MIVisionX toolkit is a comprehensive computer vision and machine intelligence libraries, utilities and applications bundled into a single toolkit.
Home Page: https://gpuopen-professionalcompute-libraries.github.io/MIVisionX/
License: MIT License
Hi,
When I try to do prepareModel.sh I have the following error in linking anntest :
[100%] Linking CXX executable anntest
//opt/rocm/lib/libMIOpen.so.1: undefined reference to MIOpenGEMM::Geometry::initialise(bool, bool, bool, bool, unsigned long, unsigned long, unsigned long, unsigned long, unsigned long, unsigned long, unsigned long, char)@MIOPENGEMM_1' /opt/rocm/mivisionx/lib/libvx_nn.so: undefined reference to MIOpenGEMM::GemmStatus MIOpenGEMM::xgemm(bool, bool, bool, unsigned long, unsigned long, unsigned long, float, _cl_mem*, unsigned long, unsigned long, _cl_mem*, unsigned long, unsigned long, float, _cl_mem*, unsigned long, unsigned long, _cl_mem*, unsigned long, unsigned long, _cl_command_queue**, unsigned int, _cl_event* const*, _cl_event**, int)@MIOPENGEMM_1'
Any idea ?
then when I go to trafficVision and I do make test I have the following error
OK: OpenVX using GPU device#0 (gfx900) [OpenCL 2.0 ] [SvmCaps 0 0]
OK: annCreateInference: successful
TETS
Traceback (most recent call last):
File "./main.py", line 58, in
yoloNet = inference.yoloInferenceNet(yoloOpenVX.weights);
File "/home/goose/GOOSE/YOLO/trafficVision/inference.py", line 29, in init
input_info,output_info = yoloOpenVX.interface.annQueryInference().decode("utf-8").split(';')
ValueError: too many values to unpack
Makefile:8: recipe for target 'test' failed
make: *** [test] Error 1
Can you help ?
Thanks
Regards
G.
trafficVision/speedEstimator.py
Line 30 in e589042
trafficVision/speedEstimator.py
Line 32 in e589042
could you please explain why you subtract 400 and 500 in different directions? and how to get these values?
thank you very much.
Model conversion fails with the latest MIVisionX Master Install
kiriti@simon:~/ISV/trafficVision$ ./main.py --video media/demo.mp4
('Loaded', 'yoloOpenVX')
OK: loaded 22 kernels from libvx_nn.so
OK: OpenVX using GPU device#0 (gfx900) [OpenCL 1.2 ] [SvmCaps 0 1]
OK: annCreateInference: successful
Traceback (most recent call last):
File "./main.py", line 49, in <module>
yoloNet = inference.yoloInferenceNet(yoloOpenVX.weights);
File "/home/kiriti/ISV/trafficVision/inference.py", line 28, in __init__
input_info,output_info = yoloOpenVX.interface.annQueryInference().decode("utf-8").split(';')
ValueError: too many values to unpack
kiriti@simon:~/ISV/kiriti/trafficVision$ make test
for vid in media/demo.mp4; do \
echo processing $vid ;\
/usr/bin/python ./main.py --video $vid ;\
done
processing media/demo.mp4
('Loaded', 'yoloOpenVX')
OK: loaded 24 kernels from libvx_nn.so
OK: OpenVX using GPU device#0 (gfx900) [OpenCL 1.2 ] [SvmCaps 0 1]
OK: annCreateInference: successful
Traceback (most recent call last):
File "./main.py", line 49, in <module>
yoloNet = inference.yoloInferenceNet(yoloOpenVX.weights);
File "/home/kiriti/ISV/kiriti/trafficVision/inference.py", line 28, in __init__
input_info,output_info = yoloOpenVX.interface.annQueryInference().decode("utf-8").split(';')
ValueError: too many values to unpack
Makefile:8: recipe for target 'test' failed
make: *** [test] Error 1
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