#!/usr/bin/env python3 import os from openpilot.system.hardware import TICI os.environ['GMMU'] = '0' os.environ['DEV'] = 'QCOM' if TICI else 'LLVM' from tinygrad.tensor import Tensor import time import pickle import numpy as np import cereal.messaging as messaging from cereal import car, log from pathlib import Path from setproctitle import setproctitle from cereal.messaging import PubMaster, SubMaster from msgq.visionipc import VisionIpcClient, VisionStreamType, VisionBuf from openpilot.common.swaglog import cloudlog from openpilot.common.params import Params from openpilot.common.filter_simple import FirstOrderFilter from openpilot.common.file_chunker import read_file_chunked 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.camerad.cameras.nv12_info import get_nv12_info from openpilot.system import sentry from opendbc.car.car_helpers import get_demo_car_params from openpilot.selfdrive.controls.lib.desire_helper import DesireHelper from openpilot.selfdrive.controls.lib.drive_helpers import get_accel_from_plan_tomb_raider, smooth_value from openpilot.selfdrive.modeld.camera_offset import CameraOffset, DEFAULT_CAMERA_HEIGHT from openpilot.selfdrive.modeld.parse_model_outputs import Parser from openpilot.selfdrive.modeld.fill_model_msg import fill_model_msg, fill_pose_msg, PublishState, get_curvature_from_output from openpilot.selfdrive.modeld.constants import ModelConstants, Plan from openpilot.selfdrive.modeld.compile_modeld import ( ARTIFACT_FORMAT_VERSION, IMAGE_HISTORY_IN_POLICY, IMAGE_HISTORY_IN_WARP, LEGACY_WARP_INPUTS, _detect_vision_keys, make_split_input_queues, make_supercombo_input_queues, ) from openpilot.selfdrive.modeld.helpers import get_tg_input_devices from openpilot.starpilot.assets.model_manager import ModelManager from openpilot.starpilot.common.model_versions import is_tinygrad_model_version from openpilot.starpilot.common.starpilot_variables import get_starpilot_toggles, MODELS_PATH, params_memory PROCESS_NAME = "selfdrive.modeld.modeld" SEND_RAW_PRED = os.getenv('SEND_RAW_PRED') BUILTIN_MODEL_KEY = "sc2" BUILTIN_MODEL_ALIASES = {BUILTIN_MODEL_KEY, "sc"} LAT_SMOOTH_SECONDS = 0.0 LONG_SMOOTH_SECONDS = 0.3 MIN_LAT_CONTROL_SPEED = 0.3 def _get_param_str(params: Params, key: str, default: str = "") -> str: try: val = params.get(key) except Exception: return default if val is None: return default if isinstance(val, bytes): try: return val.decode("utf-8") except Exception: return default if isinstance(val, (dict, list)): return default return str(val) def _get_default_param_str(params: Params, key: str) -> str: try: val = params.get_default_value(key) except Exception: return "" if val is None: return "" if isinstance(val, bytes): try: return val.decode("utf-8") except Exception: return "" return str(val) def _resolve_mirrored_param(params: Params, primary_key: str, secondary_key: str) -> str: primary_val = _get_param_str(params, primary_key).strip() secondary_val = _get_param_str(params, secondary_key).strip() if primary_val == secondary_val: return secondary_val or primary_val primary_default = _get_default_param_str(params, primary_key).strip() secondary_default = _get_default_param_str(params, secondary_key).strip() primary_non_default = bool(primary_val) and primary_val != primary_default secondary_non_default = bool(secondary_val) and secondary_val != secondary_default if secondary_non_default: return secondary_val if primary_non_default: return primary_val return secondary_val or primary_val def _canonical_model_id(model_id: str) -> str: key = (model_id or "").strip().lower() return BUILTIN_MODEL_KEY if key in BUILTIN_MODEL_ALIASES else key def get_action_from_model(model_output: dict[str, np.ndarray], prev_action: log.ModelDataV2.Action, lat_action_t: float, long_action_t: float, v_ego: float, mlsim: bool, is_v9: bool, is_v14: bool, is_v15: bool, starpilot_toggles) -> log.ModelDataV2.Action: if is_v14 or is_v15: desired_curv_unscaled, desired_accel = model_output['action'][0] if is_v15: desired_curvature = float(desired_curv_unscaled) / max(1.0, v_ego) ** 