Deep Learning Prediction with Intel MKL-DNN_Is​sue_on_Bui​ld_and_Run​_the_Execu​table

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I executed the code from Deep Learning Prediction with Intel MKL-DNN (https://www.mathworks.com/help/deeplearning/examples/deep-learning-prediction-with-intel-mkl-dnn.html). I successful on executing all the instructions except "Build and Run the Executable".
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Build and Run the Executable
Build the executable based on the target platform. On a Windows platform, this example uses Microsoft® Visual Studio® 2017 for C++.
if ispc
setenv('MATLAB_ROOT', matlabroot);
system('make_mkldnn_win17.bat');
system('resnet.exe peppers.png');
else
setenv('MATLAB_ROOT', matlabroot);
system('make -f Makefile_mkldnn_linux.mk');
system('./resnet_exe peppers.png');
end
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In this case, I am using Ubuntu 18 64 bit platform with 16GB RAM Intel I3 processor. I am using MATLAB 2019a. I am not getting any executable file so that I can execute it "system('./resnet_exe peppers.png')". I have no idea how to get this executable file in linux platform. On windows, they said Microsoft Visual Studio 2017 for C++. What about linux. Please help.
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After "Generate a Static Library for the resnet_predict Function" I got the following files:
buildInfo.mat
cnn_resnet_res4d_branch2c_b
cnn_api.cpp
cnn_resnet_res4d_branch2c_w
cnn_api.hpp
cnn_resnet_res4e_branch2a_b
cnn_api.o
cnn_resnet_res4e_branch2a_w
cnn_resnet_avg
cnn_resnet_res4e_branch2b_b
cnn_resnet_conv1_b
cnn_resnet_res4e_branch2b_w
cnn_resnet_conv1_w
cnn_resnet_res4e_branch2c_b
cnn_resnet_fc1000_b
cnn_resnet_res4e_branch2c_w
cnn_resnet_fc1000_w
cnn_resnet_res4f_branch2a_b
cnn_resnet_labels.txt
cnn_resnet_res4f_branch2a_w
cnn_resnet_res2a_branch1_b
cnn_resnet_res4f_branch2b_b
cnn_resnet_res2a_branch1_w
cnn_resnet_res4f_branch2b_w
cnn_resnet_res2a_branch2a_b
cnn_resnet_res4f_branch2c_b
cnn_resnet_res2a_branch2a_w
cnn_resnet_res4f_branch2c_w
cnn_resnet_res2a_branch2b_b
cnn_resnet_res5a_branch1_b
cnn_resnet_res2a_branch2b_w
cnn_resnet_res5a_branch1_w
cnn_resnet_res2a_branch2c_b
cnn_resnet_res5a_branch2a_b
cnn_resnet_res2a_branch2c_w
cnn_resnet_res5a_branch2a_w
cnn_resnet_res2b_branch2a_b
cnn_resnet_res5a_branch2b_b
cnn_resnet_res2b_branch2a_w
cnn_resnet_res5a_branch2b_w
cnn_resnet_res2b_branch2b_b
cnn_resnet_res5a_branch2c_b
cnn_resnet_res2b_branch2b_w
cnn_resnet_res5a_branch2c_w
cnn_resnet_res2b_branch2c_b
cnn_resnet_res5b_branch2a_b
cnn_resnet_res2b_branch2c_w
cnn_resnet_res5b_branch2a_w
cnn_resnet_res2c_branch2a_b
cnn_resnet_res5b_branch2b_b
cnn_resnet_res2c_branch2a_w
cnn_resnet_res5b_branch2b_w
cnn_resnet_res2c_branch2b_b
cnn_resnet_res5b_branch2c_b
cnn_resnet_res2c_branch2b_w
cnn_resnet_res5b_branch2c_w
cnn_resnet_res2c_branch2c_b
cnn_resnet_res5c_branch2a_b
cnn_resnet_res2c_branch2c_w
cnn_resnet_res5c_branch2a_w
cnn_resnet_res3a_branch1_b
cnn_resnet_res5c_branch2b_b
cnn_resnet_res3a_branch1_w
cnn_resnet_res5c_branch2b_w
cnn_resnet_res3a_branch2a_b
cnn_resnet_res5c_branch2c_b
cnn_resnet_res3a_branch2a_w
cnn_resnet_res5c_branch2c_w
cnn_resnet_res3a_branch2b_b
codeInfo.mat
cnn_resnet_res3a_branch2b_w
DeepLearningNetwork.cpp
cnn_resnet_res3a_branch2c_b
DeepLearningNetwork.h
cnn_resnet_res3a_branch2c_w
DeepLearningNetwork.o
cnn_resnet_res3b_branch2a_b
examples
cnn_resnet_res3b_branch2a_w
html
cnn_resnet_res3b_branch2b_b
interface
cnn_resnet_res3b_branch2b_w
MWCNNLayerImpl.cpp
cnn_resnet_res3b_branch2c_b
MWCNNLayerImpl.hpp
cnn_resnet_res3b_branch2c_w
MWCNNLayerImpl.o
cnn_resnet_res3c_branch2a_b
MWConvLayer.cpp
cnn_resnet_res3c_branch2a_w
