onepilot/openpilot/sunnypilot/modeld_v2/modeld.py

#!/usr/bin/env python3
"""
Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.

This file is part of sunnypilot and is licensed under the MIT License.
See the LICENSE.md file in the root directory for more details.
"""

import os
os.environ['GMMU'] = '0'
from openpilot.common.hardware import COMMA_HARDWARE
os.environ['DEV'] = 'QCOM' if COMMA_HARDWARE else 'CPU'
USBGPU = "USBGPU" in os.environ
if USBGPU:
  os.environ['DEV'] = 'AMD'
  os.environ['AMD_IFACE'] = 'USB'
import pickle
import time
import numpy as np
import openpilot.cereal.messaging as messaging
from openpilot.cereal import log
from opendbc.car.structs import car
from setproctitle import setproctitle
from openpilot.cereal.messaging import PubMaster, SubMaster
from openpilot.cereal.visionipc import VisionStreamType
from msgq.visionipc import VisionIpcClient, VisionBuf
from opendbc.car.car_helpers import get_demo_car_params

from tinygrad.tensor import Tensor
from tinygrad.device import Device

from openpilot.common.file_chunker import open_file_chunked
from openpilot.common.swaglog import cloudlog
from openpilot.common.params import Params
from openpilot.common.filter_simple import FirstOrderFilter
from openpilot.common.realtime import config_realtime_process, DT_MDL
from openpilot.common.transformations.camera import DEVICE_CAMERAS
from openpilot.common.transformations.model import get_warp_matrix
from openpilot.system import sentry
from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
from openpilot.selfdrive.controls.lib.desire_helper import DesireHelper
from openpilot.selfdrive.controls.lib.drive_helpers import get_accel_from_plan, smooth_value

from openpilot.sunnypilot.modeld_v2.fill_model_msg import fill_model_msg, fill_pose_msg, PublishState, get_curvature_from_output
from openpilot.sunnypilot.modeld_v2.constants import Plan
from openpilot.sunnypilot.modeld_v2.meta_helper import load_meta_constants
from openpilot.sunnypilot.modeld_v2.camera_offset_helper import CameraOffsetHelper
from openpilot.sunnypilot.modeld_v2.compile_modeld import derive_frame_skip, make_split_input_queues

from openpilot.sunnypilot.livedelay.helpers import get_lat_delay
from openpilot.sunnypilot.modeld_v2.modeld_base import ModelStateBase
from openpilot.sunnypilot.models.helpers import get_active_bundle
from openpilot.sunnypilot.selfdrive.controls.lib.relc import RoadEdgeLaneChangeController

PROCESS_NAME = "openpilot.selfdrive.modeld.modeld_tinygrad"


def _pkl_exists(path):
  from openpilot.common.file_chunker import get_manifest_path
  return os.path.exists(path) or os.path.exists(get_manifest_path(path))


def _find_driving_pkl(bundle):
  if (override := os.environ.get('COMBINED_MODEL_PKL')) and _pkl_exists(override):
    return override
  if bundle is None or not bundle.models:
    return None
  from openpilot.common.hardware.hw import Paths
  model_root = Paths.model_root()

  pkl_name = bundle.models[0].artifact.fileName
  pkl_path = os.path.join(model_root, pkl_name)
  if _pkl_exists(pkl_path):
    return pkl_path


class FrameMeta:
  frame_id: int = 0
  timestamp_sof: int = 0
  timestamp_eof: int = 0

  def __init__(self, vipc=None):
    if vipc is not None:
      self.frame_id, self.timestamp_sof, self.timestamp_eof = vipc.frame_id, vipc.timestamp_sof, vipc.timestamp_eof


class ModelState(ModelStateBase):
  inputs: dict[str, np.ndarray]
  prev_desire: np.ndarray

  def __init__(self, cam_w: int, cam_h: int):
    ModelStateBase.__init__(self)

    env_pkl = os.environ.get('COMBINED_MODEL_PKL')
    if env_pkl and os.path.exists(env_pkl):
      model_bundle = None
    else:
      model_bundle = get_active_bundle()
    self.generation = model_bundle.generation if model_bundle is not None else None
    overrides = {override.key: override.value for override in model_bundle.overrides} if model_bundle else {}