2 else: desired_curvature = float(desired_curv_unscaled) / 100.0 should_stop = (v_ego < 0.3 and desired_accel < 0.1) desired_accel = smooth_value(float(desired_accel), prev_action.desiredAcceleration, LONG_SMOOTH_SECONDS) if v_ego > MIN_LAT_CONTROL_SPEED: desired_curvature = smooth_value(desired_curvature, prev_action.desiredCurvature, 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)) plan = model_output['plan'][0] if 'planplus' in model_output: recovery_power = getattr(starpilot_toggles, "recovery_power", 1.0) plan = plan + recovery_power * model_output['planplus'][0] cloudlog.error(f"planplus applied: shape {model_output['planplus'].shape}, RECOVERY_POWER {recovery_power}") v_ego_stopping = getattr(starpilot_toggles, "vEgoStopping", 0.3) desired_accel, should_stop = get_accel_from_plan_tomb_raider(plan[:,Plan.VELOCITY][:,0], plan[:,Plan.ACCELERATION][:,0], ModelConstants.T_IDXS, action_t=long_action_t, vEgoStopping=v_ego_stopping) desired_accel = smooth_value(desired_accel, prev_action.desiredAcceleration, LONG_SMOOTH_SECONDS) if is_v9: # V9: use desired_curvature if present; otherwise do NOT fall back to plan if 'desired_curvature' in model_output: desired_curvature = float(model_output['desired_curvature'][0, 0]) else: desired_curvature = prev_action.desiredCurvature else: desired_curvature = get_curvature_from_output(model_output, plan, v_ego, lat_action_t, mlsim=mlsim) if v_ego > MIN_LAT_CONTROL_SPEED: desired_curvature = smooth_value(desired_curvature, prev_action.desiredCurvature, 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)) 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: prev_desire: np.ndarray def _build_policy_inputs(self, input_shapes: dict[str, tuple[int, ...]]) -> tuple[dict[str, np.ndarray], str | None]: numpy_inputs: dict[str, np.ndarray] = {} desire_key = next((key for key in input_shapes if key.startswith("desire")), None) if desire_key is not None: numpy_inputs[desire_key] = np.zeros(input_shapes[desire_key], dtype=np.float32) for key, shape in input_shapes.items(): if key == desire_key or key == "features_buffer" or "img" in key: continue numpy_inputs[key] = np.zeros(shape, dtype=np.float32) prev_desired_curv_key = next( (key for key in ("prev_desired_curv", "prev_desired_curvs") if key in input_shapes), None, ) return numpy_inputs, prev_desired_curv_key def __init__(self, cam_w: int, cam_h: int): params = Params() model_id = _canonical_model_id(_resolve_mirrored_param(params, "Model", "DrivingModel") or BUILTIN_MODEL_KEY) use_builtin = model_id == BUILTIN_MODEL_KEY loaded_builtin = use_builtin if use_builtin: model_path = Path(__file__).parent / "models" / "driving_tinygrad.pkl" else: model_path = MODELS_PATH / f"{model_id}_driving_tinygrad.pkl" if not model_path.is_file() and not use_builtin: cloudlog.error(f"Missing model artifact {model_path}, downloading {model_id}...") try: ModelManager(params, params_memory).download_model(model_id) except Exception: cloudlog.exception(f"Failed to download model {model_id}") if not model_path.is_file() and not use_builtin: fallback_path = Path(__file__).parent / "models" / "driving_tinygrad.pkl" if fallback_path.is_file(): cloudlog.error(f"Falling back to builtin model artifact after {model_id} download failed") model_path = fallback_path loaded_builtin = True if not model_path.is_file(): raise FileNotFoundError(model_path) artifact = pickle.loads(read_file_chunked(str(model_path))) if artifact.get("format_version") != ARTIFACT_FORMAT_VERSION: raise ValueError( f"Unsupported model artifact format {artifact.get('format_version')!r}; " f"expected {ARTIFACT_FORMAT_VERSION}" ) self.model_type = artifact["model_type"] self.metadata = artifact["metadata"] self.policy_order = artifact.get("policy_order", []) self.frame_skip = int(artifact["frame_skip"]) self.image_history_pipeline = artifact.get("image_history_pipeline", IMAGE_HISTORY_IN_WARP) self.warp_input_keys = tuple(artifact.get("warp_input_keys", LEGACY_WARP_INPUTS)) self.policy_input_keys = tuple(artifact["policy_input_keys"]) self.run_policy = artifact["run_policy"] self.warp_enqueue = artifact[(cam_w, cam_h)] self.can_prepare_only = self.image_history_pipeline == IMAGE_HISTORY_IN_WARP if self.model_type == "supercombo": input_shapes = self.metadata["model"]["input_shapes"] self.output_slices = self.metadata["model"]["output_slices"] self.input_queues, self.npy = make_supercombo_input_queues(input_shapes, self.frame_skip, self.QUEUE_DEV) self.policy_input_shapes = input_shapes else: vision_shapes = self.metadata["vision"]["input_shapes"] primary_policy = "on_policy" if "on_policy" in self.policy_order else "policy" self.policy_input_shapes = self.metadata[primary_policy]["input_shapes"] self.input_queues, self.npy = make_split_input_queues( vision_shapes, self.policy_input_shapes, self.frame_skip, self.QUEUE_DEV, ) input_shapes = vision_shapes self.road_key, self.wide_key = _detect_vision_keys(input_shapes) self.vision_input_names = [self.road_key, self.wide_key] self.numpy_inputs, self.prev_desired_curv_key = self._build_policy_inputs(self.policy_input_shapes) self.desire_key = next(key for key in self.numpy_inputs if key.startswith("desire")) self.off_policy_enabled = "off_policy" in self.policy_order self.off_policy_numpy_inputs = dict(self.numpy_inputs) if self.off_policy_enabled else {} self.prev_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32) self.parser = Parser() self.aux_parser = Parser(ignore_missing=True) self.frame_buf_size = get_nv12_info(cam_w, cam_h)[3] self._blob_cache: dict[tuple[str, int], Tensor] = {} model_version = _resolve_mirrored_param(params, "ModelVersion", "DrivingModelVersion") if not model_version: model_version = str(artifact.get("behavior_version") or "") if not model_version: versions_path = MODELS_PATH / ".model_versions.json" if versions_path.is_file(): try: import json model_version = str(json.loads(versions_path.read_text()).get(model_id) or "") except Exception: pass if loaded_builtin and not use_builtin: model_version = str(artifact.get("behavior_version") or "v11") self.policy_generation = model_version or ("v11" if loaded_builtin else "v8") self.is_v9 = self.policy_generation == "v9" self.is_v14 = self.policy_generation == "v14" self.is_v15 = self.policy_generation == "v15" self.mlsim = is_tinygrad_model_version(self.policy_generation) params.put("ModelVersion", self.policy_generation) params.put("DrivingModelVersion", self.policy_generation) if self.prev_desired_curv_key is not None: self.full_prev_desired_curv = np.zeros( (1, ModelConstants.FULL_HISTORY_BUFFER_LEN, ModelConstants.PREV_DESIRED_CURV_LEN), dtype=np.float32, ) self.temporal_idxs = slice( -1 - (ModelConstants.TEMPORAL_SKIP * (ModelConstants.INPUT_HISTORY_BUFFER_LEN - 1)), None, ModelConstants.TEMPORAL_SKIP, ) @property def QUEUE_DEV(self) -> str: if not hasattr(self, "_queue_dev"): devices = get_tg_input_devices(PROCESS_NAME, usbgpu=False) self._warp_dev = devices["WARP_DEV"] self._queue_dev = devices["QUEUE_DEV"] return self._queue_dev @property def WARP_DEV(self) -> str: if not hasattr(self, "_warp_dev"): _ = self.QUEUE_DEV return self._warp_dev @staticmethod def slice_outputs(model_outputs: np.ndarray, output_slices: dict[str, slice]) -> dict[str, np.ndarray]: return {key: model_outputs[np.newaxis, value] for key, value in output_slices.items()} def _set_optional_input(self, name: str, inputs: dict[str, np.ndarray]) -> None: if name not in self.numpy_inputs or name not in inputs: return self.numpy_inputs[name][:] = inputs[name] if name in self.npy: self.npy[name][:] = self.numpy_inputs[name] def _parse_split_outputs(self, outputs: list[np.ndarray]) -> dict[str, np.ndarray]: vision_output, *policy_outputs = outputs parsed = self.parser.parse_vision_outputs( self.slice_outputs(vision_output, self.metadata["vision"]["output_slices"]) ) policy_results: dict[str, dict[str, np.ndarray]] = {} for key, output in zip(self.policy_order, policy_outputs, strict=True): sliced = self.slice_outputs(output, self.metadata[key]["output_slices"]) policy_results[key] = ( self.aux_parser.parse_off_policy_outputs(sliced) if