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firestar5683
88 lines
5.4 KiB
Python
88 lines
5.4 KiB
Python
"""Actual registered controller/CAN/child fit across exact changing native tunes.
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Independent synthetic plant only; no physical vehicle acceptance is claimed.
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"""
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from dataclasses import replace
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from openpilot.starpilot.flm.adaptive_profile import readmit_profile
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from openpilot.starpilot.flm.learning_wire import result_message, read_runtime_admission
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from openpilot.starpilot.flm.learner_factory import fit_snapshot, decode_fit_result
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from openpilot.starpilot.flm.runtime_adapter_registry import baseline_values
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from openpilot.starpilot.flm.tests.test_actual_raw_worker import raw_job, BASE, CONTEXT, CAR, interfaces
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from openpilot.starpilot.flm.tests.test_registered_controls_global import controls_for, frame
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from openpilot.starpilot.flm.tests.test_registered_accepted_profile import accepted_frame
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from openpilot.cereal import messaging
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def test_raw_native_parameter_changes_preserve_fit_and_require_current_exact_probe():
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cp = interfaces[CAR.TOYOTA_RAV4_TSS2].get_non_essential_params(CAR.TOYOTA_RAV4_TSS2)
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with controls_for(cp, 'standard', True) as controls, controls_for(cp, 'standard', True) as reference:
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for tick in range(30):
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frame(controls, BASE - 300_000_000 + tick * 10_000_000, tick)
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frame(reference, BASE - 300_000_000 + tick * 10_000_000, tick)
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runtime = controls.global_learning
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selected_revision = runtime.baseline_revision
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snapshot, adapter, control, output, costs = raw_job(controls.CP, runtime, controls.params, native_refinements=True)
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tuples = {point.torque_values for point in adapter.replay.records}
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assert len(tuples) >= 32
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assert runtime.baseline_revision == selected_revision
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# Real reconstruction/physical fitting/heldout replay; no supplied profile,
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# coefficients, fake inverse, or fabricated validation result.
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report = fit_snapshot(snapshot)
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decoded = decode_fit_result(report)
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assert decoded is not None and decoded[0].profile is not None, report
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result, validation = decoded
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assert any(result.profile.coefficients), report
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assert adapter.install_fit(report, result, validation, physical_context=CONTEXT)
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adapter.cohort(control, costs['last_physical'])
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current = adapter.current_admission(result.profile, control, output, CONTEXT)
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assert current.healthy
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assert current.native_baseline_values == control.baseline_values
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renewed = readmit_profile(result.profile, validation, current,
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now_ns=output.apply_ns + 2_000_000, sequence=result.profile.sequence + 1)
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assert renewed is not None
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profile, fresh_validation = renewed
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assert fresh_validation.native_baseline_values == control.baseline_values
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packet = result_message(replace(result, profile=profile), 'native-tuple-raw-test', profile.sequence,
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profile.observed_ns, replay_validation=fresh_validation)
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decoded_profile, decoded_validation = read_runtime_admission(messaging.log_from_bytes(packet.to_bytes()))
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assert decoded_validation == fresh_validation and decoded_profile == profile
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assert baseline_values(controls.LaC) == runtime.initial_values
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# Install the exact same healthy native stream in both actual Controls
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# instances. Only one gets the real child/current-probe-qualified FF fit.
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for target in (controls, reference):
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for stamp in (BASE - 500_000_000, BASE - 250_000_000, BASE):
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event = messaging.new_message('lateralTorqueParameters', valid=True, logMonoTime=stamp)
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row = event.lateralTorqueParameters
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row.version, row.valid, row.useParams = 1, True, True
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row.latAccelFactorFiltered, row.latAccelOffsetFiltered, row.frictionCoefficientFiltered = control.baseline_values[1:4]
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target.sm.update_msgs(stamp / 1e9, [event.as_reader()])
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target._global_fixture_source_ns = BASE - 250_000_000
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effect = False
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applied_result = replace(result, profile=profile)
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for tick in range(20):
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now = BASE + tick * 10_000_000
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ordinary, _ = frame(reference, now, tick + 30)
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actual, _ = accepted_frame(controls, now, tick + 30, applied_result, fresh_validation)
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assert runtime.baseline_revision == selected_revision
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assert runtime.reason == 'qualified' and runtime.used
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assert runtime.outcome is not None and runtime.outcome.status == 'applied'
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assert actual.latActive == ordinary.latActive and actual.longActive == ordinary.longActive
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assert baseline_values(controls.LaC) == control.baseline_values
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effect |= actual.actuators.torque != ordinary.actuators.torque
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assert effect
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identity = runtime.identity
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event = messaging.new_message('lateralTorqueParameters', valid=True, logMonoTime=BASE + 250_000_000)
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row = event.lateralTorqueParameters
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row.version, row.valid, row.useParams = 1, True, True
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row.latAccelFactorFiltered = control.baseline_values[1] * 1.001
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row.latAccelOffsetFiltered = control.baseline_values[2]
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row.frictionCoefficientFiltered = control.baseline_values[3]
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controls.sm.update_msgs((BASE + 250_000_000) / 1e9, [event.as_reader()])
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command, _ = accepted_frame(controls, BASE + 250_000_000, 55, applied_result, fresh_validation)
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assert command.latActive
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assert runtime.identity == identity and runtime.baseline_revision == selected_revision
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assert runtime.applied_coordinates == (0., 0.)
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assert runtime.outcome is None or runtime.outcome.status != 'applied'
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packet.autoSteeringLearning.learning.runtimeProfile.replay.nativeBaselineValues = [float('nan')]
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assert read_runtime_admission(packet.as_reader()) is None
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