결론(?)은 팀 탐색기에서 하면 됨.

 

[링크 : https://ssu-gongdoli.tistory.com/47]

[링크 : http://euhyeji.blogspot.com/2019/08/github-2.html]

Posted by 구차니

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프로그램 사용/xournal2020. 10. 22. 13:56

ubuntu 18.04를 쓰는데

우분투에서 관리하는 xournal++은 프린트가 안되는 문제가 있다.

Version 1.0.16버전에서는 프린트시 파일로 저장하기 밖에 안뜨는데

PPA로 설치하니 1.1.0+dev 버전으로 설치되고 프린트에 정상적으로 목록이 출력된다.

 

[링크 : https://xournalpp.github.io/installation/]

 

짧은 영어로 올린다고 힘들었는데 너무 싱겁게 해결!

[링크 : https://github.com/xournalpp/xournalpp/issues/2329]

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Posted by 구차니

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dmesg 결과는 아래와 같은데.. 얘도 google coral 처럼 어떠한 장치명으로 붙는건 아닌듯

[ 1986.033911] usb 2-1.3: new high-speed USB device number 6 using ehci-pci
[ 1986.143012] usb 2-1.3: New USB device found, idVendor=03e7, idProduct=2485, bcdDevice= 0.01
[ 1986.143019] usb 2-1.3: New USB device strings: Mfr=1, Product=2, SerialNumber=3
[ 1986.143023] usb 2-1.3: Product: Movidius MyriadX
[ 1986.143027] usb 2-1.3: Manufacturer: Movidius Ltd.
[ 1986.143030] usb 2-1.3: SerialNumber: 00000000

 

coral 보다 더 성의가 없는데? 아예 이름이 없어!

$ lsusb
Bus 002 Device 004: ID 04f2:b242 Chicony Electronics Co., Ltd 
Bus 002 Device 006: ID 03e7:2485  
Bus 002 Device 005: ID 04e8:6860 Samsung Electronics Co., Ltd Galaxy (MTP)
Bus 002 Device 002: ID 8087:0024 Intel Corp. Integrated Rate Matching Hub
Bus 002 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub
Bus 001 Device 003: ID 04b4:6560 Cypress Semiconductor Corp. CY7C65640 USB-2.0 "TetraHub"
Bus 001 Device 002: ID 8087:0024 Intel Corp. Integrated Rate Matching Hub
Bus 001 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub

 

UNCLAINMED 라고 나오는건 어떤 특성인가? coral과 다르게 serial이 실제로 출력된다.

$ sudo lshw
                 *-usb:1 UNCLAIMED
                      description: Generic USB device
                      product: Movidius MyriadX
                      vendor: Movidius Ltd.
                      physical id: 3
                      bus info: usb@2:1.3
                      version: 0.01
                      serial: 00000000
                      capabilities: usb-2.00
                      configuration: maxpower=500mA speed=480Mbit/s

 

하라는대로 설치하는데 에러가 난다 -_-

tensorflow 버전 1.15.2 에서 3.8을 요구하는데 python이 3.6.9 버전밖에 안되다 보니 중단된다.

