jason-li-831202 / Vehicle-CV-ADAS

The project can achieve FCWS, LDWS, and LKAS functions solely using only visual sensors. using YOLOv5 / YOLOv5-lite / YOLOv6 / YOLOv7 / YOLOv8 / YOLOv9 / EfficientDet and Ultra-Fast-Lane-Detection-v2 .
GNU General Public License v3.0
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大佬,运行demo.py报错,你之前遇到过吗 #3

Closed Handaphoser closed 2 months ago

Handaphoser commented 1 year ago

[TensorRT] ERROR: INVALID_STATE: std::exception [TensorRT] ERROR: INVALID_CONFIG: Deserialize the cuda engine failed. Traceback (most recent call last): File "demo.py", line 221, in laneDetector = UltrafastLaneDetectorV2(logger=LOGGER) File "/media/huangzd5/handaphoser/projects/autodrive/Vehicle-CV-ADAS-master/TrafficLaneDetector/ultrafastLaneDetector/ultrafastLaneDetectorV2.py", line 208, in init self._initialize_model(self.model_path, self.cfg) File "/media/huangzd5/handaphoser/projects/autodrive/Vehicle-CV-ADAS-master/TrafficLaneDetector/ultrafastLaneDetector/ultrafastLaneDetectorV2.py", line 213, in _initialize_model self.infer = TensorRTEngine(model_path, cfg) File "/media/huangzd5/handaphoser/projects/autodrive/Vehicle-CV-ADAS-master/TrafficLaneDetector/ultrafastLaneDetector/ultrafastLaneDetectorV2.py", line 60, in init self.context = self._create_context(engine) File "/media/huangzd5/handaphoser/projects/autodrive/Vehicle-CV-ADAS-master/TrafficLaneDetector/ultrafastLaneDetector/ultrafastLaneDetectorV2.py", line 93, in _create_context return engine.create_execution_context() AttributeError: 'NoneType' object has no attribute 'create_execution_context'

PyCUDA ERROR: The context stack was not empty upon module cleanup.

A context was still active when the context stack was being cleaned up. At this point in our execution, CUDA may already have been deinitialized, so there is no way we can finish cleanly. The program will be aborted now. Use Context.pop() to avoid this problem.

jason-li-831202 commented 1 year ago

The problem may be due to the version of the environment. The CUDA version I'm using is 11.4, and the TensorRT version is 8.4.x. Remember to deploy on a device that matches the environment used for converting ONNX to TensorRT.

Handaphoser commented 1 year ago

好的,谢谢,我跑onnx模型没什么问题。项目很惊艳,不过速度慢了点,大佬在提升速度有什么好的建议吗,非常感谢。

jason-li-831202 commented 1 year ago

inference速度會受cpu/gpu的影響,另外object detection和lanes detection使用上沒有平行運算所以inference相對較慢,也許可以使用multi-Progress的方式去處理會有所提升,在模型方面可以試著轉換fp16的精度去提升。

Handaphoser commented 1 year ago

好的,感谢感谢

JohnsenJiang commented 1 year ago

demo配置中的模型文件和视频文件上哪里可以下载得到?谢谢!

jason-li-831202 commented 11 months ago

可以參考以下issue : https://github.com/jason-li-831202/Vehicle-CV-ADAS/issues/1#issuecomment-1432452737