    self.LAT_SMOOTH_SECONDS = float(overrides.get('lat', ".0"))
    self.LONG_SMOOTH_SECONDS = float(overrides.get('long', ".0"))
    self.MIN_LAT_CONTROL_SPEED = 0.3
    self.PLANPLUS_CONTROL: float = 1.0

    pkl_path = _find_driving_pkl(model_bundle)
    assert pkl_path is not None, "No driving pkl found — all models must be compiled with compile_modeld.py"
    self._init_combined(pkl_path, cam_w, cam_h, model_bundle)

  def _init_combined(self, pkl_path, cam_w, cam_h, bundle):
    cloudlog.warning(f"loading combined pkl: {pkl_path}")
    # TODO-SP: switch to load_oob from openpilot/selfdrive/helpers on next full recompile of all models
    jits = pickle.load(open_file_chunked(pkl_path))

    self.DEV = Device.DEFAULT
    self.WARP_DEV = 'CPU' if USBGPU else self.DEV
    self.QUEUE_DEV = self.DEV

    metadata = jits['metadata']

    self._run_policy = jits[(cam_w, cam_h)]['run_policy']
    self._warp_enqueue = jits[(cam_w, cam_h)]['warp_enqueue']

    # TODO-SP: Remove legacy use_packed detection block after all models are recompiled
    captured = getattr(self._run_policy, 'captured', None)
    if captured is not None:
      use_packed = 'packed_npy_inputs' in getattr(captured, 'expected_names', [])
    else:
      use_packed = True

    if 'model' in metadata:
      model_metadata = metadata['model']
      self.vision_output_slices = model_metadata['output_slices']
      self.policy_output_slices = {}
      self._policy_slices_list = []
      self._combined_model_type = 'supercombo'
      self._vision_input_names = [key for key in model_metadata['input_shapes'] if 'img' in key]
      from openpilot.sunnypilot.modeld_v2.compile_modeld import make_supercombo_input_queues
      frame_skip = derive_frame_skip({}, model_metadata['input_shapes'])
      self.input_queues, self.numpy_inputs = make_supercombo_input_queues(model_metadata['input_shapes'],
                                                                          frame_skip, device=self.QUEUE_DEV, use_packed=use_packed)
    else:
      vision_metadata = metadata['vision']
      policy_keys = [k for k in metadata if k != 'vision']
      if policy_keys == ['policy']:
        self._combined_model_type = 'split'
      else:
        self._combined_model_type = 'multi_policy'
      self.vision_output_slices = vision_metadata['output_slices']
      self._policy_keys = policy_keys
      self._policy_slices_list = [metadata[k]['output_slices'] for k in policy_keys]
      self.policy_output_slices = self._policy_slices_list[0]
      self._has_on_policy = any('on' in k.lower() for k in policy_keys)
      first_policy_metadata = metadata[policy_keys[0]]
      vision_input_shapes = vision_metadata['input_shapes']
      policy_input_shapes = first_policy_metadata['input_shapes']
      self._vision_input_names = [k for k in vision_input_shapes if 'img' in k]
      frame_skip = derive_frame_skip(vision_input_shapes, policy_input_shapes)
      self.input_queues, self.numpy_inputs = make_split_input_queues(vision_input_shapes, policy_input_shapes,
                                                                     frame_skip, device=self.QUEUE_DEV, use_packed=use_packed)

    self._desire_key = next(key for key in self.numpy_inputs if key.startswith('desire'))
    self._road_key = next(key for key in self._vision_input_names if 'big' not in key)
    self._wide_key = next(key for key in self._vision_input_names if 'big' in key)

    is_20hz = bundle.is20hz if bundle else self._combined_model_type in ('split', 'multi_policy')
    if is_20hz:
      from openpilot.sunnypilot.models.split_model_constants import SplitModelConstants
      self.constants = SplitModelConstants()
    else:
      from openpilot.sunnypilot.modeld_v2.constants import ModelConstants
      self.constants = ModelConstants()

    if self._combined_model_type != 'supercombo':
      from openpilot.sunnypilot.modeld_v2.parse_model_outputs_split import Parser as SplitParser
      self.parser = SplitParser()
    else:
      from openpilot.sunnypilot.modeld_v2.parse_model_outputs import Parser as CombinedParser
      self.parser = CombinedParser()

    self.prev_desire = np.zeros(self.constants.DESIRE_LEN, dtype=np.float32)
    self.full_frames: dict = {}
    self._blob_cache: dict = {}
    nv12_info = get_nv12_info(cam_w, cam_h)
    self.frame_buf_params = dict.fromkeys(self._vision_input_names, nv12_info)

    yuv_size = self.frame_buf_params[self._road_key][3]
    self._warp_enqueue(
      **self.input_queues,
      frame=Tensor(np.zeros(yuv_size, dtype=np.uint8), device=self.WARP_DEV).contiguous().realize(),
      big_frame=Tensor(np.zeros(yuv_size, dtype=np.uint8), device=self.WARP_DEV).contiguous().realize())


