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

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

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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)

>>

Answer 1

[Praveen Kumar Gajula]

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.