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117 lines
4.1 KiB
117 lines
4.1 KiB
#!/usr/bin/env python3
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import gc
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import math
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import time
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import ctypes
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import numpy as np
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from pathlib import Path
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from cereal import messaging
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from cereal.messaging import PubMaster, SubMaster
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from cereal.visionipc import VisionIpcClient, VisionStreamType
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from openpilot.common.swaglog import cloudlog
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from openpilot.common.params import Params
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from openpilot.common.realtime import set_realtime_priority
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from openpilot.selfdrive.modeld.constants import ModelConstants
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from openpilot.selfdrive.modeld.runners import ModelRunner, Runtime
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NAV_INPUT_SIZE = 256*256
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NAV_FEATURE_LEN = 256
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NAV_DESIRE_LEN = 32
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NAV_OUTPUT_SIZE = 2*2*ModelConstants.IDX_N + NAV_DESIRE_LEN + NAV_FEATURE_LEN
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MODEL_PATHS = {
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ModelRunner.SNPE: Path(__file__).parent / 'models/navmodel_q.dlc',
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ModelRunner.ONNX: Path(__file__).parent / 'models/navmodel.onnx'}
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class NavModelOutputXY(ctypes.Structure):
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_fields_ = [
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("x", ctypes.c_float),
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("y", ctypes.c_float)]
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class NavModelOutputPlan(ctypes.Structure):
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_fields_ = [
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("mean", NavModelOutputXY*ModelConstants.IDX_N),
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("std", NavModelOutputXY*ModelConstants.IDX_N)]
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class NavModelResult(ctypes.Structure):
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_fields_ = [
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("plan", NavModelOutputPlan),
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("desire_pred", ctypes.c_float*NAV_DESIRE_LEN),
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("features", ctypes.c_float*NAV_FEATURE_LEN)]
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class ModelState:
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inputs: dict[str, np.ndarray]
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output: np.ndarray
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model: ModelRunner
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def __init__(self):
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assert ctypes.sizeof(NavModelResult) == NAV_OUTPUT_SIZE * ctypes.sizeof(ctypes.c_float)
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self.output = np.zeros(NAV_OUTPUT_SIZE, dtype=np.float32)
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self.inputs = {'input_img': np.zeros(NAV_INPUT_SIZE, dtype=np.uint8)}
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self.model = ModelRunner(MODEL_PATHS, self.output, Runtime.DSP, True, None)
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self.model.addInput("input_img", None)
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def run(self, buf:np.ndarray) -> tuple[np.ndarray, float]:
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self.inputs['input_img'][:] = buf
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t1 = time.perf_counter()
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self.model.setInputBuffer("input_img", self.inputs['input_img'].view(np.float32))
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self.model.execute()
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t2 = time.perf_counter()
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return self.output, t2 - t1
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def get_navmodel_packet(model_output: np.ndarray, valid: bool, frame_id: int, location_ts: int, execution_time: float, dsp_execution_time: float):
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model_result = ctypes.cast(model_output.ctypes.data, ctypes.POINTER(NavModelResult)).contents
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msg = messaging.new_message('navModel')
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msg.valid = valid
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msg.navModel.frameId = frame_id
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msg.navModel.locationMonoTime = location_ts
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msg.navModel.modelExecutionTime = execution_time
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msg.navModel.dspExecutionTime = dsp_execution_time
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msg.navModel.features = model_result.features[:]
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msg.navModel.desirePrediction = model_result.desire_pred[:]
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msg.navModel.position.x = [p.x for p in model_result.plan.mean]
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msg.navModel.position.y = [p.y for p in model_result.plan.mean]
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msg.navModel.position.xStd = [math.exp(p.x) for p in model_result.plan.std]
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msg.navModel.position.yStd = [math.exp(p.y) for p in model_result.plan.std]
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return msg
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def main():
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gc.disable()
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set_realtime_priority(1)
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# there exists a race condition when two processes try to create a
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# SNPE model runner at the same time, wait for dmonitoringmodeld to finish
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cloudlog.warning("waiting for dmonitoringmodeld to initialize")
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if not Params().get_bool("DmModelInitialized", True):
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return
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model = ModelState()
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cloudlog.warning("models loaded, navmodeld starting")
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vipc_client = VisionIpcClient("navd", VisionStreamType.VISION_STREAM_MAP, True)
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while not vipc_client.connect(False):
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time.sleep(0.1)
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assert vipc_client.is_connected()
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cloudlog.warning(f"connected with buffer size: {vipc_client.buffer_len}")
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sm = SubMaster(["navInstruction"])
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pm = PubMaster(["navModel"])
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while True:
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buf = vipc_client.recv()
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if buf is None:
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continue
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sm.update(0)
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t1 = time.perf_counter()
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model_output, dsp_execution_time = model.run(buf.data[:buf.uv_offset])
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t2 = time.perf_counter()
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valid = vipc_client.valid and sm.valid["navInstruction"]
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pm.send("navModel", get_navmodel_packet(model_output, valid, vipc_client.frame_id, vipc_client.timestamp_sof, t2 - t1, dsp_execution_time))
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if __name__ == "__main__":
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main()
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