jetauto@jetauto-desktop:~/jetauto_ws/src/jetauto_example/scripts/yolov5_detect$ python3 xwc.py [10/29/2024-18:41:42] [TRT] [I] [MemUsageChange] Init CUDA: CPU +224, GPU +0, now: CPU 265, GPU 3416 (MiB) [10/29/2024-18:41:42] [TRT] [I] Loaded engine size: 22 MiB [10/29/2024-18:41:47] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +158, GPU +36, now: CPU 452, GPU 3479 (MiB) [10/29/2024-18:41:53] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +240, GPU -12, now: CPU 692, GPU 3467 (MiB) [10/29/2024-18:41:53] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +21, now: CPU 0, GPU 21 (MiB) [10/29/2024-18:41:53] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +0, now: CPU 670, GPU 3445 (MiB) [10/29/2024-18:41:53] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +0, now: CPU 670, GPU 3445 (MiB) [10/29/2024-18:41:53] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +34, now: CPU 0, GPU 55 (MiB) bingding: data (3, 480, 640) bingding: prob (38001, 1, 1) (python3:31020): GStreamer-CRITICAL **: 18:41:55.346: Trying to dispose element pipeline0, but it is in READY instead of the NULL state. You need to explicitly set elements to the NULL state before dropping the final reference, to allow them to clean up. This problem may also be caused by a refcounting bug in the application or some element. [ WARN:[email protected]] global /home/jetauto/opencv/modules/videoio/src/cap_gstreamer.cpp (1356) open OpenCV | GStreamer warning: unable to start pipeline (python3:31020): GStreamer-CRITICAL **: 18:41:55.347: Trying to dispose element videoconvert0, but it is in PAUSED instead of the NULL state. You need to explicitly set elements to the NULL state before dropping the final reference, to allow them to clean up. This problem may also be caused by a refcounting bug in the application or some element. [ WARN:[email protected]] global /home/jetauto/opencv/modules/videoio/src/cap_gstreamer.cpp (862) isPipelinePlaying OpenCV | GStreamer warning: GStreamer: pipeline have not been created (python3:31020): GStreamer-CRITICAL **: 18:41:55.347: Trying to dispose element appsink0, but it is in READY instead of the NULL state. You need to explicitly set elements to the NULL state before dropping the final reference, to allow them to clean up. This problem may also be caused by a refcounting bug in the application or some element. (python3:31020): GStreamer-CRITICAL **: 18:41:55.350: gst_element_post_message: assertion 'GST_IS_ELEMENT (element)' failed ^CTraceback (most recent call last): File "xwc.py", line 431, in <module> boxes, scores, classid = yolov5_wrapper.infer(frame) File "xwc.py", line 153, in infer input_image, image_raw, origin_h, origin_w = self.preprocess_image(raw_image_generator) File "xwc.py", line 264, in preprocess_image image = np.transpose(image, [2, 0, 1]) File "<__array_function__ internals>", line 6, in transpose File "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py", line 653, in transpose return _wrapfunc(a, 'transpose', axes) KeyboardInterrupt ------------------------------------------------------------------- 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. 解释

