Closed gitunit closed 4 years ago
Can you provide more information about your setup, your environment, using CPU/GPU/VPU, which application(s) (in C++, in Python?), which command line parameters, running natively (Ubuntu 18.04?) or within a container? Are you using the prebuilt OpenVINO installation package or compiled DLDT from source? Can you also describe how you measure the memory consumption?
im using CVAT with OpenVINO 2019R3 (from the OpenVINO website). thus i believe it (CVAT) is using python. i've tested on Ubuntu 16.04 and also on plain Debian
Can you try to run the sample "object_detection_demo_yolov3_async" (from Open-Model-Zoo, part of the OpenVINO installation), please? It allows you to specify a video file via the parameter "-i" as well. With "-d" you can specify whether to use CPU, GPU or VPU (e.g. Myriax/MyriadX).
Try to use a "native" OpenVINO sample first - and later maybe file an issue at "https://github.com/opencv/cvat" ;-)
ok. i've tried and couldn't observe a memory leak. is there an equivalent python version?
This is probably an issue with CVAT. I think there are prexisting issues that note this issue.
There is a Python demo as well:
/opt/intel/openvino/inference_engine/demos/python_demos/object_detection_demo_yolov3_async
thx.
where do i get CPU extensions (i need to test on CPU since CVAT only supports that, so i can compare better)? i get the following error:
[ ERROR ] Following layers are not supported by the plugin for specified device CPU: detector/yolo-v3/ResizeNearestNeighbor, detector/yolo-v3/ResizeNearestNeighbor_1, detector/yolo-v3/Conv_22/BiasAdd/YoloRegion, detector/yolo-v3/Conv_14/BiasAdd/YoloRegion, detector/yolo-v3/Conv_6/BiasAdd/YoloRegion
Under /opt/intel/openvino/inference_engine/lib/intel64/
, you can specifiy e.g. "-l /opt/intel/openvino/inference_engine/lib/intel64/libMKLDNNPlugin.so".
[ INFO ] Creating Inference Engine... Traceback (most recent call last): File "/opt/intel/openvino_2019.3.376/inference_engine/demos/python_demos/object_detection_demo_yolov3_async/object_detection_demo_yolov3_async.py", line 359, in <module> sys.exit(main() or 0) File "/opt/intel/openvino_2019.3.376/inference_engine/demos/python_demos/object_detection_demo_yolov3_async/object_detection_demo_yolov3_async.py", line 178, in main ie.add_extension(args.cpu_extension, "CPU") File "ie_api.pyx", line 118, in openvino.inference_engine.ie_api.IECore.add_extension RuntimeError: dlSym cannot locate method 'CreateExtension': /opt/intel/openvino/inference_engine/lib/intel64/libMKLDNNPlugin.so: undefined symbol: CreateExtension
something missing from the installation maybe?
Have you installed the packages in "requirements.txt" under /opt/intel/openvino/inference_engine/demos/python_demos
?
I don't need to specify a CPU extension... It just works for me:
$> python3 object_detection_demo_yolov3_async.py -m /var/data/yolo-v3/FP32/yolo-v3.xml -i /var/data/Airport-1080p-30FPS-5Mbps-AVC.mp4 -d CPU --labels /var/data/yolo-v3/FP32/yolo-v3.labels -t 0.6
And also for tiny-yolo-v3:
$> python3 object_detection_demo_yolov3_async.py -m /var/data/tiny-yolo-v3/FP32/tiny-yolo-v3.xml -i /var/data/Airport-1080p-30FPS-5Mbps-AVC.mp4 -d CPU --labels /var/data/tiny-yolo-v3/FP32/tiny-yolo-v3.labels -t 0.6
I'm using "Python 3.5.2". I'm using "openvino_2020.1.023".
I think your older version of OpenVINO still has the old CPU-extension library.... Can you check your folder "/opt/intel/openvino/inference_engine/lib/intel64/" and see whether you see "libcpu_extension*.so"... Then use this instead of "libMKLDNNPlugin.so", sorry.
thx, that did the trick. i used libcpu_extension_avx2.so and the inference is running. no memory leak there so far.
@gitunit where do people usually get the cpu extensions people reference them everywhere but there isn't a clear method to get them
@Maioy97 open model zoo
i have used this instructions to integrate yolov3 into OpenVINO. when im using it for inference, there is a very big memory leak which will lead to a crash after all RAM gets consumed (and all SWAP as well). therefore i never can finish processing the whole video (2 hours long). i have tested it on a machine with 32 gb RAM and appr. the same in SWAP space but due to the memory leak it gets filled up until it crashes.