisesRegex(ValueError, "More simulated timesteps than observed"):
_ = metric.compute(sim_output)
def test_half_trajectories(self) -> None:
observed_trajectory = torch.ones(40, 7)
observed_trajectory[20:, :] += 1.0
attrs = {
"simulated_ego_states": torch.ones(40, 7),
"recorded_ego_states": observed_trajectory,
}
sim_output = mock.Mock(**attrs)
metric = metrics.DistanceToRefTrajectoryMetric()
result = metric.compute(sim_output)
self.assertEqual(len(torch.unique(result)), 1)
def test_larger_observed_ego(self) -> None:
simulated_steps = 20
attrs = {
"simulated_ego_states": torch.ones(simulated_steps, 7),
"recorded_ego_states": torch.ones(50, 7),
}
sim_output = mock.Mock(**attrs)
metric = metrics.DistanceToRefTrajectoryMetric()
result = metric.compute(sim_output)
self.assertEqual(result.sum(), 0.)
# Default fraction should be 80% of the samples
simulated_steps_fraction = int(simulated_steps * 0.8)
self.assertEqual(len(result), simulated_steps_fraction)
def test_different_fraction(self) -> None:
simulated_steps = 20
scene_fraction = 0.5
attrs = {
"simulated_ego_states": torch.ones(simulated_steps, 7),
"recorded_ego_states": torch.ones(simulated_steps, 7),
}
sim_output = mock.Mock(**attrs)
metric = metrics.DistanceToRefTrajectoryMetric(scene_fraction)
result = metric.compute(sim_output)
simulated_steps_fraction = int(simulated_steps * scene_fraction)
self.assertEqual(len(result), simulated_steps_fraction)
self.assertEqual(result.sum(), 0.)
def test_parallel_trajectory(self) -> None:
simulated_steps = 20
attrs = {
"simulated_ego_states": torch.ones(simulated_steps, 7),
"recorded_ego_states": torch.full((20, 7), 2.0),
}
sim_output = mock.Mock(**attrs)
metric = metrics.DistanceToRefTrajectoryMetric()
result = metric.compute(sim_output)
# Default fraction should be 80% of the samples
simulated_steps_fraction = int(simulated_steps * 0.8)
self.assertEqual(len(result), simulated_steps_fraction)
self.assertEqual(len(torch.unique(result)), 1)
self.assertAlmostEqual(torch.unique(result).item(), 1.4142, 4)
