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Unpin numpy (#29421)

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* Unpin numpy

* Update lock file

* leave acados comment

* Fix warnings

* Fix more paramsd warnings
old-commit-hash: 62a88e504d9ad82f6d070a11a3499bf89c19d08e
This commit is contained in:
Kacper Rączy
2023-08-17 15:36:14 -07:00
committed by GitHub
parent 023cd0492f
commit eb87a31bc5
4 changed files with 17 additions and 17 deletions
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poetry.lock LFS generated
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2
pyproject.toml
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@@ -31,7 +31,7 @@ hexdump = "*"
Jinja2 = "*"
json-rpc = "*"
libusb1 = "*"
numpy = "~1.23" # pinned for acados
numpy = "*"
onnx = ">=1.14.0"
onnxruntime-gpu = { version = ">=1.15.1", platform = "linux", markers = "platform_machine == 'x86_64'" }
pillow = "*"

2
selfdrive/controls/lib/lateral_planner.py
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@@ -132,7 +132,7 @@ class LateralPlanner:
lateralPlan.psis = self.lat_mpc.x_sol[0:CONTROL_N, 2].tolist()
lateralPlan.curvatures = (self.lat_mpc.x_sol[0:CONTROL_N, 3]/self.v_ego).tolist()
lateralPlan.curvatureRates = [float(x/self.v_ego) for x in self.lat_mpc.u_sol[0:CONTROL_N - 1]] + [0.0]
lateralPlan.curvatureRates = [float(x.item() / self.v_ego) for x in self.lat_mpc.u_sol[0:CONTROL_N - 1]] + [0.0]
lateralPlan.mpcSolutionValid = bool(plan_solution_valid)
lateralPlan.solverExecutionTime = self.lat_mpc.solve_time

26
selfdrive/locationd/paramsd.py
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@@ -85,8 +85,8 @@ class ParamsLearner:
# We observe the current stiffness and steer ratio (with a high observation noise) to bound
# the respective estimate STD. Otherwise the STDs keep increasing, causing rapid changes in the
# states in longer routes (especially straight stretches).
stiffness = float(self.kf.x[States.STIFFNESS])
steer_ratio = float(self.kf.x[States.STEER_RATIO])
stiffness = float(self.kf.x[States.STIFFNESS].item())
steer_ratio = float(self.kf.x[States.STEER_RATIO].item())
self.kf.predict_and_observe(t, ObservationKind.STIFFNESS, np.array([[stiffness]]))
self.kf.predict_and_observe(t, ObservationKind.STEER_RATIO, np.array([[steer_ratio]]))
@@ -198,14 +198,14 @@ def main(sm=None, pm=None):
learner = ParamsLearner(CP, CP.steerRatio, 1.0, 0.0)
x = learner.kf.x
angle_offset_average = clip(math.degrees(x[States.ANGLE_OFFSET]),
angle_offset_average = clip(math.degrees(x[States.ANGLE_OFFSET].item()),
angle_offset_average - MAX_ANGLE_OFFSET_DELTA, angle_offset_average + MAX_ANGLE_OFFSET_DELTA)
angle_offset = clip(math.degrees(x[States.ANGLE_OFFSET] + x[States.ANGLE_OFFSET_FAST]),
angle_offset = clip(math.degrees(x[States.ANGLE_OFFSET].item() + x[States.ANGLE_OFFSET_FAST].item()),
angle_offset - MAX_ANGLE_OFFSET_DELTA, angle_offset + MAX_ANGLE_OFFSET_DELTA)
roll = clip(float(x[States.ROAD_ROLL]), roll - ROLL_MAX_DELTA, roll + ROLL_MAX_DELTA)
roll_std = float(P[States.ROAD_ROLL])
roll = clip(float(x[States.ROAD_ROLL].item()), roll - ROLL_MAX_DELTA, roll + ROLL_MAX_DELTA)
roll_std = float(P[States.ROAD_ROLL].item())
# Account for the opposite signs of the yaw rates
sensors_valid = bool(abs(learner.speed * (x[States.YAW_RATE] + learner.yaw_rate)) < LATERAL_ACC_SENSOR_THRESHOLD)
sensors_valid = bool(abs(learner.speed * (x[States.YAW_RATE].item() + learner.yaw_rate)) < LATERAL_ACC_SENSOR_THRESHOLD)
avg_offset_valid = check_valid_with_hysteresis(avg_offset_valid, angle_offset_average, OFFSET_MAX, OFFSET_LOWERED_MAX)
total_offset_valid = check_valid_with_hysteresis(total_offset_valid, angle_offset, OFFSET_MAX, OFFSET_LOWERED_MAX)
roll_valid = check_valid_with_hysteresis(roll_valid, roll, ROLL_MAX, ROLL_LOWERED_MAX)
@@ -215,8 +215,8 @@ def main(sm=None, pm=None):
liveParameters = msg.liveParameters
liveParameters.posenetValid = True
liveParameters.sensorValid = sensors_valid
liveParameters.steerRatio = float(x[States.STEER_RATIO])
liveParameters.stiffnessFactor = float(x[States.STIFFNESS])
liveParameters.steerRatio = float(x[States.STEER_RATIO].item())
liveParameters.stiffnessFactor = float(x[States.STIFFNESS].item())
liveParameters.roll = roll
liveParameters.angleOffsetAverageDeg = angle_offset_average
liveParameters.angleOffsetDeg = angle_offset
@@ -228,10 +228,10 @@ def main(sm=None, pm=None):
0.2 <= liveParameters.stiffnessFactor <= 5.0,
min_sr <= liveParameters.steerRatio <= max_sr,
))
liveParameters.steerRatioStd = float(P[States.STEER_RATIO])
liveParameters.stiffnessFactorStd = float(P[States.STIFFNESS])
liveParameters.angleOffsetAverageStd = float(P[States.ANGLE_OFFSET])
liveParameters.angleOffsetFastStd = float(P[States.ANGLE_OFFSET_FAST])
liveParameters.steerRatioStd = float(P[States.STEER_RATIO].item())
liveParameters.stiffnessFactorStd = float(P[States.STIFFNESS].item())
liveParameters.angleOffsetAverageStd = float(P[States.ANGLE_OFFSET].item())
liveParameters.angleOffsetFastStd = float(P[States.ANGLE_OFFSET_FAST].item())
if DEBUG:
liveParameters.filterState = log.LiveLocationKalman.Measurement.new_message()
liveParameters.filterState.value = x.tolist()