/opt/intel/openvino_2021/install_dependencies$ ./install_NCS_udev_rules.sh 
Updating udev rules...
Udev rules have been successfully installed.
/opt/intel/openvino_2021/install_dependencies$ cd ../deployment_tools/model_optimizer/install_prerequisites/
/opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites$ ./install_prerequisites
install_prerequisites.sh        install_prerequisites_onnx.sh
install_prerequisites_caffe.sh  install_prerequisites_tf.sh
install_prerequisites_kaldi.sh  install_prerequisites_tf2.sh
install_prerequisites_mxnet.sh  
/opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites$ ./install_prerequisites.sh
기존:1 https://apt.repos.intel.com/openvino/2021 all InRelease
기존:2 https://packages.microsoft.com/repos/vscode stable InRelease            
기존:3 http://apt.postgresql.org/pub/repos/apt bionic-pgdg InRelease           
기존:4 http://dl.openfoam.org/ubuntu bionic InRelease                          
기존:6 http://kr.archive.ubuntu.com/ubuntu bionic InRelease                    
받기:7 https://packages.cloud.google.com/apt coral-edgetpu-stable InRelease [6332 B]
받기:5 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]   
받기:8 http://kr.archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]  
받기:10 http://security.ubuntu.com/ubuntu bionic-security/main amd64 DEP-11 Metadata [48.9 kB]
받기:11 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 DEP-11 Metadata [58.8 kB]
받기:12 http://security.ubuntu.com/ubuntu bionic-security/multiverse amd64 DEP-11 Metadata [2464 B]
받기:9 http://kr.archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]
받기:13 http://kr.archive.ubuntu.com/ubuntu bionic-updates/main amd64 DEP-11 Metadata [295 kB]
받기:14 http://kr.archive.ubuntu.com/ubuntu bionic-updates/universe amd64 DEP-11 Metadata [287 kB]
받기:15 http://kr.archive.ubuntu.com/ubuntu bionic-updates/multiverse amd64 DEP-11 Metadata [2464 B]
받기:16 http://kr.archive.ubuntu.com/ubuntu bionic-backports/universe amd64 DEP-11 Metadata [9288 B]
내려받기 468 k바이트, 소요시간 10초 (46.4 k바이트/초)                          
패키지 목록을 읽는 중입니다... 완료
의존성 트리를 만드는 중입니다       
상태 정보를 읽는 중입니다... 완료
8 packages can be upgraded. Run 'apt list --upgradable' to see them.
패키지 목록을 읽는 중입니다... 완료
의존성 트리를 만드는 중입니다       
상태 정보를 읽는 중입니다... 완료
패키지 python3-pip는 이미 최신 버전입니다 (9.0.1-2.3~ubuntu1.18.04.3).
다음 패키지가 자동으로 설치되었지만 더 이상 필요하지 않습니다:
  linux-headers-5.4.0-48-generic linux-hwe-5.4-headers-5.4.0-48
  linux-image-5.4.0-48-generic linux-modules-5.4.0-48-generic
  linux-modules-extra-5.4.0-48-generic
Use 'sudo apt autoremove' to remove them.
다음 새 패키지를 설치할 것입니다:
  libgfortran5 python3-venv python3.6-venv
0개 업그레이드, 3개 새로 설치, 0개 제거 및 8개 업그레이드 안 함.
596 k바이트 아카이브를 받아야 합니다.
이 작업 후 2704 k바이트의 디스크 공간을 더 사용하게 됩니다.
받기:1 http://kr.archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libgfortran5 amd64 8.4.0-1ubuntu1~18.04 [589 kB]
받기:2 http://kr.archive.ubuntu.com/ubuntu bionic-updates/universe amd64 python3.6-venv amd64 3.6.9-1~18.04ubuntu1.3 [6180 B]
받기:3 http://kr.archive.ubuntu.com/ubuntu bionic-updates/universe amd64 python3-venv amd64 3.6.7-1~18.04 [1208 B]
내려받기 596 k바이트, 소요시간 4초 (159 k바이트/초)
Selecting previously unselected package libgfortran5:amd64.
(데이터베이스 읽는중 ...현재 326645개의 파일과 디렉터리가 설치되어 있습니다.)
Preparing to unpack .../libgfortran5_8.4.0-1ubuntu1~18.04_amd64.deb ...
Unpacking libgfortran5:amd64 (8.4.0-1ubuntu1~18.04) ...
Selecting previously unselected package python3.6-venv.
Preparing to unpack .../python3.6-venv_3.6.9-1~18.04ubuntu1.3_amd64.deb ...
Unpacking python3.6-venv (3.6.9-1~18.04ubuntu1.3) ...
Selecting previously unselected package python3-venv.
Preparing to unpack .../python3-venv_3.6.7-1~18.04_amd64.deb ...
Unpacking python3-venv (3.6.7-1~18.04) ...
python3.6-venv (3.6.9-1~18.04ubuntu1.3) 설정하는 중입니다 ...
libgfortran5:amd64 (8.4.0-1ubuntu1~18.04) 설정하는 중입니다 ...
python3-venv (3.6.7-1~18.04) 설정하는 중입니다 ...
Processing triggers for man-db (2.8.3-2ubuntu0.1) ...
Processing triggers for libc-bin (2.27-3ubuntu1.2) ...
The directory '/home/minimonk/.cache/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
The directory '/home/minimonk/.cache/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
Ignoring tensorflow: markers 'python_version >= "3.8"' don't match your environment
Collecting tensorflow<2.0,>=1.15.2 (from -r /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites/../requirements.txt (line 1))
  Could not find a version that satisfies the requirement tensorflow<2.0,>=1.15.2 (from -r /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites/../requirements.txt (line 1)) (from versions: 0.12.1, 1.0.0, 1.0.1, 1.1.0rc0, 1.1.0rc1, 1.1.0rc2, 1.1.0, 1.2.0rc0, 1.2.0rc1, 1.2.0rc2, 1.2.0, 1.2.1, 1.3.0rc0, 1.3.0rc1, 1.3.0rc2, 1.3.0, 1.4.0rc0, 1.4.0rc1, 1.4.0, 1.4.1, 1.5.0rc0, 1.5.0rc1, 1.5.0, 1.5.1, 1.6.0rc0, 1.6.0rc1, 1.6.0, 1.7.0rc0, 1.7.0rc1, 1.7.0, 1.7.1, 1.8.0rc0, 1.8.0rc1, 1.8.0, 1.9.0rc0, 1.9.0rc1, 1.9.0rc2, 1.9.0, 1.10.0rc0, 1.10.0rc1, 1.10.0, 1.10.1, 1.11.0rc0, 1.11.0rc1, 1.11.0rc2, 1.11.0, 1.12.0rc0, 1.12.0rc1, 1.12.0rc2, 1.12.0, 1.12.2, 1.12.3, 1.13.0rc0, 1.13.0rc1, 1.13.0rc2, 1.13.1, 1.13.2, 1.14.0rc0, 1.14.0rc1, 1.14.0, 2.0.0a0, 2.0.0b0, 2.0.0b1)
No matching distribution found for tensorflow<2.0,>=1.15.2 (from -r /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites/../requirements.txt (line 1))
Error on or near line 92; exiting with status 1
/opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites$ ./install_prerequisites.sh
기존:1 https://apt.repos.intel.com/openvino/2021 all InRelease                 
기존:2 http://security.ubuntu.com/ubuntu bionic-security InRelease             
기존:3 http://apt.postgresql.org/pub/repos/apt bionic-pgdg InRelease           
기존:4 https://packages.microsoft.com/repos/vscode stable InRelease            
기존:5 https://packages.cloud.google.com/apt coral-edgetpu-stable InRelease    
기존:6 http://kr.archive.ubuntu.com/ubuntu bionic InRelease                    
기존:7 http://dl.openfoam.org/ubuntu bionic InRelease                          
기존:8 http://kr.archive.ubuntu.com/ubuntu bionic-updates InRelease            
기존:9 http://kr.archive.ubuntu.com/ubuntu bionic-backports InRelease
패키지 목록을 읽는 중입니다... 완료     
의존성 트리를 만드는 중입니다       
상태 정보를 읽는 중입니다... 완료
8 packages can be upgraded. Run 'apt list --upgradable' to see them.
패키지 목록을 읽는 중입니다... 완료
의존성 트리를 만드는 중입니다       
상태 정보를 읽는 중입니다... 완료
패키지 libgfortran5는 이미 최신 버전입니다 (8.4.0-1ubuntu1~18.04).
패키지 python3-pip는 이미 최신 버전입니다 (9.0.1-2.3~ubuntu1.18.04.3).
패키지 python3-venv는 이미 최신 버전입니다 (3.6.7-1~18.04).
다음 패키지가 자동으로 설치되었지만 더 이상 필요하지 않습니다:
  linux-headers-5.4.0-48-generic linux-hwe-5.4-headers-5.4.0-48
  linux-image-5.4.0-48-generic linux-modules-5.4.0-48-generic
  linux-modules-extra-5.4.0-48-generic
Use 'sudo apt autoremove' to remove them.
0개 업그레이드, 0개 새로 설치, 0개 제거 및 8개 업그레이드 안 함.
The directory '/home/minimonk/.cache/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
The directory '/home/minimonk/.cache/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
Ignoring tensorflow: markers 'python_version >= "3.8"' don't match your environment
Collecting tensorflow<2.0,>=1.15.2 (from -r /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites/../requirements.txt (line 1))
  Could not find a version that satisfies the requirement tensorflow<2.0,>=1.15.2 (from -r /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites/../requirements.txt (line 1)) (from versions: 0.12.1, 1.0.0, 1.0.1, 1.1.0rc0, 1.1.0rc1, 1.1.0rc2, 1.1.0, 1.2.0rc0, 1.2.0rc1, 1.2.0rc2, 1.2.0, 1.2.1, 1.3.0rc0, 1.3.0rc1, 1.3.0rc2, 1.3.0, 1.4.0rc0, 1.4.0rc1, 1.4.0, 1.4.1, 1.5.0rc0, 1.5.0rc1, 1.5.0, 1.5.1, 1.6.0rc0, 1.6.0rc1, 1.6.0, 1.7.0rc0, 1.7.0rc1, 1.7.0, 1.7.1, 1.8.0rc0, 1.8.0rc1, 1.8.0, 1.9.0rc0, 1.9.0rc1, 1.9.0rc2, 1.9.0, 1.10.0rc0, 1.10.0rc1, 1.10.0, 1.10.1, 1.11.0rc0, 1.11.0rc1, 1.11.0rc2, 1.11.0, 1.12.0rc0, 1.12.0rc1, 1.12.0rc2, 1.12.0, 1.12.2, 1.12.3, 1.13.0rc0, 1.13.0rc1, 1.13.0rc2, 1.13.1, 1.13.2, 1.14.0rc0, 1.14.0rc1, 1.14.0, 2.0.0a0, 2.0.0b0, 2.0.0b1)
No matching distribution found for tensorflow<2.0,>=1.15.2 (from -r /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites/../requirements.txt (line 1))
Error on or near line 92; exiting with status 1