  @property
  def mlsim(self) -> bool:
    return bool(self.generation is not None and self.generation >= 11)

  @property
  def vision_input_names(self) -> list[str]:
    return self._vision_input_names

  @property
  def desire_key(self) -> str:
    return self._desire_key

  def run(self, bufs: dict[str, VisionBuf], transforms: dict[str, np.ndarray],
                inputs: dict[str, np.ndarray], prepare_only: bool) -> dict[str, np.ndarray] | None:
    for key in bufs.keys():
      ptr = np.frombuffer(bufs[key].data, dtype=np.uint8).ctypes.data
      yuv_size = self.frame_buf_params[key][3]
      cache_key = (key, ptr)
      if cache_key not in self._blob_cache:
        self._blob_cache[cache_key] = Tensor.from_blob(ptr, (yuv_size,), dtype='uint8', device=self.WARP_DEV)
      self.full_frames[key] = self._blob_cache[cache_key]

    desire_key = self.desire_key
    inputs[desire_key][0] = 0
    self.numpy_inputs[desire_key][:] = np.where(inputs[desire_key] - self.prev_desire > .99, inputs[desire_key], 0)
    self.prev_desire[:] = inputs[desire_key]
    for key in ('traffic_convention', 'lateral_control_params', 'action_t'):
      if key in self.numpy_inputs and key in inputs:
        self.numpy_inputs[key][:] = inputs[key]

    road_key = self._road_key
    wide_key = self._wide_key
    self.numpy_inputs['tfm'][:, :] = transforms[road_key].reshape(3, 3)
    self.numpy_inputs['big_tfm'][:, :] = transforms[wide_key].reshape(3, 3)

    if prepare_only:
      self._warp_enqueue(**self.input_queues, frame=self.full_frames[road_key], big_frame=self.full_frames[wide_key])
      return None

    raw_outputs = self._run_policy(**self.input_queues, frame=self.full_frames[road_key], big_frame=self.full_frames[wide_key])

    if self._combined_model_type == 'supercombo':
      model_output = raw_outputs.numpy().flatten()
      sliced = {k: model_output[np.newaxis, v] for k, v in self.vision_output_slices.items()}
      outputs = self.parser.parse_outputs(sliced)
      if 'prev_feat' in self.numpy_inputs:
        self.numpy_inputs['prev_feat'][:] = model_output[self.vision_output_slices['hidden_state']]
    else:
      vision_output = raw_outputs[0].numpy().flatten()
      vision_sliced = {k: vision_output[np.newaxis, v] for k, v in self.vision_output_slices.items()}
      outputs = self.parser.parse_vision_outputs(vision_sliced)

      if 'prev_feat' in self.numpy_inputs and 'hidden_state' in self.vision_output_slices:
        self.numpy_inputs['prev_feat'][:] = vision_output[self.vision_output_slices['hidden_state']]

      for i, policy_slices in enumerate(self._policy_slices_list):
        policy_output = raw_outputs[i + 1].numpy().flatten()
        policy_sliced = {k: policy_output[np.newaxis, v] for k, v in policy_slices.items()}
        parsed = self.parser.parse_policy_outputs(policy_sliced)
        if ('off' in self._policy_keys[i]
          and self._has_on_policy
          and any('plan' in self._policy_slices_list[j] for j, k in enumerate(self._policy_keys) if 'on' in k.lower())):

          parsed.pop('plan', None)

        outputs.update(parsed)

      if 'planplus' in outputs and 'plan' in outputs:
        outputs['plan'] = outputs['plan'] + outputs['planplus']

    if 'desired_curvature' in outputs and 'prev_desired_curv' in self.numpy_inputs:
      buf = self.numpy_inputs['prev_desired_curv']
      buf[0, :-1] = buf[0, 1:]
      buf[0, -1, :] = outputs['desired_curvature'][0, :] if not self.mlsim else 0

