MATLAB: Code Generation for Deep Learning Networks with ARM Compute Library_Help

code generationdeep learning networks with arm compute library

Hi,

This is the code I copied from (https://in.mathworks.com/help/coder/ug/code-generation-for-deep-learning-networks-with-arm-compute-library.html). It is not executing. Giving error. Please help.

I have installed the following: I am using MATLAB 2019a (version on Windows 10 64bit) I have quadro p1000 GPU. 32 GB RAM. i7 8th Gen.

MATLAB Coder Interface for Deep Learning Libraries. To install the support package, select it from the MATLAB Add-Ons menu.
ARM Compute Library for computer vision and machine learning must be installed on the target hardware.
Deep Learning Toolbox™.
Environment variables for the compilers and libraries.

My executed Code:

r = raspi('192.168.43.48','pi','raspberry');

cfg = coder.config('exe');

cfg.TargetLang = 'C++';

dlcfg = coder.DeepLearningConfig('arm-compute');

dlcfg.ArmArchitecture = 'armv7';

dlcfg.ArmComputeVersion = '18.03';

cfg.DeepLearningConfig = dlcfg;

hw = coder.hardware('Raspberry Pi');

cfg.Hardware = hw;

buildDir = '~/remoteBuildDir';

cfg.Hardware.BuildDir = buildDir;

cfg.CustomSource = 'main.cpp';

codegen -config cfg squeezenet_raspi_predict.m -args {ones(227, 227, 3,'single')} -report

Output:

>> Untitled6

Deploying code. This may take a few minutes.

??? Unable to locate function 'squeezenet_raspi_predict'.

Code generation failed: View Error Report

Error using codegen

Error in Untitled6 (line 19)

codegen -config cfg squeezenet_raspi_predict.m -args {ones(227, 227, 3,'single')} -report

Best Answer

Your code seems fine and should work properly if you have the ‘MATLAB Support Package for Raspberry Pi Hardware’.

Use the code in the following link to generate code in case you don’t have the support package installed.

https://www.mathworks.com/help/coder/ug/code-generation-for-deep-learning-networks-with-arm-compute-library.html#mw_eb682444-57dc-4677-8ad5-fd02f9787d54

Related Solutions

MATLAB: MATLAB Coder: How to build the ARM Compute Library for Deep Learning C++ code generation and deployment

Background

To build and run generated C++ code for Deep Learning on an ARM hardware target, you must have the ARM Compute Library installed on the ARM target.

The following describes instructions for building the ARM Compute library on an ARM target, such as the Raspberry Pi, Hikey960, and so on. You must use the ARM Compute Library version appropriate for your MATLAB Coder release:

For the MATLAB Coder R2018b release, use ARM Compute version 18.03.
For the MATLAB Coder R2019a release, you can use ARM Compute version 18.03 or 18.05.
For other MATLAB Coder releases, use the ARM Compute version specific for the release, as described in the release documentation: https://www.mathworks.com/help/coder/ug/prerequisites-for-deep-learning-with-matlab-coder.html

If you need to set up environment variables on your ARM target to point to the ARM Compute Library, see:

How do I setup the environment variables on ARM targets to point to the ARM Compute Library?

ARM Compute Library Build Instructions on Linux based hardware

These instructions assume that the operating system is already present on the hardware. For example, Raspbian Stretch on the Raspberry Pi. On the target hardware, open a command terminal and perform these steps.

1. Install git. Enter:

sudo apt-get install git

2. As noted above, you must use the ARM Compute Library appropriate for your MATLAB Coder release. To download the latest version of the ARM Compute library from https://github.com/ARM-software/ComputeLibrary, enter:

git clone https://github.com/Arm-software/ComputeLibrary.git

To use older libraries:

a. Download source code zip or tar file from https://github.com/ARM-software/ComputeLibrary/releases and unzip downloaded source code. (OR)

b. Use git commands to download a specific version. For example, to download version 18.05, use below commands:

git clone https://github.com/Arm-software/ComputeLibrary.git
cd ComputeLibrary
git tag -l
git checkout v18.05

3. Install scons:

sudo apt-get install scons
cd ComputeLibrary

4. Build the library by running the scons command with the relevant build options. For information on library build options, see https://arm-software.github.io/ComputeLibrary/latest/index.xhtml#S3_how_to_build. The ARM Compute libraries are generated under the folder 'build'.

For example, to build the ARM Compute Library for Raspberry Pi, enter:

scons Werror=0 -j2 debug=0 neon=1 opencl=0 os=linux arch=armv7a openmp=1 examples=0 asserts=0 build=native

To build ARM Compute Library for Hikey960 with opencl, enter:

scons Werror=0 -j2 debug=0 neon=0 opencl=1 os=linux arch=arm64-v8a openmp=1 examples=0 asserts=0 build=native

5. Rename the folder ‘build’ to ‘lib’.

MATLAB: MATLAB Coder: How to setup the environment variables on ARM targets to point to the ARM Compute Library

To avoid build failures on ARM hardware targets, such as the Raspberry Pi and Hikey960, you must set the necessary environment variables non-interactively.

For example, with the ComputeLibrary folder installed under ~, the user home directory, and the ARM Compute libraries stored at ~/ComputeLibrary/lib, you can add this code block to the file ~/.bashrc:

#Set below block, if you use your hardware non-interactively (through ssh from remote host or tty/new terminal onto local machine)
case $- in 
    *i*) ;; 
      *) 
      export ARM_COMPUTELIB=~/ComputeLibrary 
      export PATH=${PATH}:~/ComputeLibrary 
      export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:~/ComputeLibrary/lib
      return;; 
esac

Best Answer

Related Solutions

MATLAB: MATLAB Coder: How to build the ARM Compute Library for Deep Learning C++ code generation and deployment

MATLAB: MATLAB Coder: How to setup the environment variables on ARM targets to point to the ARM Compute Library

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