#OpenVINO Ubuntu Xenial, Virtualbox and Vagrant Install, Intel NCS2 (Neural Compute Stick 2)
Prerequsites
Download Latest VirtualBox from [https://www.virtualbox.org/wiki/Downloads] https://www.virtualbox.org/wiki/Downloads
Make sure to download extension pack
Oracle VM VirtualBox Extension Pack
Download and Install Vagrant from[https://www.vagrantup.com/downloads.html]https://www.vagrantup.com/downloads.html
First step is to setup VM.
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vagrant up |
Vagrant is configured to apply all usb filters required to access Neural Compute Stick 1 and 2 inside Virtualbox Ubuntu Xenial VM
This will create VM on your host machine with name “OpenVinoVM”
This will also automatically download OpenVINO to /home/vagrant/openvino
Setup
Install OpenVINO Dependencies
cd /home/vagrant/openvino/l_openvino_toolkit_p_2019.1.094/ && sudo ./install_openvino_dependencies.sh
Install OpenVINO
cd /home/vagrant/openvino/l_openvino_toolkit_p_2019.1.094/ && sudo ./install.sh
This will have multiple manual steps like accepting license and selecting kind of installation
Default installation path the download configured in Vagrantfile
/opt/intel/openvino_2019.1.094
Setup Vars
source /opt/intel/openvino_2019.1.094/bin/setupvars.sh
echo “source /opt/intel/openvino_2019.1.094/bin/setupvars.sh” >> /home/vagrant/.bashrc
Install UDEV Rules
THese are required for USB to be activated and used
sh/opt/intel/openvino_2019.1.094/install_dependencies/install_NCS_udev_ru es.sh
Update UDEV Rules are below
sudo vi /etc/udev/rules.d/97-myriad-usbboot.rules
I modified MODE to 666 from 660. OpenVINO has 660 as default.
SUBSYSTEM==“usb”, ATTRS{idProduct}“2150”, ATTRS{idVendor}“03e7”, GROUP=“users”, MODE=“0666”, ENV{ID_MM_DEVICE_IGNORE}=“1”
SUBSYSTEM==“usb”, ATTRS{idProduct}“2485”, ATTRS{idVendor}“03e7”, GROUP=“users”, MODE=“0666”, ENV{ID_MM_DEVICE_IGNORE}=“1”
SUBSYSTEM==“usb”, ATTRS{idProduct}“f63b”, ATTRS{idVendor}“03e7”, GROUP=“users”, MODE=“0666”, ENV{ID_MM_DEVICE_IGNORE}=“1”
Reload UDEV
sudo udevadm control –reload-rules && sudo udevadm trigger && sudo ldconfig
Configure Model Optimizer
cd /opt/intel/openvino_2019.1.094/deployment_tools/model_optimizer/install_prerequisites && sudo ./install_prerequisites.sh
If you dont want to run optimizer for all different kind of model you can choose specific optimizer
Example: sudo ./install_prerequisites_caffe.sh ( For Caffe Model)
Example: sudo ./install_prerequisites_tf.sh ( For Tensorflow Model)
Verify if USB is attached
Type lsusb
You should see some USB device with vendor id like
Bus 002 Device 002: ID 03e7:2485
Finally Test
cd /opt/intel/openvino_2019.1.094/deployment_tools/demo && ./demo_squeezenet_download_convert_run.sh
This should print something like this
Image /opt/intel/openvino_2019.1.094/deployment_tools/demo/car.png
| classid | probability | label |
|---|---|---|
| 817 | 0.8363345 | sports car, sport car |
| 511 | 0.0946488 | convertible |
| 479 | 0.0419131 | car wheel |
| 751 | 0.0091071 | racer, race car, racing car |
| 436 | 0.0068161 | beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon |
| 656 | 0.0037564 | minivan |
| 586 | 0.0025741 | half track |
| 717 | 0.0016069 | pickup, pickup truck |
| 864 | 0.0012027 | tow truck, tow car, wrecker |
| 581 | 0.0005882 | grille, radiator grille |
total inference time: 11.7261708
Average running time of one iteration: 11.7261708 ms
Throughput: 85.2793311 FPS
Downloading Public Model and Running Test
cd /opt/intel/openvino_2019.1.094/deployment_tools/tools/model_downloader/
List public models that are known to work with OpenVINO
python3 downloader.py –print_all
Download a specific model, say GoogLeNet V2
Make sure vagrant has access to folder
sudo chmod -R 777 /opt/intel/openvino_2019.1.094/deployment_tools/tools/model_downloader/
python3 downloader.py –name googlenet-v2
Convert Pretained Model to IR ( Intermediate Represenation. This can run on multiple hardware)
cd /opt/intel/openvino_2019.1.094/deployment_tools/tools/model_downloader/classification/googlenet/v2/caffe
Use model optimizer to convert googlenet.caffemodel to IR
/opt/intel/openvino_2019.1.094/deployment_tools/model_optimizer/mo.py –data_type FP16 –input_model googlenet-v2.caffemodel –input_proto googlenet-v2.prototxt
Deploy the converted IR model onto Intel NCS 2 using the toolkit’s IE API
cd /opt/intel/openvino_2019.1.094/deployment_tools/inference_engine/samples/python_samples
Download a test image from the internet
sudo wget -Nhttps://upload.wikimedia.org/wikipedia/commons/b/b6/Felis_catus-cat_on_snow.jpg
Run an inference on this image using a built-in sample code
python3 classification_sample/classification_sample.py -m /opt/intel/openvino_2019.1.094/deployment_tools/tools/model_downloader/classification/googlenet/v2/caffe/./googlenet-v2.xml -i Felis_catus-cat_on_snow.jpg -d MYRIAD
This should give results like
Image Felis_catus-cat_on_snow.jpg
| classid | probability |
|---|---|
| 173 | 0.4843750 |
| 54 | 0.2985840 |
| 7 | 0.1647949 |
| 200 | 0.0359497 |
| 66 | 0.0035839 |
| 10 | 0.0024872 |
| 473 | 0.0024281 |
| 84 | 0.0016794 |
| 198 | 0.0014591 |
| 152 | 0.0006762 |
Look at [GIST]https://gist.github.com/ashwinrayaprolu1984/7245a37b86e5fd1920f8e4409e276132
Next write up will be on Image Classification Using OpenCV and OpenVINO
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Published on System Code Geeks with permission by Ashwin Kumar, partner at our SCG program. See the original article here: OpenVINO Ubuntu Xenial, Virtualbox and Vagrant Install, Intel NCS2 (Neural Compute Stick 2) Opinions expressed by System Code Geeks contributors are their own. |
