sunnypilot v2026.002.000
version: sunnypilot v2026.002.000 (staging) date: 2026-07-21T07:44:02 master commit: 2c334ede443d7391d27575af8c854a095ba702a8
This commit is contained in:
github-actions[bot] committed
commit
de77c89cf6
3995 files changed
+1260688
No files matched your search
+156
@@ -0,0 +1,156 @@
|
||||
#!/usr/bin/env python3
|
||||
import os
|
||||
from openpilot.selfdrive.modeld.helpers import MODELS_DIR, get_tg_input_devices
|
||||
from tinygrad.tensor import Tensor
|
||||
import time
|
||||
import pickle
|
||||
import numpy as np
|
||||
|
||||
from openpilot.cereal import messaging
|
||||
from openpilot.cereal.messaging import PubMaster, SubMaster
|
||||
from msgq.visionipc import VisionIpcClient, VisionStreamType, VisionBuf
|
||||
from openpilot.common.swaglog import cloudlog
|
||||
from openpilot.common.realtime import config_realtime_process
|
||||
from openpilot.common.transformations.model import dmonitoringmodel_intrinsics
|
||||
from openpilot.common.transformations.camera import _ar_ox_fisheye, _os_fisheye
|
||||
from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
|
||||
from openpilot.common.file_chunker import open_file_chunked
|
||||
from openpilot.selfdrive.modeld.parse_model_outputs import sigmoid, safe_exp
|
||||
|
||||
PROCESS_NAME = "openpilot.selfdrive.modeld.dmonitoringmodeld"
|
||||
SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
|
||||
MODEL_PKL_PATH = MODELS_DIR / 'dmonitoring_model_tinygrad.pkl'
|
||||
METADATA_PATH = MODELS_DIR / 'dmonitoring_model_metadata.pkl'
|
||||
|
||||
|
||||
class ModelState:
|
||||
inputs: dict[str, np.ndarray]
|
||||
output: np.ndarray
|
||||
|
||||
def __init__(self, cam_w: int, cam_h: int):
|
||||
self.DEV = get_tg_input_devices(PROCESS_NAME, usbgpu=False)['DEV']
|
||||
with open(METADATA_PATH, 'rb') as f:
|
||||
model_metadata = pickle.load(f)
|
||||
self.input_shapes = model_metadata['input_shapes']
|
||||
self.output_slices = model_metadata['output_slices']
|
||||
|
||||
self.numpy_inputs = {
|
||||
'calib': np.zeros(self.input_shapes['calib'], dtype=np.float32),
|
||||
}
|
||||
|
||||
self.warp_inputs_np = {'transform': np.zeros((3,3), dtype=np.float32)}
|
||||
self.warp_inputs = {k: Tensor(v, device='NPY') for k,v in self.warp_inputs_np.items()}
|
||||
self.frame_buf_params = get_nv12_info(cam_w, cam_h)
|
||||
self.tensor_inputs = {k: Tensor(v, device='NPY').realize() for k,v in self.numpy_inputs.items()}
|
||||
self._blob_cache : dict[int, Tensor] = {}
|
||||
self.model_run = pickle.load(open_file_chunked(str(MODEL_PKL_PATH)))
|
||||
with open(MODELS_DIR / f'dm_warp_{cam_w}x{cam_h}_tinygrad.pkl', "rb") as f:
|
||||
self.image_warp = pickle.load(f)
|
||||
|
||||
def run(self, buf: VisionBuf, calib: np.ndarray, transform: np.ndarray) -> tuple[np.ndarray, float]:
|
||||
self.numpy_inputs['calib'][0,:] = calib
|
||||
|
||||
t1 = time.perf_counter()
|
||||
|
||||
ptr = np.frombuffer(buf.data, dtype=np.uint8).ctypes.data
|
||||
# There is a ringbuffer of imgs, just cache tensors pointing to all of them
|
||||
if ptr not in self._blob_cache:
|
||||
self._blob_cache[ptr] = Tensor.from_blob(ptr, (self.frame_buf_params[3],), dtype='uint8', device=self.DEV)
|
||||
|
||||
self.warp_inputs_np['transform'][:] = transform[:]
|
||||
self.tensor_inputs['input_img'] = self.image_warp(self._blob_cache[ptr], self.warp_inputs['transform'])
|
||||
|
||||
output = self.model_run(**self.tensor_inputs).numpy().flatten()
|
||||
|
||||
t2 = time.perf_counter()
|
||||
return output, t2 - t1
|
||||
|
||||
def slice_outputs(model_outputs, output_slices):
|
||||
return {k: model_outputs[np.newaxis, v] for k,v in output_slices.items()}
|
||||
|
||||
def parse_model_output(model_output):
|
||||
parsed = {}
|
||||
parsed['wheel_on_right'] = sigmoid(model_output['wheel_on_right'])
|
||||
for ds_suffix in ['lhd', 'rhd']:
|
||||
face_descs = model_output[f'face_descs_{ds_suffix}']
|
||||
parsed[f'face_descs_{ds_suffix}'] = face_descs[:, :-6]
|
||||