 

그래서 로 직접 설치하니 1.14 였나 1.15 였나 약간 낮은 버전이 설치 되면서 넘어간다 (ubuntu 18.04.5 LTS)

$ pip3 install tensorflow

 

그래서 데모를 실행하는데 일단 에러!

에러를 봐서는 "Can not init Myriad device: NC_ERROR" 장치가 연결되지 않아서 그런것 같다.

 

~$ cd /opt/intel/openvino_2021/deployment_tools/demo/
/opt/intel/openvino_2021/deployment_tools/demo$ ./demo_squeezenet_download_convert_run.sh -d MYRIAD
target = MYRIAD
target_precision = FP16
[setupvars.sh] OpenVINO environment initialized


###################################################



Downloading the Caffe model and the prototxt
Installing dependencies
기존:1 https://apt.repos.intel.com/openvino/2021 all InRelease
기존:2 https://packages.microsoft.com/repos/vscode stable InRelease            
기존:3 http://apt.postgresql.org/pub/repos/apt bionic-pgdg InRelease           
받기:4 https://packages.cloud.google.com/apt coral-edgetpu-stable InRelease [6332 B]
기존:5 http://kr.archive.ubuntu.com/ubuntu bionic InRelease                    
기존:6 http://security.ubuntu.com/ubuntu bionic-security InRelease             
기존:7 http://dl.openfoam.org/ubuntu bionic InRelease                          
기존:8 http://kr.archive.ubuntu.com/ubuntu bionic-updates InRelease            
기존:9 http://kr.archive.ubuntu.com/ubuntu bionic-backports InRelease
내려받기 6332 바이트, 소요시간 2초 (3924 바이트/초)
패키지 목록을 읽는 중입니다... 완료
의존성 트리를 만드는 중입니다       
상태 정보를 읽는 중입니다... 완료
8 packages can be upgraded. Run 'apt list --upgradable' to see them.
Run sudo -E apt -y install build-essential python3-pip virtualenv cmake libcairo2-dev libpango1.0-dev libglib2.0-dev libgtk2.0-dev libswscale-dev libavcodec-dev libavformat-dev libgstreamer1.0-0 gstreamer1.0-plugins-base