key == "off_policy" else self.parser.parse_policy_outputs(sliced) ) for key in self.policy_order: if key not in ("on_policy", "policy"): parsed.update(policy_results[key]) primary_key = "on_policy" if "on_policy" in policy_results else "policy" parsed.update(policy_results[primary_key]) return parsed 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: frames: dict[str, Tensor] = {} for key, buf in bufs.items(): ptr = np.frombuffer(buf.data, dtype=np.uint8).ctypes.data cache_key = (key, ptr) if cache_key not in self._blob_cache: self._blob_cache[cache_key] = Tensor.from_blob( ptr, (self.frame_buf_size,), dtype="uint8", device=self.WARP_DEV, ) frames[key] = self._blob_cache[cache_key] inputs[self.desire_key][0] = 0 self.numpy_inputs[self.desire_key].fill(0) self.numpy_inputs[self.desire_key].reshape(-1, ModelConstants.DESIRE_LEN)[-1] = inputs[self.desire_key] self.npy["desire"][:] = np.where( inputs[self.desire_key] - self.prev_desire > 0.99, inputs[self.desire_key], 0, ) self.prev_desire[:] = inputs[self.desire_key] for name in self.numpy_inputs: if name not in (self.desire_key, self.prev_desired_curv_key): self._set_optional_input(name, inputs) self.npy["tfm"][:] = transforms[self.road_key] self.npy["big_tfm"][:] = transforms[self.wide_key] warp_output = self.warp_enqueue( **{key: self.input_queues[key] for key in self.warp_input_keys}, frame=frames[self.road_key], big_frame=frames[self.wide_key], ) if self.image_history_pipeline == IMAGE_HISTORY_IN_POLICY: output_tensors = self.run_policy( **{key: self.input_queues[key] for key in self.policy_input_keys}, warped=warp_output, ) else: img, big_img = warp_output if prepare_only: return None output_tensors = self.run_policy( **{key: self.input_queues[key] for key in self.policy_input_keys}, img=img, big_img=big_img, ) outputs = [output.numpy().flatten() for output in output_tensors] if self.model_type == "supercombo": model_output = outputs[0] parsed = self.parser.parse_outputs(self.slice_outputs(model_output, self.output_slices)) if "prev_feat" in self.npy and "hidden_state" in self.output_slices: self.npy["prev_feat"][:] = model_output[self.output_slices["hidden_state"]] else: parsed = self._parse_split_outputs(outputs) if self.prev_desired_curv_key is not None and "desired_curvature" in parsed: self.full_prev_desired_curv[0, :-1] = self.full_prev_desired_curv[0, 1:] self.full_prev_desired_curv[0, -1, :] = parsed["desired_curvature"][0, :] history = self.full_prev_desired_curv[0, self.temporal_idxs] self.numpy_inputs[self.prev_desired_curv_key][:] = 0 * history if self.mlsim else history if self.prev_desired_curv_key in self.npy: self.npy[self.prev_desired_curv_key][:] = self.numpy_inputs[self.prev_desired_curv_key] if SEND_RAW_PRED: parsed["raw_pred"] = np.concatenate([output.copy() for output in outputs]) return parsed 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_ROAD in available_streams main_wide_camera = VisionStreamType.VISION_STREAM_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_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})") start_time = time.monotonic() cloudlog.warning("loading model") model = ModelState(vipc_client_main.width, vipc_client_main.height) cloudlog.warning(f"model loaded in {time.monotonic() - start_time:.1f}s, modeld starting") # messaging pm = PubMaster(["modelV2", "drivingModelData", "cameraOdometry", "starpilotModelV2"]) sm = SubMaster(["deviceState", "carState", "roadCameraState", "liveCalibration", "driverMonitoringState", "carControl", "liveDelay", "starpilotPlan"]) publish_state = PublishState() params = Params() # setup filter to track dropped frames frame_dropped_filter = FirstOrderFilter(0., 10., 1. / ModelConstants.