mirror of
https://github.com/firestar5683/StarPilot.git
synced 2026-08-21 08:14:00 +08:00
dmonitoringmodeld: clean up data structures (#36624)
* update onnx * get meta * start * cast * deprecate notready * more * line too long * 2
This commit is contained in:
ZwX1616
@@ -32,7 +32,7 @@ lenvCython.Program('models/commonmodel_pyx.so', 'models/commonmodel_pyx.pyx', LI
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tinygrad_files = ["#"+x for x in glob.glob(env.Dir("#tinygrad_repo").relpath + "/**", recursive=True, root_dir=env.Dir("#").abspath) if 'pycache' not in x]
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# Get model metadata
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for model_name in ['driving_vision', 'driving_policy']:
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for model_name in ['driving_vision', 'driving_policy', 'dmonitoring_model']:
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fn = File(f"models/{model_name}").abspath
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script_files = [File(Dir("#selfdrive/modeld").File("get_model_metadata.py").abspath)]
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cmd = f'python3 {Dir("#selfdrive/modeld").abspath}/get_model_metadata.py {fn}.onnx'
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@@ -7,7 +7,6 @@ from tinygrad.dtype import dtypes
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import math
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import time
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import pickle
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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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@@ -16,47 +15,16 @@ from cereal.messaging import PubMaster, SubMaster
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from msgq.visionipc import VisionIpcClient, VisionStreamType, VisionBuf
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from openpilot.common.swaglog import cloudlog
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from openpilot.common.realtime import config_realtime_process
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from openpilot.common.transformations.model import dmonitoringmodel_intrinsics, DM_INPUT_SIZE
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from openpilot.common.transformations.model import dmonitoringmodel_intrinsics
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from openpilot.common.transformations.camera import _ar_ox_fisheye, _os_fisheye
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from openpilot.selfdrive.modeld.models.commonmodel_pyx import CLContext, MonitoringModelFrame
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from openpilot.selfdrive.modeld.parse_model_outputs import sigmoid
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from openpilot.selfdrive.modeld.runners.tinygrad_helpers import qcom_tensor_from_opencl_address
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MODEL_WIDTH, MODEL_HEIGHT = DM_INPUT_SIZE
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CALIB_LEN = 3
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FEATURE_LEN = 512
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OUTPUT_SIZE = 83 + FEATURE_LEN
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PROCESS_NAME = "selfdrive.modeld.dmonitoringmodeld"
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SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
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MODEL_PKL_PATH = Path(__file__).parent / 'models/dmonitoring_model_tinygrad.pkl'
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# TODO: slice from meta
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class DriverStateResult(ctypes.Structure):
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_fields_ = [
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("face_orientation", ctypes.c_float*3),
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("face_position", ctypes.c_float*3),
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("face_orientation_std", ctypes.c_float*3),
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("face_position_std", ctypes.c_float*3),
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("face_prob", ctypes.c_float),
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("_unused_a", ctypes.c_float*8),
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("left_eye_prob", ctypes.c_float),
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("_unused_b", ctypes.c_float*8),
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("right_eye_prob", ctypes.c_float),
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("left_blink_prob", ctypes.c_float),
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("right_blink_prob", ctypes.c_float),
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("sunglasses_prob", ctypes.c_float),
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("_unused_c", ctypes.c_float),
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("_unused_d", ctypes.c_float*4),
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("not_ready_prob", ctypes.c_float*2)]
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class DMonitoringModelResult(ctypes.Structure):
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_fields_ = [
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("driver_state_lhd", DriverStateResult),
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("driver_state_rhd", DriverStateResult),
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("wheel_on_right_prob", ctypes.c_float),
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("features", ctypes.c_float*FEATURE_LEN)]
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METADATA_PATH = Path(__file__).parent / 'models/dmonitoring_model_metadata.pkl'
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class ModelState:
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@@ -64,11 +32,14 @@ class ModelState:
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output: np.ndarray
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def __init__(self, cl_ctx):
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assert ctypes.sizeof(DMonitoringModelResult) == OUTPUT_SIZE * ctypes.sizeof(ctypes.c_float)
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with open(METADATA_PATH, 'rb') as f:
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model_metadata = pickle.load(f)
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self.input_shapes = model_metadata['input_shapes']
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self.output_slices = model_metadata['output_slices']
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self.frame = MonitoringModelFrame(cl_ctx)
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self.numpy_inputs = {
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'calib': np.zeros((1, CALIB_LEN), dtype=np.float32),
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'calib': np.zeros(self.input_shapes['calib'], dtype=np.float32),
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}
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self.tensor_inputs = {k: Tensor(v, device='NPY').realize() for k,v in self.numpy_inputs.items()}
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@@ -84,9 +55,9 @@ class ModelState:
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if TICI:
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# The imgs tensors are backed by opencl memory, only need init once
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if 'input_img' not in self.tensor_inputs:
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self.tensor_inputs['input_img'] = qcom_tensor_from_opencl_address(input_img_cl.mem_address, (1, MODEL_WIDTH*MODEL_HEIGHT), dtype=dtypes.uint8)
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self.tensor_inputs['input_img'] = qcom_tensor_from_opencl_address(input_img_cl.mem_address, self.input_shapes['input_img'], dtype=dtypes.uint8)
