Files
sunnypilot/selfdrive/modeld/navmodeld.py
YassineYousfi d18f185115 modeld: parsing and publishing in python (#30273)
* WIP try modeld all in python

* fix plan

* add lane lines stds

* fix lane lines prob

* add lead prob

* add meta

* simplify plan parsing

* add hard brake pred

* add confidence

* fix desire state and desire pred

* check this file for now

* rm prints

* rm debug

* add todos

* add plan_t_idxs

* same as cpp

* removed cython

* add wfd width - rm cpp code

* add new files rm old files

* get metadata at compile time

* forgot this file

* now uses more CPU

* not used

* update readme

* lint

* copy this too

* simplify disengage probs

* update model replay ref commit

* update again

* confidence: remove if statemens

* use publish_state.enqueue

* Revert "use publish_state.enqueue"

This reverts commit d8807c8348338a1f773a8de00fd796abb8181404.

* confidence: better shape defs

* use ModelConstants class

* fix confidence

* Parser

* slightly more power too

* no inline ifs :(

* confidence: just use if statements
old-commit-hash: cad17b1255
2023-10-19 14:23:51 -07:00

119 lines
4.1 KiB
Python
Executable File

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