Openvino ncs2

1. OPENVINO™
Open Visual Inference & Neural Network Optimization Toolkit

2. • 將Intel® Movidius™視覺處理單元(VPU)整合在USB裝置上
• 這樣就可對數百萬種低功耗的嵌入式裝置提供視覺智能
• 例如監控攝影機、可用手勢控制的無人機、工業級機器視覺設備等
• 針對低功耗應用所設計適合嵌入式系統 e.g. Raspberry Pi
• 可直接自裝置上執行即時的深度神經網路，讓 AI 應用能夠離線部署
Intel® Movidius™
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3. Intel NCS的SDK : OpenVINO
OpenVINO 主要應用為Inference/Predict，Model須在其它框
架如TensorFlow, Caffe,….訓練好才能使用
OpenVINO 簡介
OpenVINO 除了可提供硬體
加速外，更提供模型優化器
(Model Optimizer)，可去除
模型中的冗餘參數，並可降
階到16 bit float 以犧牲數個
小數來換取推論速度提升數
十倍到百倍
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4. 工具包整合了 OpenCV, OpenVX, OpenCL 等開源軟體工具
硬體加速晶片支援 CPU, GPU, FPGA, ASIC (IPU、VPU)等
OS支援 Windows, Liunx (Ubuntu, CentOS…) , Raspbian 等
支援 Caffe, TensorFlow, Mxnet, ONNX 等深度學習框架已
訓練好的模型及參數
• 需要轉出二個中間表示(Intermediate Representation、IR)檔案
(*.bin, *.xml)，再交給推論引擎(Inference Engine)依指定的加速硬
體(CPU、GPU、FPGA、ASIC)來進行預測
OpenVINO 簡介
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5. Enables CNN-based deep learning inference on the
edge
Supports heterogeneous execution across Intel® CPU,
Intel® Integrated Graphics, Intel® Movidius™ Neural
Compute Stick, Intel® Neural Compute Stick 2, and
Intel® Vision Accelerator Design with Intel® Movidius™
VPUs
Speeds time-to-market via an easy-to-use library of
computer vision functions and pre-optimized kernels
Includes optimized calls for computer vision standards
including OpenCV*, OpenCL™, and OpenVX*
OpenVINO™ toolkit
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6. OpenVINO™ toolkit Workflow
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The NCSDK only supports the original NCS.
The OpenVINO™ toolkit supports the Intel® NCS 2 and the original NCS.

7. OpenVINO™ toolkit Workflow
.bin
.xml
Model optimizer
Inference Engine
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8. 12

9. Run OpenVINO Sample Application on Raspberry Pi
安裝 OpenVINO
toolkit 並設定環
境變數
準備好 pre-trained
Model
(xml, bin)
執行應用程式
.xml: Describes the network topology
.bin: Contains the weights and biases binary data
https://docs.openvinotoolkit.org/latest/_docs_install_guides_i
nstalling_openvino_raspbian.html
編譯Sample
Application
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10. #下載並解壓縮OpenVINO工具包
 wget
https://download.01.org/opencv/2019/openvinotoolkit/l_openvino_toolkit_raspbi_p_2019.1.094
.tgz
 tar -xvf l_openvino_toolkit_raspbi_p_2019.1.094.tgz
#設定環境變數
 sed -i "s|<INSTALLDIR>|$(pwd)/inference_engine_vpu_arm|"
inference_engine_vpu_arm/bin/setupvars.sh
 source inference_engine_vpu_arm/bin/setupvars.sh
(完成後顯示：[setupvars.sh] OpenVINO environment initialized)
 echo “source ~/inference_engine_vpu_arm /bin/setupvars.sh” >> ~/.bashrc (每次登入即自動
初始化環境)
#設定USB規則 (完成後即可插入運算棒)
 sh inference_engine_vpu_arm/install_dependencies/install_NCS_udev_rules.sh
(完成後顯示：[install_NCS_udev_rules.sh] udev rules installed)
在 Raspbian 9 安裝 OpenVINO toolkit (Pi 3/Pi 3+)
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11. # 切到sample code路徑
 cd
~/inference_engine_vpu_arm/deployment_tools/inference_engine/samples
 mkdir build && cd build
#編譯OpenVINO Face Detection demo (須事前確認有安裝cmake)
 cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-
march=armv7-a"
 make -j2 object_detection_sample_ssd
#下載pre-trained model (.xml, .bin)
 wget –no-check-certificate
https://download.01.org/openvinotoolkit/2018_R4/open_model_zoo/face-
detection-adas-0001/FP16/face-detection-adas-0001.bin
 wget –no-check-certificate
https://download.01.org/openvinotoolkit/2018_R4/open_model_zoo/face-
detection-adas-0001/FP16/face-detection-adas-0001.xml
OpenVINO : Face recognition sample測試
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12. #執行程式
./armv7l/Release/object_detection_sample_ssd -m face-
detection-adas-0001.xml -d MYRIAD -i <YOURIMG>
系統會出現 [ INFO ] Image out_0.bmp created! 訊息，即可察看結果檔
案:out_0.bmp (右下圖所示)
執行結果
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