    # TODO-SP: This is a hack to prevent GPU corruption by calculating in CPU space, it can be removed on next recompile
    if 'prev_feat' not in self.numpy_inputs and 'feat_q' in self.input_queues:
      feat_val = self.input_queues['feat_q'].numpy()
      self.input_queues['feat_q'].assign(feat_val).realize()

    return outputs

  def get_action_from_model(self, model_output: dict[str, np.ndarray], prev_action: log.ModelDataV2.Action,
                            lat_action_t: float, long_action_t: float, v_ego: float) -> log.ModelDataV2.Action:
    if 'action' not in model_output:
      plan = model_output['plan'][0]
      desired_accel, should_stop = get_accel_from_plan(plan[:, Plan.VELOCITY][:, 0], plan[:, Plan.ACCELERATION][:, 0], self.constants.T_IDXS,
                                                       action_t=long_action_t)

      curvature_plan = (plan + (self.PLANPLUS_CONTROL - 1.0) * model_output['planplus'][0]
                        if 'planplus' in model_output and self.PLANPLUS_CONTROL != 1.0 else plan)
      desired_curvature = get_curvature_from_output(model_output, curvature_plan, v_ego, lat_action_t, self.mlsim)
    else:
      desired_accel = model_output['action'][0, 1]
      desired_curvature = model_output['action'][0, 0] / (max(1.0, v_ego))**2
      should_stop = (v_ego < 0.3 and desired_accel < 0.1)

    desired_accel = smooth_value(desired_accel, prev_action.desiredAcceleration, self.LONG_SMOOTH_SECONDS)

    if self.generation is not None and self.generation >= 10: # smooth curvature for post FOF models
      if v_ego > self.MIN_LAT_CONTROL_SPEED:
        desired_curvature = smooth_value(desired_curvature, prev_action.desiredCurvature, self.LAT_SMOOTH_SECONDS)
      else:
        desired_curvature = prev_action.desiredCurvature

    return log.ModelDataV2.Action(desiredCurvature=float(desired_curvature),desiredAcceleration=float(desired_accel), shouldStop=bool(should_stop))


def main(demo=False):
  cloudlog.warning("modeld init")

  sentry.set_tag("daemon", PROCESS_NAME)
  cloudlog.bind(daemon=PROCESS_NAME)
  setproctitle(PROCESS_NAME)
  config_realtime_process(7, 54)

  # visionipc clients
  while True:
    available_streams = VisionIpcClient.available_streams("camerad", block=False)
    if available_streams:
      use_extra_client = VisionStreamType.VISION_STREAM_WIDE_ROAD in available_streams and VisionStreamType.VISION_STREAM_NARROW_ROAD in available_streams
      main_wide_camera = VisionStreamType.VISION_STREAM_NARROW_ROAD not in available_streams
      break
    time.sleep(.1)

  vipc_client_main_stream = VisionStreamType.VISION_STREAM_WIDE_ROAD if main_wide_camera else VisionStreamType.VISION_STREAM_NARROW_ROAD
  vipc_client_main = VisionIpcClient("camerad", vipc_client_main_stream, True)
  vipc_client_extra = VisionIpcClient("camerad", VisionStreamType.VISION_STREAM_WIDE_ROAD, False)
  cloudlog.warning(f"vision stream set up, main_wide_camera: {main_wide_camera}, use_extra_client: {use_extra_client}")

  while not vipc_client_main.connect(False):
    time.sleep(0.1)
  while use_extra_client and not vipc_client_extra.connect(False):
    time.sleep(0.1)

  cloudlog.warning(f"connected main cam with buffer size: {vipc_client_main.buffer_len} ({vipc_client_main.width} x {vipc_client_main.height})")
  if use_extra_client:
    cloudlog.warning(f"connected extra cam with buffer size: {vipc_client_extra.buffer_len} ({vipc_client_extra.width} x {vipc_client_extra.height})")

  cloudlog.warning("loading model")
  model = ModelState(cam_w=vipc_client_main.width, cam_h=vipc_client_main.height)
  cloudlog.warning("models loaded, modeld starting")