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 = CameraOffset() camera_offset.set_target(params.get_float("CameraOffset", return_default=True)) 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 this needs more thought, use .2s extra for now to estimate other delays # TODO Move smooth seconds to action function long_delay = CP.longitudinalActuatorDelay + LONG_SMOOTH_SECONDS prev_action = log.ModelDataV2.Action() DH = DesireHelper() starpilot_toggles = get_starpilot_toggles(sm) 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["roadCameraState"].frameId v_ego = max(sm["carState"].vEgo, 0.) lat_delay = sm["liveDelay"].lateralDelay + LAT_SMOOTH_SECONDS lateral_control_params = np.array([v_ego, lat_delay], dtype=np.float32) if sm.frame % 60 == 0: camera_offset.set_target(params.get_float("CameraOffset", return_default=True)) if sm.updated["liveCalibration"] and sm.seen['roadCameraState'] and sm.seen['deviceState']: device_from_calib_euler = np.array(sm["liveCalibration"].rpyCalib, dtype=np.float32) dc = DEVICE_CAMERAS[(str(sm['deviceState'].deviceType), str(sm['roadCameraState'].sensor))] model_transform_main = get_warp_matrix(device_from_calib_euler, dc.ecam.intrinsics if main_wide_camera else dc.fcam.intrinsics, False).astype(np.float32) model_transform_extra = get_warp_matrix(device_from_calib_euler, dc.ecam.intrinsics, True).astype(np.float32) camera_height = sm["liveCalibration"].height[0] if sm["liveCalibration"].height else DEFAULT_CAMERA_HEIGHT model_transform_main, model_transform_extra = camera_offset.update( model_transform_main, model_transform_extra, str(sm["deviceState"].deviceType), str(sm["roadCameraState"].sensor), camera_height, main_wide_camera, ) live_calib_seen = True traffic_convention = np.zeros(2) traffic_convention[int(is_rhd)] = 1 vec_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32) if desire >= 0 and desire < ModelConstants.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 = model.can_prepare_only and vipc_dropped_frames > 0 if prepare_only: cloudlog.error(f"skipping model eval. Dropped {vipc_dropped_frames} frames") bufs = { model.road_key: buf_main, model.wide_key: buf_extra, } transforms = { model.road_key: model_transform_main, model.wide_key: model_transform_extra, } frame_delay = DT_MDL # Average time elapsed since the current frame finished exposing. action_delay = DT_MDL / 2 # Target the midpoint between current output and the next model step. 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 'action_t' in model.numpy_inputs or (model.off_policy_enabled and 'action_t' in model.off_policy_numpy_inputs): inputs['action_t'] = np.array([lat_action_t, long_action_t], dtype=np.float32) if 'prev_action' in model.numpy_inputs or (model.off_policy_enabled and 'prev_action' in model.off_policy_numpy_inputs): inputs['prev_action'] = np.array([ prev_action.desiredCurvature * max(1.0, v_ego) ** 2, prev_action.desiredAcceleration, ], dtype=np.float32) # Include optional inputs only if the loaded model expects them if 'lateral_control_params' in model.numpy_inputs: inputs['lateral_control_params'] = lateral_control_params 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') starpilot_modelv2_send = messaging.new_message('starpilotModelV2') drivingdata_send = messaging.new_message('drivingModelData') posenet_send = messaging.new_message('cameraOdometry') action = get_action_from_model( model_output, prev_action, lat_action_t, long_action_t, v_ego, model.mlsim, model.is_v9, model.is_v14, model.is_v15, starpilot_toggles, ) 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) 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 DH.update(sm['carState'], sm['carControl'].latActive, lane_change_prob, sm['starpilotPlan'], starpilot_toggles, sm['carControl'].enabled) modelv2_send.modelV2.meta.laneChangeState = DH.lane_change_state modelv2_send.modelV2.meta.laneChangeDirection = DH.lane_change_direction starpilot_modelv2_send.starpilotModelV2.turnDirection = DH.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('starpilotModelV2', starpilot_modelv2_send) pm.send('drivingModelData', drivingdata_send) pm.send('cameraOdometry', posenet_send) last_vipc_frame_id = meta_main.frame_id # Update planner-driven parameters if sm.updated['starpilotPlan']: starpilot_toggles = get_starpilot_toggles(sm) 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