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else:
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self.tensor_inputs['input_img'] = Tensor(self.frame.buffer_from_cl(input_img_cl).reshape((1, MODEL_WIDTH*MODEL_HEIGHT)), dtype=dtypes.uint8).realize()
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self.tensor_inputs['input_img'] = Tensor(self.frame.buffer_from_cl(input_img_cl).reshape(self.input_shapes['input_img']), dtype=dtypes.uint8).realize()
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output = self.model_run(**self.tensor_inputs).contiguous().realize().uop.base.buffer.numpy()
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@@ -95,31 +66,31 @@ class ModelState:
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return output, t2 - t1
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def fill_driver_state(msg, ds_result: DriverStateResult):
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msg.faceOrientation = list(ds_result.face_orientation)
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msg.faceOrientationStd = [math.exp(x) for x in ds_result.face_orientation_std]
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msg.facePosition = list(ds_result.face_position[:2])
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msg.facePositionStd = [math.exp(x) for x in ds_result.face_position_std[:2]]
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msg.faceProb = float(sigmoid(ds_result.face_prob))
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msg.leftEyeProb = float(sigmoid(ds_result.left_eye_prob))
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msg.rightEyeProb = float(sigmoid(ds_result.right_eye_prob))
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msg.leftBlinkProb = float(sigmoid(ds_result.left_blink_prob))
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msg.rightBlinkProb = float(sigmoid(ds_result.right_blink_prob))
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msg.sunglassesProb = float(sigmoid(ds_result.sunglasses_prob))
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msg.notReadyProb = [float(sigmoid(x)) for x in ds_result.not_ready_prob]
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def fill_driver_state(msg, model_output, output_slices, ds_suffix):
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face_descs = model_output[output_slices[f'face_descs_{ds_suffix}']]
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face_descs_std = face_descs[-6:]
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msg.faceOrientation = [float(x) for x in face_descs[:3]]
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msg.faceOrientationStd = [math.exp(x) for x in face_descs_std[:3]]
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msg.facePosition = [float(x) for x in face_descs[3:5]]
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msg.facePositionStd = [math.exp(x) for x in face_descs_std[3:5]]
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msg.faceProb = float(sigmoid(model_output[output_slices[f'face_prob_{ds_suffix}']][0]))
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msg.leftEyeProb = float(sigmoid(model_output[output_slices[f'left_eye_prob_{ds_suffix}']][0]))
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msg.rightEyeProb = float(sigmoid(model_output[output_slices[f'right_eye_prob_{ds_suffix}']][0]))
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msg.leftBlinkProb = float(sigmoid(model_output[output_slices[f'left_blink_prob_{ds_suffix}']][0]))
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msg.rightBlinkProb = float(sigmoid(model_output[output_slices[f'right_blink_prob_{ds_suffix}']][0]))
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msg.sunglassesProb = float(sigmoid(model_output[output_slices[f'sunglasses_prob_{ds_suffix}']][0]))
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msg.phoneProb = float(sigmoid(model_output[output_slices[f'using_phone_prob_{ds_suffix}']][0]))
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def get_driverstate_packet(model_output: np.ndarray, frame_id: int, location_ts: int, execution_time: float, gpu_execution_time: float):
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model_result = ctypes.cast(model_output.ctypes.data, ctypes.POINTER(DMonitoringModelResult)).contents
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def get_driverstate_packet(model_output: np.ndarray, output_slices: dict[str, slice], frame_id: int, location_ts: int, exec_time: float, gpu_exec_time: float):
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msg = messaging.new_message('driverStateV2', valid=True)
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ds = msg.driverStateV2
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ds.frameId = frame_id
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ds.modelExecutionTime = execution_time
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ds.gpuExecutionTime = gpu_execution_time
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ds.wheelOnRightProb = float(sigmoid(model_result.wheel_on_right_prob))
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ds.modelExecutionTime = exec_time
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ds.gpuExecutionTime = gpu_exec_time
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ds.wheelOnRightProb = float(sigmoid(model_output[output_slices['wheel_on_right']][0]))
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ds.rawPredictions = model_output.tobytes() if SEND_RAW_PRED else b''
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fill_driver_state(ds.leftDriverData, model_result.driver_state_lhd)
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fill_driver_state(ds.rightDriverData, model_result.driver_state_rhd)
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fill_driver_state(ds.leftDriverData, model_output, output_slices, 'lhd')
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fill_driver_state(ds.rightDriverData, model_output, output_slices, 'rhd')
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return msg
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@@ -140,7 +111,7 @@ def main():
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sm = SubMaster(["liveCalibration"])
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pm = PubMaster(["driverStateV2"])
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calib = np.zeros(CALIB_LEN, dtype=np.float32)
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calib = np.zeros(model.numpy_inputs['calib'].size, dtype=np.float32)
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model_transform = None
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while True:
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@@ -160,7 +131,8 @@ def main():
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model_output, gpu_execution_time = model.run(buf, calib, model_transform)
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t2 = time.perf_counter()
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pm.send("driverStateV2", get_driverstate_packet(model_output, vipc_client.frame_id, vipc_client.timestamp_sof, t2 - t1, gpu_execution_time))
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msg = get_driverstate_packet(model_output, model.output_slices, vipc_client.frame_id, vipc_client.timestamp_sof, t2 - t1, gpu_execution_time)
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pm.send("driverStateV2", msg)
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if __name__ == "__main__":
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:3a53626ab84757813fb16a1441704f2ae7192bef88c331bdc2415be6981d204f
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size 7191776
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oid sha256:3446bf8b22e50e47669a25bf32460ae8baf8547037f346753e19ecbfcf6d4e59
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size 6954368
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