패키지 목록을 읽는 중입니다... 완료
의존성 트리를 만드는 중입니다       
상태 정보를 읽는 중입니다... 완료
패키지 build-essential는 이미 최신 버전입니다 (12.4ubuntu1).
패키지 cmake는 이미 최신 버전입니다 (3.10.2-1ubuntu2.18.04.1).
패키지 gstreamer1.0-plugins-base는 이미 최신 버전입니다 (1.14.5-0ubuntu1~18.04.1).
gstreamer1.0-plugins-base 패키지는 수동설치로 지정합니다.
패키지 libgstreamer1.0-0는 이미 최신 버전입니다 (1.14.5-0ubuntu1~18.04.1).
libgstreamer1.0-0 패키지는 수동설치로 지정합니다.
패키지 python3-pip는 이미 최신 버전입니다 (9.0.1-2.3~ubuntu1.18.04.3).
다음 패키지가 자동으로 설치되었지만 더 이상 필요하지 않습니다:
  linux-headers-5.4.0-48-generic linux-hwe-5.4-headers-5.4.0-48
  linux-image-5.4.0-48-generic linux-modules-5.4.0-48-generic
  linux-modules-extra-5.4.0-48-generic
Use 'sudo apt autoremove' to remove them.
다음의 추가 패키지가 설치될 것입니다 :
  gir1.2-gtk-2.0 gir1.2-harfbuzz-0.0 icu-devtools libatk1.0-dev libavutil-dev
  libcairo-script-interpreter2 libfontconfig1-dev libfreetype6-dev
  libgdk-pixbuf2.0-dev libglib2.0-dev-bin libgraphite2-dev libharfbuzz-dev
  libharfbuzz-gobject0 libicu-dev libicu-le-hb-dev libicu-le-hb0 libiculx60
  libpcre16-3 libpcre3-dev libpcre32-3 libpcrecpp0v5 libpixman-1-dev
  libpng-dev libpng-tools libswresample-dev libxcb-shm0-dev libxcomposite-dev
  libxcursor-dev libxft-dev libxinerama-dev libxml2-utils libxrandr-dev
  libxrender-dev python3-virtualenv x11proto-composite-dev x11proto-randr-dev
  x11proto-xinerama-dev
제안하는 패키지:
  libcairo2-doc libglib2.0-doc libgraphite2-utils libgtk2.0-doc icu-doc
  libpango1.0-doc
다음 새 패키지를 설치할 것입니다:
  gir1.2-gtk-2.0 gir1.2-harfbuzz-0.0 icu-devtools libatk1.0-dev libavcodec-dev
  libavformat-dev libavutil-dev libcairo-script-interpreter2 libcairo2-dev
  libfontconfig1-dev libfreetype6-dev libgdk-pixbuf2.0-dev libglib2.0-dev
  libglib2.0-dev-bin libgraphite2-dev libgtk2.0-dev libharfbuzz-dev
  libharfbuzz-gobject0 libicu-dev libicu-le-hb-dev libicu-le-hb0 libiculx60
  libpango1.0-dev libpcre16-3 libpcre3-dev libpcre32-3 libpcrecpp0v5
  libpixman-1-dev libpng-dev libpng-tools libswresample-dev libswscale-dev
  libxcb-shm0-dev libxcomposite-dev libxcursor-dev libxft-dev libxinerama-dev
  libxml2-utils libxrandr-dev libxrender-dev python3-virtualenv virtualenv
  x11proto-composite-dev x11proto-randr-dev x11proto-xinerama-dev
0개 업그레이드, 45개 새로 설치, 0개 제거 및 8개 업그레이드 안 함.
26.4 M바이트 아카이브를 받아야 합니다.
이 작업 후 123 M바이트의 디스크 공간을 더 사용하게 됩니다.
받기:1 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 gir1.2-gtk-2.0 amd64 2.24.32-1ubuntu1 [172 kB]
받기:2 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 gir1.2-harfbuzz-0.0 amd64 1.7.2-1ubuntu1 [18.6 kB]
받기:3 http://kr.archive.ubuntu.com/ubuntu bionic-updates/main amd64 icu-devtools amd64 60.2-3ubuntu3.1 [179 kB]
받기:4 http://kr.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libglib2.0-dev-bin amd64 2.56.4-0ubuntu0.18.04.6 [102 kB]
받기:5 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libpcre16-3 amd64 2:8.39-9 [147 kB]
받기:6 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libpcre32-3 amd64 2:8.39-9 [138 kB]
받기:7 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libpcrecpp0v5 amd64 2:8.39-9 [15.3 kB]
받기:8 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libpcre3-dev amd64 2:8.39-9 [537 kB]
받기:9 http://kr.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libglib2.0-dev amd64 2.56.4-0ubuntu0.18.04.6 [1385 kB]
받기:10 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libatk1.0-dev amd64 2.28.1-1 [79.9 kB]
받기:11 http://kr.archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libavutil-dev amd64 7:3.4.8-0ubuntu0.2 [294 kB]
받기:12 http://kr.archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libswresample-dev amd64 7:3.4.8-0ubuntu0.2 [68.7 kB]
받기:13 http://kr.archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libavcodec-dev amd64 7:3.4.8-0ubuntu0.2 [5079 kB]
받기:14 http://kr.archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libavformat-dev amd64 7:3.4.8-0ubuntu0.2 [1132 kB]
받기:15 http://kr.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libcairo-script-interpreter2 amd64 1.15.10-2ubuntu0.1 [53.5 kB]
받기:16 http://kr.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libpng-dev amd64 1.6.34-1ubuntu0.18.04.2 [177 kB]
받기:17 http://kr.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libfreetype6-dev amd64 2.8.1-2ubuntu2.1 [2539 kB]
받기:18 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libfontconfig1-dev amd64 2.12.6-0ubuntu2 [689 kB]
받기:19 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libxrender-dev amd64 1:0.9.10-1 [24.9 kB]
받기:20 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libpixman-1-dev amd64 0.34.0-2 [244 kB]
받기:21 http://kr.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libxcb-shm0-dev amd64 1.13-2~ubuntu18.04 [6684 B]
받기:22 http://kr.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libcairo2-dev amd64 1.15.10-2ubuntu0.1 [626 kB]
받기:23 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libgdk-pixbuf2.0-dev amd64 2.36.11-2 [46.8 kB]