<think>嗯，用户给了一段Python脚本运行的错误日志，看起来是运行一个基于YOLOv5的目标检测脚本时遇到了问题。首先，我需要仔细看看这些错误信息，找出可能的原因和解决方案。 首先，日志里提到了TensorRT（TRT）的相关信息，看起来程序在加载TensorRT引擎，初始化CUDA、cuBLAS、cuDNN这些库的时候占用了内存。这部分看起来正常，没有明显的错误。接下来是GStreamer的CRITICAL错误，提示在释放元素的时候状态不对，比如pipeline0处于READY状态而不是NULL，videoconvert0处于PAUSED状态。然后OpenCV的警告说无法启动pipeline，可能GStreamer的管道创建失败。最后是KeyboardInterrupt，用户可能按了Ctrl+C中断了程序，导致PyCUDA报错，上下文栈未清空。 首先，GStreamer相关的错误可能和视频流的处理有关。用户可能在用OpenCV通过GStreamer来捕获摄像头或视频文件，但配置可能有问题。比如，GStreamer的管道没有正确关闭，导致元素状态没有正确释放。此外，OpenCV的警告提示无法启动pipeline，可能说明摄像头设备没找到，或者GStreamer的管道字符串有误。 然后是KeyboardInterrupt导致的异常，程序在中断时可能没有正确释放CUDA资源，导致PyCUDA在清理时发现上下文栈未清空，从而报错。PyCUDA的文档中提到，需要确保在退出前通过Context.pop()来正确释放上下文，否则可能会有问题。 接下来，我需要考虑用户可能的环境配置问题。比如，是否正确安装了GStreamer及其插件，OpenCV是否编译了GStreamer支持。另外，摄像头设备权限是否有问题，比如/dev/video0是否可读。还有，代码中是否有正确释放资源的逻辑，尤其是在捕获视频流和处理CUDA资源时。 可能的解决步骤包括： 1. 检查GStreamer管道配置是否正确，确保摄像头设备正确。 2. 在代码中添加正确释放GStreamer管道的逻辑，比如在退出前将元素状态设为NULL。 3. 处理KeyboardInterrupt异常，确保在捕获到中断信号时，正确释放CUDA上下文和其他资源。 4. 检查PyCUDA的使用，确保在程序结束前调用Context.pop()来清理上下文栈。 5. 确保OpenCV编译时启用了GStreamer支持，并且相关插件已安装。 另外，用户的环境是Ubuntu，可能需要安装gstreamer1.0-plugins-good等插件。代码中的cv2.VideoCapture可能使用了GStreamer后端，如果配置错误会导致无法打开视频流。此外，程序在中断时未正确释放资源，可能需要使用try-except块来捕获KeyboardInterrupt，并在其中进行清理操作。 总结下来，用户的问题主要集中在GStreamer管道的配置和资源释放，以及PyCUDA上下文的正确管理。需要逐步排查这些方面，确保资源正确初始化和释放，避免状态错误和内存泄漏。</think>你提供的日志信息显示程序运行时涉及TensorRT推理、GStreamer视频流处理和PyCUDA上下文管理三个层面的问题。以下是分步解析： --- ### 一、TensorRT推理部分 ``` [TRT] [I] Loaded engine size: 22 MiB ``` - **正常现象**：TensorRT成功加载了22MB的推理引擎，初始化了CUDA、cuBLAS和cuDNN组件，内存占用变化属于正常初始化过程。 --- ### 二、GStreamer视频流问题 #### 1. 关键错误现象 ``` GStreamer-CRITICAL: Trying to dispose element pipeline0...in READY state OpenCV | GStreamer warning: unable to start pipeline ``` #### 2. 原因分析 - **状态未正确释放**：GStreamer管道(`pipeline0`/`appsink0`)未被正确设置为`NULL`状态，导致资源释放时冲突。 - **管道初始化失败**：可能由于以下原因： - 摄像头设备未正确识别（如`/dev/video*`权限问题） - GStreamer插件缺失（如缺少`gstreamer1.0-plugins-good`） - OpenCV未正确编译GStreamer支持 #### 3. 解决方案 ```python # 在代码中添加资源释放逻辑（示例） import cv2 cap = cv2.VideoCapture(...) try: while True: # 处理视频流 except KeyboardInterrupt: cap.release() # 显式释放视频流 cv2.destroyAllWindows() ``` --- ### 三、PyCUDA上下文错误 #### 1. 关键错误现象 ``` PyCUDA ERROR: The context stack was not empty upon module cleanup ``` #### 2. 原因分析 - **未正确释放CUDA上下文**：程序被`KeyboardInterrupt`强行终止时，PyCUDA上下文未通过`pop()`方法释放。 - **与TensorRT交互问题**：TensorRT可能已释放CUDA资源，但PyCUDA仍持有引用。 #### 3. 解决方案 ```python import pycuda.autoinit # 确保自动初始化上下文 from pycuda.tools import clear_context_caches try: # 主程序逻辑 except KeyboardInterrupt: clear_context_caches() # 清理CUDA上下文 context.pop() # 如果手动创建了上下文 ``` --- ### 四、完整修复建议 1. **检查视频输入设备** ```bash ls /dev/video* # 确认摄像头设备存在 v4l2-ctl --list-devices # 查看详细设备信息 ``` 2. **安装GStreamer依赖** ```bash sudo apt-get install gstreamer1.0-plugins-good \ gstreamer1.0-tools \ gstreamer1.0-libav ``` 3. **修改代码逻辑** ```python # 在视频流捕获部分添加异常处理 cap = cv2.VideoCapture('v4l2src device=/dev/video0 ! videoconvert ! appsink', cv2.CAP_GSTREAMER) if not cap.isOpened(): print("无法打开摄像头，检查GStreamer管道配置") exit() ``` 4. **增强CUDA上下文管理** ```python import atexit def cleanup(): from pycuda.tools import clear_context_caches clear_context_caches() atexit.register(cleanup) ``` --- ### 五、附加建议 - 使用`nvidia-smi`监控GPU内存变化 - 检查OpenCV编译信息： ```bash opencv_version --verbose | grep "GStreamer" ``` - 尝试更换视频流输入方式（如改用`cv2.CAP_V4L2`） 通过上述步骤应能解决GStreamer管道初始化失败和PyCUDA上下文泄漏问题。如果问题持续，建议提供完整的GStreamer管道配置代码进一步分析。