  # messaging
  pm = PubMaster(["modelV2", "drivingModelData", "cameraOdometry", "modelDataV2SP"])
  sm = SubMaster(["deviceState", "carState", "narrowRoadCameraState", "extrinsicsCalibration", "driverMonitoringState", "carControl", "lateralDelay"])

  publish_state = PublishState()
  params = Params()

  # setup filter to track dropped frames
  frame_dropped_filter = FirstOrderFilter(0., 10., 1. / model.constants.MODEL_FREQ)
  frame_id = 0
  last_vipc_frame_id = 0
  run_count = 0

  model_transform_main = np.zeros((3, 3), dtype=np.float32)
  model_transform_extra = np.zeros((3, 3), dtype=np.float32)
  live_calib_seen = False
  buf_main, buf_extra = None, None
  meta_main = FrameMeta()
  meta_extra = FrameMeta()
  camera_offset_helper = CameraOffsetHelper()


  if demo:
    CP = get_demo_car_params()
  else:
    CP = messaging.log_from_bytes(params.get("CarParams", block=True), car.CarParams)
  cloudlog.info("modeld got CarParams: %s", CP.brand)

  # TODO Move smooth seconds to action function
  long_delay = CP.longitudinalActuatorDelay + model.LONG_SMOOTH_SECONDS
  prev_action = log.ModelDataV2.Action()

  DH = DesireHelper()
  meta_constants = load_meta_constants()
  RELC = RoadEdgeLaneChangeController()

  while True:
    # Keep receiving frames until we are at least 1 frame ahead of previous extra frame
    while meta_main.timestamp_sof < meta_extra.timestamp_sof + 25000000:
      buf_main = vipc_client_main.recv()
      meta_main = FrameMeta(vipc_client_main)
      if buf_main is None:
        break

    if buf_main is None:
      cloudlog.debug("vipc_client_main no frame")
      continue

    if use_extra_client:
      # Keep receiving extra frames until frame id matches main camera
      while True:
        buf_extra = vipc_client_extra.recv()
        meta_extra = FrameMeta(vipc_client_extra)
        if buf_extra is None or meta_main.timestamp_sof < meta_extra.timestamp_sof + 25000000:
          break

      if buf_extra is None:
        cloudlog.debug("vipc_client_extra no frame")
        continue

      if abs(meta_main.timestamp_sof - meta_extra.timestamp_sof) > 10000000:
        cloudlog.error(f"frames out of sync! main: {meta_main.frame_id} ({meta_main.timestamp_sof / 1e9:.5f}),\
                       extra: {meta_extra.frame_id} ({meta_extra.timestamp_sof / 1e9:.5f})")

    else:
      # Use single camera
      buf_extra = buf_main
      meta_extra = meta_main

    sm.update(0)
    desire = DH.desire
    is_rhd = sm["driverMonitoringState"].isRHD
    frame_id = sm["narrowRoadCameraState"].frameId
    v_ego = max(sm["carState"].vEgo, 0.)
    if sm.frame % 60 == 0:
      model.lat_delay = get_lat_delay(params, sm["lateralDelay"].lateralDelay)
      model.PLANPLUS_CONTROL = params.get("PlanplusControl", return_default=True)
      camera_offset_helper.set_offset(params.get("CameraOffset", return_default=True))
    lat_delay = model.lat_delay + model.LAT_SMOOTH_SECONDS
    if sm.updated["extrinsicsCalibration"] and sm.seen['narrowRoadCameraState'] and sm.seen['deviceState']:
      device_from_calib_euler = np.array(sm["extrinsicsCalibration"].rpyCalib, dtype=np.float32)
      dc = DEVICE_CAMERAS[(str(sm['deviceState'].deviceType), str(sm['narrowRoadCameraState'].sensor))]
      main_intrinsics = dc.wide_road.intrinsics if main_wide_camera else dc.narrow_road.intrinsics
      model_transform_main = get_warp_matrix(device_from_calib_euler, main_intrinsics, False).astype(np.float32)
      model_transform_extra = get_warp_matrix(device_from_calib_euler, dc.wide_road.intrinsics, True).astype(np.float32)
      model_transform_main, model_transform_extra = camera_offset_helper.update(model_transform_main, model_transform_extra, sm, main_wide_camera)
      live_calib_seen = True

    traffic_convention = np.zeros(2)
    traffic_convention[int(is_rhd)] = 1

    vec_desire = np.zeros(model.constants.DESIRE_LEN, dtype=np.float32)
    if desire >= 0 and desire < model.constants.DESIRE_LEN:
      vec_desire[desire] = 1

