48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
"""
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Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
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"""
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from __future__ import annotations
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import numpy as np
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from iqpilot.selfdrive.iqmodeld.tools.compile_supercombo import (
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_captured_devices,
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_validate_pose_outputs,
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)
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from iqpilot.selfdrive.iqmodeld.models.runners.tinygrad.supercombo_runner import _captured_queue_depth
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class _Captured:
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def __init__(self, expected_input_info):
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self.expected_input_info = expected_input_info
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class _FakeJit:
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def __init__(self, expected_input_info):
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self.captured = _Captured(expected_input_info)
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def test_captured_queue_helpers_extract_depth_and_device():
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infos = [
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("noop", (), "uchar", "QCOM"),
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("reshape(arg=None, src=(noop, stack(arg=None, src=(const(arg=5), const(arg=6), const(arg=128), const(arg=256)))))", (), "uchar", "QCOM"),
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("reshape(arg=None, src=(noop, const(arg=3)))", (), "float", "NPY"),
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]
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fake_jit = _FakeJit(infos)
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assert _captured_queue_depth(fake_jit) == 5
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assert _captured_devices(fake_jit) == {"QCOM", "NPY"}
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def test_validate_pose_outputs_accepts_sane_odometry_payload():
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outputs = {
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"pose": np.array([[1.0, 0.5, 0.25, 0.1, 0.2, 0.3]], dtype=np.float32),
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"pose_stds": np.array([[0.5, 0.4, 0.3, 0.2, 0.2, 0.2]], dtype=np.float32),
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"wide_from_device_euler": np.array([[0.1, 0.2, 0.3]], dtype=np.float32),
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"wide_from_device_euler_stds": np.array([[0.2, 0.2, 0.2]], dtype=np.float32),
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"road_transform": np.array([[0.5, 0.4, 0.3, 0.2, 0.1, 0.0]], dtype=np.float32),
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"road_transform_stds": np.array([[0.3, 0.3, 0.3, 0.2, 0.2, 0.2]], dtype=np.float32),
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}
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_validate_pose_outputs(outputs)
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