parsed[f'face_descs_{ds_suffix}_std'] = safe_exp(face_descs[:, -6:])
|
||||
for key in ['face_prob', 'left_eye_prob', 'right_eye_prob','left_blink_prob', 'right_blink_prob', 'sunglasses_prob', 'using_phone_prob', 'sleep_prob']:
|
||||
parsed[f'{key}_{ds_suffix}'] = sigmoid(model_output[f'{key}_{ds_suffix}'])
|
||||
return parsed
|
||||
|
||||
def fill_driver_data(msg, model_output, ds_suffix):
|
||||
msg.faceOrientation = model_output[f'face_descs_{ds_suffix}'][0, :3].tolist()
|
||||
msg.faceOrientationStd = model_output[f'face_descs_{ds_suffix}_std'][0, :3].tolist()
|
||||
msg.facePosition = model_output[f'face_descs_{ds_suffix}'][0, 3:5].tolist()
|
||||
msg.facePositionStd = model_output[f'face_descs_{ds_suffix}_std'][0, 3:5].tolist()
|
||||
msg.faceProb = model_output[f'face_prob_{ds_suffix}'][0, 0].item()
|
||||
msg.leftEyeProb = model_output[f'left_eye_prob_{ds_suffix}'][0, 0].item()
|
||||
msg.rightEyeProb = model_output[f'right_eye_prob_{ds_suffix}'][0, 0].item()
|
||||
msg.leftBlinkProb = model_output[f'left_blink_prob_{ds_suffix}'][0, 0].item()
|
||||
msg.rightBlinkProb = model_output[f'right_blink_prob_{ds_suffix}'][0, 0].item()
|
||||
msg.sunglassesProb = model_output[f'sunglasses_prob_{ds_suffix}'][0, 0].item()
|
||||
msg.phoneProb = model_output[f'using_phone_prob_{ds_suffix}'][0, 0].item()
|
||||
msg.sleepProb = model_output[f'sleep_prob_{ds_suffix}'][0, 0].item()
|
||||
|
||||
def get_driverstate_packet(model_output, frame_id: int, location_ts: int, exec_time: float, gpu_exec_time: float):
|
||||
msg = messaging.new_message('driverStateV2', valid=True)
|
||||
ds = msg.driverStateV2
|
||||
ds.frameId = frame_id
|
||||
ds.modelExecutionTime = exec_time
|
||||
ds.gpuExecutionTime = gpu_exec_time
|
||||
ds.rawPredictions = model_output['raw_pred']
|
||||
ds.wheelOnRightProb = model_output['wheel_on_right'][0, 0].item()
|
||||
fill_driver_data(ds.leftDriverData, model_output, 'lhd')
|
||||
fill_driver_data(ds.rightDriverData, model_output, 'rhd')
|
||||
return msg
|
||||
|
||||
|
||||
def main():
|
||||
config_realtime_process(7, 5)
|
||||
|
||||
cloudlog.warning("connecting to driver stream")
|
||||
vipc_client = VisionIpcClient("camerad", VisionStreamType.VISION_STREAM_DRIVER, True)
|
||||
while not vipc_client.connect(False):
|
||||
time.sleep(0.1)
|
||||
assert vipc_client.is_connected()
|
||||
cloudlog.warning(f"connected with buffer size: {vipc_client.buffer_len}")
|
||||
|
||||
model = ModelState(vipc_client.width, vipc_client.height)
|
||||
cloudlog.warning("models loaded, dmonitoringmodeld starting")
|
||||
|
||||
sm = SubMaster(["liveCalibration"])
|
||||
pm = PubMaster(["driverStateV2"])
|
||||
|
||||
calib = np.zeros(model.numpy_inputs['calib'].size, dtype=np.float32)
|
||||
model_transform = None
|
||||
|
||||
while True:
|
||||
buf = vipc_client.recv()
|
||||
if buf is None:
|
||||
continue
|
||||
|
||||
if model_transform is None:
|
||||
cam = _os_fisheye if buf.width == _os_fisheye.width else _ar_ox_fisheye
|
||||
model_transform = np.linalg.inv(np.dot(dmonitoringmodel_intrinsics, np.linalg.inv(cam.intrinsics))).astype(np.float32)
|
||||
|
||||
sm.update(0)
|
||||
if sm.updated["liveCalibration"]:
|
||||
calib[:] = np.array(sm["liveCalibration"].rpyCalib)
|
||||
|
||||
t1 = time.perf_counter()
|
||||
model_output, gpu_execution_time = model.run(buf, calib, model_transform)
|
||||
t2 = time.perf_counter()
|
||||
raw_pred = model_output.tobytes() if SEND_RAW_PRED else b''
|
||||
model_output = slice_outputs(model_output, model.output_slices)
|
||||
model_output = parse_model_output(model_output)
|
||||
model_output['raw_pred'] = raw_pred
|
||||
msg = get_driverstate_packet(model_output, vipc_client.frame_id, vipc_client.timestamp_sof, t2 - t1, gpu_execution_time)
|
||||
pm.send("driverStateV2", msg)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
main()
|
||||
except KeyboardInterrupt:
|
||||
cloudlog.warning("got SIGINT")
|
||||
Reference in new issue
Block a user