So I am converting retinanet for object detection, after I convert the model to XML and BIN, I try to use the provided ssd python code to test the converted model.
Here is my code for running the script:
python3 object_detection_demo_ssd_async.py -m openvino_test_model/ssd_v2_for_retina/frozen_inference_graph.xml -i car_1.bmp
But I got following error:
[ ERROR ] Following layers are not supported by the plugin for specified device CPU:
Resample_6828, Resample_, PriorBoxClustered_4, PriorBoxClustered_3, PriorBoxClustered_2, PriorBoxClustered_1, PriorBoxClustered_0, DetectionOutput
[ ERROR ] Please try to specify cpu extensions library path in demo's command line parameters using -l or --cpu_extension command line argument
I searched the Document, and find that this should be included in CPU extension lib (i am testing using CPU). However in openvino API I didn't find how and where to utilize this lib. I searched the name for those layers and find out they are located in deployment_tools/model_optimizer/extensions/ops/. But I dont know how to use them. I assume I should use the -l/--cpu_extension parameter but I dont know what is the argument.
So, I noticed that the cpu extension in python for -l is in /opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_xxxx.so
And also, the object_detection_demo_ssd_async.py is not well written. It only considers the case when async is used. If you are testing with one image, then it wont work. (though the code notation says it can deal with single image) . One needs to modify this code for yourself.
Is there a VPU extension libs?
Dear Yang, Yuchen,
We have released a new version of OpenVino 2019R1.1 . Please try it, many issues have been fixed. I'm glad you found out about the -l switch. You can find detailed documentation about all OpenVino samples at the doc link below:
Also the object_detection_demo_ssd_async.py does only use the Async API (as you noted). But we have a non-Async C++ version. The C++ code should be easily translatable to Python.