받기:24 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libgraphite2-dev amd64 1.3.11-2 [14.5 kB]
받기:25 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libharfbuzz-gobject0 amd64 1.7.2-1ubuntu1 [13.4 kB]
받기:26 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libicu-le-hb0 amd64 1.0.3+git161113-4 [14.3 kB]
받기:27 http://kr.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libiculx60 amd64 60.2-3ubuntu3.1 [19.0 kB]
받기:28 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libicu-le-hb-dev amd64 1.0.3+git161113-4 [29.5 kB]
받기:29 http://kr.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libicu-dev amd64 60.2-3ubuntu3.1 [8889 kB]
받기:30 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libharfbuzz-dev amd64 1.7.2-1ubuntu1 [302 kB]
받기:31 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libxft-dev amd64 2.3.2-1 [45.7 kB]
받기:32 http://kr.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libpango1.0-dev amd64 1.40.14-1ubuntu0.1 [288 kB]
받기:33 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 x11proto-xinerama-dev all 2018.4-4 [2628 B]
받기:34 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libxinerama-dev amd64 2:1.1.3-1 [8404 B]
받기:35 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 x11proto-randr-dev all 2018.4-4 [2620 B]
받기:36 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libxrandr-dev amd64 2:1.5.1-1 [24.0 kB]
받기:37 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libxcursor-dev amd64 1:1.1.15-1 [26.5 kB]
받기:38 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 x11proto-composite-dev all 1:2018.4-4 [2620 B]
받기:39 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libxcomposite-dev amd64 1:0.4.4-2 [9136 B]
받기:40 http://kr.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libxml2-utils amd64 2.9.4+dfsg1-6.1ubuntu1.3 [35.9 kB]
받기:41 http://kr.archive.ubuntu.com/ubuntu bionic/main amd64 libgtk2.0-dev amd64 2.24.32-1ubuntu1 [2652 kB]
받기:42 http://kr.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libpng-tools amd64 1.6.34-1ubuntu0.18.04.2 [25.6 kB]
받기:43 http://kr.archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libswscale-dev amd64 7:3.4.8-0ubuntu0.2 [166 kB]
받기:44 http://kr.archive.ubuntu.com/ubuntu bionic/universe amd64 python3-virtualenv all 15.1.0+ds-1.1 [43.4 kB]
받기:45 http://kr.archive.ubuntu.com/ubuntu bionic/universe amd64 virtualenv all 15.1.0+ds-1.1 [4476 B]
내려받기 26.4 M바이트, 소요시간 1분 4초 (412 k바이트/초)                       
패키지에서 템플릿을 추출하는 중: 100%
Selecting previously unselected package gir1.2-gtk-2.0.
(데이터베이스 읽는중 ...현재 326659개의 파일과 디렉터리가 설치되어 있습니다.)
Preparing to unpack .../00-gir1.2-gtk-2.0_2.24.32-1ubuntu1_amd64.deb ...
Unpacking gir1.2-gtk-2.0 (2.24.32-1ubuntu1) ...
Selecting previously unselected package gir1.2-harfbuzz-0.0:amd64.
Preparing to unpack .../01-gir1.2-harfbuzz-0.0_1.7.2-1ubuntu1_amd64.deb ...
Unpacking gir1.2-harfbuzz-0.0:amd64 (1.7.2-1ubuntu1) ...
Selecting previously unselected package icu-devtools.
Preparing to unpack .../02-icu-devtools_60.2-3ubuntu3.1_amd64.deb ...
Unpacking icu-devtools (60.2-3ubuntu3.1) ...
Selecting previously unselected package libglib2.0-dev-bin.
Preparing to unpack .../03-libglib2.0-dev-bin_2.56.4-0ubuntu0.18.04.6_amd64.deb ...
Unpacking libglib2.0-dev-bin (2.56.4-0ubuntu0.18.04.6) ...
Selecting previously unselected package libpcre16-3:amd64.
Preparing to unpack .../04-libpcre16-3_2%3a8.39-9_amd64.deb ...
Unpacking libpcre16-3:amd64 (2:8.39-9) ...
Selecting previously unselected package libpcre32-3:amd64.
Preparing to unpack .../05-libpcre32-3_2%3a8.39-9_amd64.deb ...
Unpacking libpcre32-3:amd64 (2:8.39-9) ...
Selecting previously unselected package libpcrecpp0v5:amd64.
Preparing to unpack .../06-libpcrecpp0v5_2%3a8.39-9_amd64.deb ...
Unpacking libpcrecpp0v5:amd64 (2:8.39-9) ...
Selecting previously unselected package libpcre3-dev:amd64.
Preparing to unpack .../07-libpcre3-dev_2%3a8.39-9_amd64.deb ...
Unpacking libpcre3-dev:amd64 (2:8.39-9) ...
Selecting previously unselected package libglib2.0-dev:amd64.
Preparing to unpack .../08-libglib2.0-dev_2.56.4-0ubuntu0.18.04.6_amd64.deb ...
Unpacking libglib2.0-dev:amd64 (2.56.4-0ubuntu0.18.04.6) ...
Selecting previously unselected package libatk1.0-dev:amd64.
Preparing to unpack .../09-libatk1.0-dev_2.28.1-1_amd64.deb ...
Unpacking libatk1.0-dev:amd64 (2.28.1-1) ...
Selecting previously unselected package libavutil-dev:amd64.
Preparing to unpack .../10-libavutil-dev_7%3a3.4.8-0ubuntu0.2_amd64.deb ...
Unpacking libavutil-dev:amd64 (7:3.4.8-0ubuntu0.2) ...
Selecting previously unselected package libswresample-dev:amd64.
Preparing to unpack .../11-libswresample-dev_7%3a3.4.8-0ubuntu0.2_amd64.deb ...
Unpacking libswresample-dev:amd64 (7:3.4.8-0ubuntu0.2) ...
Selecting previously unselected package libavcodec-dev:amd64.
Preparing to unpack .../12-libavcodec-dev_7%3a3.4.8-0ubuntu0.2_amd64.deb ...