MWConvLayer.hpp
cnn_resnet_res3c_branch2b_b
MWConvLayerImpl.cpp
cnn_resnet_res3c_branch2b_w
MWConvLayerImpl.hpp
cnn_resnet_res3c_branch2c_b
MWConvLayerImpl.o
cnn_resnet_res3c_branch2c_w
MWConvLayer.o
cnn_resnet_res3d_branch2a_b
MWFusedConvReLULayer.cpp
cnn_resnet_res3d_branch2a_w
MWFusedConvReLULayer.hpp
cnn_resnet_res3d_branch2b_b
MWFusedConvReLULayerImpl.cpp
cnn_resnet_res3d_branch2b_w
MWFusedConvReLULayerImpl.hpp
cnn_resnet_res3d_branch2c_b
MWFusedConvReLULayerImpl.o
cnn_resnet_res3d_branch2c_w
MWFusedConvReLULayer.o
cnn_resnet_res4a_branch1_b
MWMkldnnUtils.cpp
cnn_resnet_res4a_branch1_w
MWMkldnnUtils.hpp
cnn_resnet_res4a_branch2a_b
MWMkldnnUtils.o
cnn_resnet_res4a_branch2a_w
MWTargetNetworkImpl.cpp
cnn_resnet_res4a_branch2b_b
MWTargetNetworkImpl.hpp
cnn_resnet_res4a_branch2b_w
MWTargetNetworkImpl.o
cnn_resnet_res4a_branch2c_b
predict.cpp
cnn_resnet_res4a_branch2c_w
predict.h
cnn_resnet_res4b_branch2a_b
predict.o
cnn_resnet_res4b_branch2a_w
resnet_predict.cpp
cnn_resnet_res4b_branch2b_b
resnet_predict.h
cnn_resnet_res4b_branch2b_w
resnet_predict_initialize.cpp
cnn_resnet_res4b_branch2c_b
resnet_predict_initialize.h
cnn_resnet_res4b_branch2c_w
resnet_predict_initialize.o
cnn_resnet_res4c_branch2a_b
resnet_predict.o
cnn_resnet_res4c_branch2a_w
resnet_predict_ref.rsp
cnn_resnet_res4c_branch2b_b
resnet_predict_rtw.mk
cnn_resnet_res4c_branch2b_w
resnet_predict_terminate.cpp
cnn_resnet_res4c_branch2c_b
resnet_predict_terminate.h
cnn_resnet_res4c_branch2c_w
resnet_predict_terminate.o
cnn_resnet_res4d_branch2a_b
resnet_predict_types.h
cnn_resnet_res4d_branch2a_w
rtw_proj.tmw
cnn_resnet_res4d_branch2b_b
rtwtypes.h
cnn_resnet_res4d_branch2b_w
=============================================
List of tools installed in my matlab:
ver
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MATLAB Version: 9.6.0.1150989 (R2019a) Update 4
MATLAB License Number: 40524824
Operating System: Linux 5.0.0-23-generic #24~18.04.1-Ubuntu SMP Mon Jul 29 16:12:28 UTC 2019 x86_64
Java Version: Java 1.8.0_181-b13 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode
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MATLAB Version 9.6 (R2019a)
Simulink Version 9.3 (R2019a)
Audio Toolbox Version 2.0 (R2019a)
Bioinformatics Toolbox Version 4.12 (R2019a)
Communications Toolbox Version 7.1 (R2019a)
Computer Vision Toolbox Version 9.0 (R2019a)
Control System Toolbox Version 10.6 (R2019a)
DSP System Toolbox Version 9.8 (R2019a)
Deep Learning Toolbox Version 12.1 (R2019a)
Fixed-Point Designer Version 6.3 (R2019a)
GPU Coder Version 1.3 (R2019a)
HDL Coder Version 3.14 (R2019a)
HDL Verifier Version 5.6 (R2019a)
Image Acquisition Toolbox Version 6.0 (R2019a)
Image Processing Toolbox Version 10.4 (R2019a)
Instrument Control Toolbox Version 4.0 (R2019a)
MATLAB Coder Version 4.2 (R2019a)
MATLAB Compiler Version 7.0.1 (R2019a)
MATLAB Compiler SDK Version 6.6.1 (R2019a)
Optimization Toolbox Version 8.3 (R2019a)
Parallel Computing Toolbox Version 7.0 (R2019a)
Sensor Fusion and Tracking Toolbox Version 1.1 (R2019a)
Signal Processing Toolbox Version 8.2 (R2019a)
Simulink Control Design Version 5.3 (R2019a)
Statistics and Machine Learning Toolbox Version 11.5 (R2019a)
Symbolic Math Toolbox Version 8.3 (R2019a)
System Composer Version 1.0 (R2019a)
System Identification Toolbox Version 9.10 (R2019a)
Text Analytics Toolbox Version 1.3 (R2019a)
>>

Risposte (1)

Praveen Kumar Gajula
Praveen Kumar Gajula il 18 Giu 2020
Hi,
You need to execute the below commands for linux.
setenv('MATLAB_ROOT', matlabroot);
system('make -f Makefile_mkldnn_linux.mk');
system('./resnet_exe peppers.png');
These commands will build the resent_exe.
Thank you,
Praveen.

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