    # tracked dropped frames
    vipc_dropped_frames = max(0, meta_main.frame_id - last_vipc_frame_id - 1)
    frames_dropped = frame_dropped_filter.update(min(vipc_dropped_frames, 10))
    if run_count < 10: # let frame drops warm up
      frame_dropped_filter.x = 0.
      frames_dropped = 0.
    run_count = run_count + 1

    frame_drop_ratio = frames_dropped / (1 + frames_dropped)
    prepare_only = vipc_dropped_frames > 0
    if prepare_only:
      cloudlog.error(f"skipping model eval. Dropped {vipc_dropped_frames} frames")

    bufs = {name: buf_extra if 'big' in name else buf_main for name in model.vision_input_names}
    transforms = {name: model_transform_extra if 'big' in name else model_transform_main for name in model.vision_input_names}

    frame_delay = DT_MDL # compensate for time passed since the frame was captured: current_time - timestamp_eof is 50ms on average
    action_delay = DT_MDL / 2 # middle of the interval between model output (current state) and next frame (expected state)
    lat_action_t = lat_delay + frame_delay + action_delay
    long_action_t = long_delay + frame_delay + action_delay

    inputs:dict[str, np.ndarray] = {
      model.desire_key: vec_desire,
      'traffic_convention': traffic_convention,
    }

    if 'lateral_control_params' in model.numpy_inputs:
      inputs['lateral_control_params'] = np.array([v_ego, lat_delay], dtype=np.float32)

    if 'action_t' in model.numpy_inputs:
      inputs['action_t'] = np.array([lat_action_t, long_action_t], dtype=np.float32)

    mt1 = time.perf_counter()
    model_output = model.run(bufs, transforms, inputs, prepare_only)
    mt2 = time.perf_counter()
    model_execution_time = mt2 - mt1

    if model_output is not None:
      modelv2_send = messaging.new_message('modelV2')
      drivingdata_send = messaging.new_message('drivingModelData')
      posenet_send = messaging.new_message('cameraOdometry')
      mdv2sp_send = messaging.new_message('modelDataV2SP')

      action = model.get_action_from_model(model_output, prev_action, lat_action_t, long_action_t, v_ego)
      prev_action = action
      fill_model_msg(drivingdata_send, modelv2_send, model_output, action,
                     publish_state, meta_main.frame_id, meta_extra.frame_id, frame_id,
                     frame_drop_ratio, meta_main.timestamp_eof, model_execution_time, live_calib_seen, meta_constants)

      desire_state = modelv2_send.modelV2.meta.desireState
      l_lane_change_prob = desire_state[log.Desire.laneChangeLeft]
      r_lane_change_prob = desire_state[log.Desire.laneChangeRight]
      lane_change_prob = l_lane_change_prob + r_lane_change_prob
      left_edge, right_edge = RELC.update_and_fill(modelv2_send.modelV2, mdv2sp_send.modelDataV2SP, v_ego)
      DH.update(sm['carState'], sm['carControl'].latActive, lane_change_prob, left_edge, right_edge)
      modelv2_send.modelV2.meta.laneChangeState = DH.lane_change_state
      modelv2_send.modelV2.meta.laneChangeDirection = DH.lane_change_direction
      mdv2sp_send.modelDataV2SP.laneTurnDirection = DH.lane_turn_direction
      drivingdata_send.drivingModelData.meta.laneChangeState = DH.lane_change_state
      drivingdata_send.drivingModelData.meta.laneChangeDirection = DH.lane_change_direction

      fill_pose_msg(posenet_send, model_output, meta_main.frame_id, vipc_dropped_frames, meta_main.timestamp_eof, live_calib_seen)
      pm.send('modelV2', modelv2_send)
      pm.send('drivingModelData', drivingdata_send)
      pm.send('cameraOdometry', posenet_send)
      pm.send('modelDataV2SP', mdv2sp_send)
    last_vipc_frame_id = meta_main.frame_id


if __name__ == "__main__":
  try:
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--demo', action='store_true', help='A boolean for demo mode.')
    args = parser.parse_args()
    main(demo=args.demo)
  except KeyboardInterrupt:
    cloudlog.warning(f"child {PROCESS_NAME} got SIGINT")
  except Exception:
    sentry.capture_exception()
    raise