Unpacking libavcodec-dev:amd64 (7:3.4.8-0ubuntu0.2) ...
Selecting previously unselected package libavformat-dev:amd64.
Preparing to unpack .../13-libavformat-dev_7%3a3.4.8-0ubuntu0.2_amd64.deb ...
Unpacking libavformat-dev:amd64 (7:3.4.8-0ubuntu0.2) ...
Selecting previously unselected package libcairo-script-interpreter2:amd64.
Preparing to unpack .../14-libcairo-script-interpreter2_1.15.10-2ubuntu0.1_amd64.deb ...
Unpacking libcairo-script-interpreter2:amd64 (1.15.10-2ubuntu0.1) ...
Selecting previously unselected package libpng-dev:amd64.
Preparing to unpack .../15-libpng-dev_1.6.34-1ubuntu0.18.04.2_amd64.deb ...
Unpacking libpng-dev:amd64 (1.6.34-1ubuntu0.18.04.2) ...
Selecting previously unselected package libfreetype6-dev:amd64.
Preparing to unpack .../16-libfreetype6-dev_2.8.1-2ubuntu2.1_amd64.deb ...
Unpacking libfreetype6-dev:amd64 (2.8.1-2ubuntu2.1) ...
Selecting previously unselected package libfontconfig1-dev:amd64.
Preparing to unpack .../17-libfontconfig1-dev_2.12.6-0ubuntu2_amd64.deb ...
Unpacking libfontconfig1-dev:amd64 (2.12.6-0ubuntu2) ...
Selecting previously unselected package libxrender-dev:amd64.
Preparing to unpack .../18-libxrender-dev_1%3a0.9.10-1_amd64.deb ...
Unpacking libxrender-dev:amd64 (1:0.9.10-1) ...
Selecting previously unselected package libpixman-1-dev:amd64.
Preparing to unpack .../19-libpixman-1-dev_0.34.0-2_amd64.deb ...
Unpacking libpixman-1-dev:amd64 (0.34.0-2) ...
Selecting previously unselected package libxcb-shm0-dev:amd64.
Preparing to unpack .../20-libxcb-shm0-dev_1.13-2~ubuntu18.04_amd64.deb ...
Unpacking libxcb-shm0-dev:amd64 (1.13-2~ubuntu18.04) ...
Selecting previously unselected package libcairo2-dev:amd64.
Preparing to unpack .../21-libcairo2-dev_1.15.10-2ubuntu0.1_amd64.deb ...
Unpacking libcairo2-dev:amd64 (1.15.10-2ubuntu0.1) ...
Selecting previously unselected package libgdk-pixbuf2.0-dev.
Preparing to unpack .../22-libgdk-pixbuf2.0-dev_2.36.11-2_amd64.deb ...
Unpacking libgdk-pixbuf2.0-dev (2.36.11-2) ...
Selecting previously unselected package libgraphite2-dev:amd64.
Preparing to unpack .../23-libgraphite2-dev_1.3.11-2_amd64.deb ...
Unpacking libgraphite2-dev:amd64 (1.3.11-2) ...
Selecting previously unselected package libharfbuzz-gobject0:amd64.
Preparing to unpack .../24-libharfbuzz-gobject0_1.7.2-1ubuntu1_amd64.deb ...
Unpacking libharfbuzz-gobject0:amd64 (1.7.2-1ubuntu1) ...
Selecting previously unselected package libicu-le-hb0:amd64.
Preparing to unpack .../25-libicu-le-hb0_1.0.3+git161113-4_amd64.deb ...
Unpacking libicu-le-hb0:amd64 (1.0.3+git161113-4) ...
Selecting previously unselected package libiculx60:amd64.
Preparing to unpack .../26-libiculx60_60.2-3ubuntu3.1_amd64.deb ...
Unpacking libiculx60:amd64 (60.2-3ubuntu3.1) ...
Selecting previously unselected package libicu-le-hb-dev:amd64.
Preparing to unpack .../27-libicu-le-hb-dev_1.0.3+git161113-4_amd64.deb ...
Unpacking libicu-le-hb-dev:amd64 (1.0.3+git161113-4) ...
Selecting previously unselected package libicu-dev.
Preparing to unpack .../28-libicu-dev_60.2-3ubuntu3.1_amd64.deb ...
Unpacking libicu-dev (60.2-3ubuntu3.1) ...
Selecting previously unselected package libharfbuzz-dev:amd64.
Preparing to unpack .../29-libharfbuzz-dev_1.7.2-1ubuntu1_amd64.deb ...
Unpacking libharfbuzz-dev:amd64 (1.7.2-1ubuntu1) ...
Selecting previously unselected package libxft-dev.
Preparing to unpack .../30-libxft-dev_2.3.2-1_amd64.deb ...
Unpacking libxft-dev (2.3.2-1) ...
Selecting previously unselected package libpango1.0-dev.
Preparing to unpack .../31-libpango1.0-dev_1.40.14-1ubuntu0.1_amd64.deb ...
Unpacking libpango1.0-dev (1.40.14-1ubuntu0.1) ...
Selecting previously unselected package x11proto-xinerama-dev.
Preparing to unpack .../32-x11proto-xinerama-dev_2018.4-4_all.deb ...
Unpacking x11proto-xinerama-dev (2018.4-4) ...
Selecting previously unselected package libxinerama-dev:amd64.
Preparing to unpack .../33-libxinerama-dev_2%3a1.1.3-1_amd64.deb ...
Unpacking libxinerama-dev:amd64 (2:1.1.3-1) ...
Selecting previously unselected package x11proto-randr-dev.
Preparing to unpack .../34-x11proto-randr-dev_2018.4-4_all.deb ...
Unpacking x11proto-randr-dev (2018.4-4) ...
Selecting previously unselected package libxrandr-dev:amd64.
Preparing to unpack .../35-libxrandr-dev_2%3a1.5.1-1_amd64.deb ...
Unpacking libxrandr-dev:amd64 (2:1.5.1-1) ...
Selecting previously unselected package libxcursor-dev:amd64.
Preparing to unpack .../36-libxcursor-dev_1%3a1.1.15-1_amd64.deb ...
Unpacking libxcursor-dev:amd64 (1:1.1.15-1) ...
Selecting previously unselected package x11proto-composite-dev.
Preparing to unpack .../37-x11proto-composite-dev_1%3a2018.4-4_all.deb ...
Unpacking x11proto-composite-dev (1:2018.4-4) ...
Selecting previously unselected package libxcomposite-dev:amd64.
Preparing to unpack .../38-libxcomposite-dev_1%3a0.4.4-2_amd64.deb ...
Unpacking libxcomposite-dev:amd64 (1:0.4.4-2) ...
Selecting previously unselected package libxml2-utils.
Preparing to unpack .../39-libxml2-utils_2.9.4+dfsg1-6.1ubuntu1.3_amd64.deb ...
Unpacking libxml2-utils (2.9.4+dfsg1-6.1ubuntu1.3) ...
Selecting previously unselected package libgtk2.0-dev.
Preparing to unpack .../40-libgtk2.0-dev_2.24.32-1ubuntu1_amd64.deb ...
Unpacking libgtk2.0-dev (2.24.32-1ubuntu1) ...
Selecting previously unselected package libpng-tools.
Preparing to unpack .../41-libpng-tools_1.6.34-1ubuntu0.18.04.2_amd64.deb ...
Unpacking libpng-tools (1.6.34-1ubuntu0.18.04.2) ...
Selecting previously unselected package libswscale-dev:amd64.
Preparing to unpack .../42-libswscale-dev_7%3a3.4.8-0ubuntu0.2_amd64.deb ...
Unpacking libswscale-dev:amd64 (7:3.4.8-0ubuntu0.2) ...
Selecting previously unselected package python3-virtualenv.
Preparing to unpack .../43-python3-virtualenv_15.1.0+ds-1.1_all.deb ...
Unpacking python3-virtualenv (15.1.0+ds-1.1) ...
Selecting previously unselected package virtualenv.
Preparing to unpack .../44-virtualenv_15.1.0+ds-1.1_all.deb ...
Unpacking virtualenv (15.1.0+ds-1.1) ...
gir1.2-gtk-2.0 (2.24.32-1ubuntu1) 설정하는 중입니다 ...
libavutil-dev:amd64 (7:3.4.8-0ubuntu0.2) 설정하는 중입니다 ...
libglib2.0-dev-bin (2.56.4-0ubuntu0.18.04.6) 설정하는 중입니다 ...
libcairo-script-interpreter2:amd64 (1.15.10-2ubuntu0.1) 설정하는 중입니다 ...
libpng-tools (1.6.34-1ubuntu0.18.04.2) 설정하는 중입니다 ...
libicu-le-hb0:amd64 (1.0.3+git161113-4) 설정하는 중입니다 ...
libxcb-shm0-dev:amd64 (1.13-2~ubuntu18.04) 설정하는 중입니다 ...
libxml2-utils (2.9.4+dfsg1-6.1ubuntu1.3) 설정하는 중입니다 ...
libxrender-dev:amd64 (1:0.9.10-1) 설정하는 중입니다 ...
libswscale-dev:amd64 (7:3.4.8-0ubuntu0.2) 설정하는 중입니다 ...
gir1.2-harfbuzz-0.0:amd64 (1.7.2-1ubuntu1) 설정하는 중입니다 ...
x11proto-xinerama-dev (2018.4-4) 설정하는 중입니다 ...
libpixman-1-dev:amd64 (0.34.0-2) 설정하는 중입니다 ...
libswresample-dev:amd64 (7:3.4.8-0ubuntu0.2) 설정하는 중입니다 ...
x11proto-randr-dev (2018.4-4) 설정하는 중입니다 ...
libxinerama-dev:amd64 (2:1.1.3-1) 설정하는 중입니다 ...
python3-virtualenv (15.1.0+ds-1.1) 설정하는 중입니다 ...
libiculx60:amd64 (60.2-3ubuntu3.1) 설정하는 중입니다 ...
libpcrecpp0v5:amd64 (2:8.39-9) 설정하는 중입니다 ...
libpcre32-3:amd64 (2:8.39-9) 설정하는 중입니다 ...
icu-devtools (60.2-3ubuntu3.1) 설정하는 중입니다 ...
libpcre16-3:amd64 (2:8.39-9) 설정하는 중입니다 ...
libpng-dev:amd64 (1.6.34-1ubuntu0.18.04.2) 설정하는 중입니다 ...
virtualenv (15.1.0+ds-1.1) 설정하는 중입니다 ...
libgraphite2-dev:amd64 (1.3.11-2) 설정하는 중입니다 ...
libharfbuzz-gobject0:amd64 (1.7.2-1ubuntu1) 설정하는 중입니다 ...
x11proto-composite-dev (1:2018.4-4) 설정하는 중입니다 ...
libxcursor-dev:amd64 (1:1.1.15-1) 설정하는 중입니다 ...
libpcre3-dev:amd64 (2:8.39-9) 설정하는 중입니다 ...
libxrandr-dev:amd64 (2:1.5.1-1) 설정하는 중입니다 ...
libxcomposite-dev:amd64 (1:0.4.4-2) 설정하는 중입니다 ...
libavcodec-dev:amd64 (7:3.4.8-0ubuntu0.2) 설정하는 중입니다 ...
libglib2.0-dev:amd64 (2.56.4-0ubuntu0.18.04.6) 설정하는 중입니다 ...
libfreetype6-dev:amd64 (2.8.1-2ubuntu2.1) 설정하는 중입니다 ...
libavformat-dev:amd64 (7:3.4.8-0ubuntu0.2) 설정하는 중입니다 ...
libfontconfig1-dev:amd64 (2.12.6-0ubuntu2) 설정하는 중입니다 ...
libxft-dev (2.3.2-1) 설정하는 중입니다 ...
libicu-le-hb-dev:amd64 (1.0.3+git161113-4) 설정하는 중입니다 ...
libicu-dev (60.2-3ubuntu3.1) 설정하는 중입니다 ...
Processing triggers for libc-bin (2.27-3ubuntu1.2) ...
Processing triggers for man-db (2.8.3-2ubuntu0.1) ...
Processing triggers for libglib2.0-0:amd64 (2.56.4-0ubuntu0.18.04.6) ...
libatk1.0-dev:amd64 (2.28.1-1) 설정하는 중입니다 ...
libgdk-pixbuf2.0-dev (2.36.11-2) 설정하는 중입니다 ...
libharfbuzz-dev:amd64 (1.7.2-1ubuntu1) 설정하는 중입니다 ...
libcairo2-dev:amd64 (1.15.10-2ubuntu0.1) 설정하는 중입니다 ...
libpango1.0-dev (1.40.14-1ubuntu0.1) 설정하는 중입니다 ...
libgtk2.0-dev (2.24.32-1ubuntu1) 설정하는 중입니다 ...
패키지 목록을 읽는 중입니다... 완료
의존성 트리를 만드는 중입니다       
상태 정보를 읽는 중입니다... 완료
패키지 libpng-dev는 이미 최신 버전입니다 (1.6.34-1ubuntu0.18.04.2).
libpng-dev 패키지는 수동설치로 지정합니다.
다음 패키지가 자동으로 설치되었지만 더 이상 필요하지 않습니다:
  linux-headers-5.4.0-48-generic linux-hwe-5.4-headers-5.4.0-48
  linux-image-5.4.0-48-generic linux-modules-5.4.0-48-generic
  linux-modules-extra-5.4.0-48-generic
Use 'sudo apt autoremove' to remove them.
0개 업그레이드, 0개 새로 설치, 0개 제거 및 8개 업그레이드 안 함.
The directory '/home/minimonk/.cache/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
The directory '/home/minimonk/.cache/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
Requirement already satisfied: pyyaml in /usr/lib/python3/dist-packages (from -r /opt/intel/openvino_2021/deployment_tools/demo/../open_model_zoo/tools/downloader/requirements.in (line 1))
Requirement already satisfied: requests in /usr/lib/python3/dist-packages (from -r /opt/intel/openvino_2021/deployment_tools/demo/../open_model_zoo/tools/downloader/requirements.in (line 2))
Run python3 /opt/intel/openvino_2021/deployment_tools/open_model_zoo/tools/downloader/downloader.py --name squeezenet1.1 --output_dir /home/minimonk/openvino_models/models --cache_dir /home/minimonk/openvino_models/cache

################|| Downloading squeezenet1.1 ||################

========== Downloading /home/minimonk/openvino_models/models/public/squeezenet1.1/squeezenet1.1.prototxt
... 100%, 9 KB, 43302 KB/s, 0 seconds passed

========== Downloading /home/minimonk/openvino_models/models/public/squeezenet1.1/squeezenet1.1.caffemodel
... 100%, 4834 KB, 590 KB/s, 8 seconds passed

========== Replacing text in /home/minimonk/openvino_models/models/public/squeezenet1.1/squeezenet1.1.prototxt



###################################################

Install Model Optimizer dependencies

기존:1 https://apt.repos.intel.com/openvino/2021 all InRelease
기존:2 https://packages.microsoft.com/repos/vscode stable InRelease            
기존:3 http://apt.postgresql.org/pub/repos/apt bionic-pgdg InRelease           
기존:4 https://packages.cloud.google.com/apt coral-edgetpu-stable InRelease    
기존:5 http://dl.openfoam.org/ubuntu bionic InRelease                          
기존:6 http://security.ubuntu.com/ubuntu bionic-security InRelease             
기존:7 http://kr.archive.ubuntu.com/ubuntu bionic InRelease                    
기존:8 http://kr.archive.ubuntu.com/ubuntu bionic-updates InRelease
기존:9 http://kr.archive.ubuntu.com/ubuntu bionic-backports InRelease
패키지 목록을 읽는 중입니다... 완료     
의존성 트리를 만드는 중입니다       
상태 정보를 읽는 중입니다... 완료
8 packages can be upgraded. Run 'apt list --upgradable' to see them.
패키지 목록을 읽는 중입니다... 완료
의존성 트리를 만드는 중입니다       
상태 정보를 읽는 중입니다... 완료
패키지 libgfortran5는 이미 최신 버전입니다 (8.4.0-1ubuntu1~18.04).
패키지 python3-pip는 이미 최신 버전입니다 (9.0.1-2.3~ubuntu1.18.04.3).
패키지 python3-venv는 이미 최신 버전입니다 (3.6.7-1~18.04).
다음 패키지가 자동으로 설치되었지만 더 이상 필요하지 않습니다:
  linux-headers-5.4.0-48-generic linux-hwe-5.4-headers-5.4.0-48
  linux-image-5.4.0-48-generic linux-modules-5.4.0-48-generic
  linux-modules-extra-5.4.0-48-generic
Use 'sudo apt autoremove' to remove them.
0개 업그레이드, 0개 새로 설치, 0개 제거 및 8개 업그레이드 안 함.
The directory '/home/minimonk/.cache/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
The directory '/home/minimonk/.cache/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
Collecting networkx>=1.11 (from -r /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites/../requirements_caffe.txt (line 1))
  Downloading https://files.pythonhosted.org/packages/9b/cd/dc52755d30ba41c60243235460961fc28022e5b6731f16c268667625baea/networkx-2.5-py3-none-any.whl (1.6MB)
    100% |████████████████████████████████| 1.6MB 748kB/s 
Requirement already satisfied: numpy>=1.13.0 in /home/minimonk/.local/lib/python3.6/site-packages (from -r /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites/../requirements_caffe.txt (line 2))
Collecting protobuf>=3.6.1 (from -r /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites/../requirements_caffe.txt (line 3))
  Downloading https://files.pythonhosted.org/packages/30/79/510974552cebff2ba04038544799450defe75e96ea5f1675dbf72cc8744f/protobuf-3.13.0-cp36-cp36m-manylinux1_x86_64.whl (1.3MB)
    100% |████████████████████████████████| 1.3MB 689kB/s 
Collecting test-generator==0.1.1 (from -r /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites/../requirements_caffe.txt (line 4))
  Downloading https://files.pythonhosted.org/packages/4a/52/e5eec4d926eb466844eaeeaac84af5372e946dd520fb2b6adf3388e620b0/test_generator-0.1.1-py2.py3-none-any.whl
Collecting defusedxml>=0.5.0 (from -r /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites/../requirements_caffe.txt (line 5))
  Downloading https://files.pythonhosted.org/packages/06/74/9b387472866358ebc08732de3da6dc48e44b0aacd2ddaa5cb85ab7e986a2/defusedxml-0.6.0-py2.py3-none-any.whl
Collecting decorator>=4.3.0 (from networkx>=1.11->-r /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites/../requirements_caffe.txt (line 1))
  Downloading https://files.pythonhosted.org/packages/ed/1b/72a1821152d07cf1d8b6fce298aeb06a7eb90f4d6d41acec9861e7cc6df0/decorator-4.4.2-py2.py3-none-any.whl
Requirement already satisfied: six>=1.9 in /home/minimonk/.local/lib/python3.6/site-packages (from protobuf>=3.6.1->-r /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites/../requirements_caffe.txt (line 3))
Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from protobuf>=3.6.1->-r /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites/../requirements_caffe.txt (line 3))
Installing collected packages: decorator, networkx, protobuf, test-generator, defusedxml
  Found existing installation: protobuf 3.0.0
    Not uninstalling protobuf at /usr/lib/python3/dist-packages, outside environment /usr
Successfully installed decorator-4.4.2 defusedxml-0.6.0 networkx-2.5 protobuf-3.13.0 test-generator-0.1.1
[WARNING] All Model Optimizer dependencies are installed globally.
[WARNING] If you want to keep Model Optimizer in separate sandbox
[WARNING] run install_prerequisites.sh venv {caffe|tf|tf2|mxnet|kaldi|onnx}


###################################################

Convert a model with Model Optimizer

Run python3 /opt/intel/openvino_2021/deployment_tools/open_model_zoo/tools/downloader/converter.py --mo /opt/intel/openvino_2021/deployment_tools/model_optimizer/mo.py --name squeezenet1.1 -d /home/minimonk/openvino_models/models -o /home/minimonk/openvino_models/ir --precisions FP16

========== Converting squeezenet1.1 to IR (FP16)
Conversion command: /usr/bin/python3 -- /opt/intel/openvino_2021/deployment_tools/model_optimizer/mo.py --framework=caffe --data_type=FP16 --output_dir=/home/minimonk/openvino_models/ir/public/squeezenet1.1/FP16 --model_name=squeezenet1.1 '--input_shape=[1,3,227,227]' --input=data '--mean_values=data[104.0,117.0,123.0]' --output=prob --input_model=/home/minimonk/openvino_models/models/public/squeezenet1.1/squeezenet1.1.caffemodel --input_proto=/home/minimonk/openvino_models/models/public/squeezenet1.1/squeezenet1.1.prototxt

Model Optimizer arguments:
Common parameters:
	- Path to the Input Model: 	/home/minimonk/openvino_models/models/public/squeezenet1.1/squeezenet1.1.caffemodel
	- Path for generated IR: 	/home/minimonk/openvino_models/ir/public/squeezenet1.1/FP16
	- IR output name: 	squeezenet1.1
	- Log level: 	ERROR
	- Batch: 	Not specified, inherited from the model
	- Input layers: 	data
	- Output layers: 	prob
	- Input shapes: 	[1,3,227,227]
	- Mean values: 	data[104.0,117.0,123.0]
	- Scale values: 	Not specified
	- Scale factor: 	Not specified
	- Precision of IR: 	FP16
	- Enable fusing: 	True
	- Enable grouped convolutions fusing: 	True
	- Move mean values to preprocess section: 	None
	- Reverse input channels: 	False
Caffe specific parameters:
	- Path to Python Caffe* parser generated from caffe.proto: 	/opt/intel/openvino_2021/deployment_tools/model_optimizer/mo/front/caffe/proto
	- Enable resnet optimization: 	True
	- Path to the Input prototxt: 	/home/minimonk/openvino_models/models/public/squeezenet1.1/squeezenet1.1.prototxt
	- Path to CustomLayersMapping.xml: 	Default
	- Path to a mean file: 	Not specified
	- Offsets for a mean file: 	Not specified
Model Optimizer version: 	2021.1.0-1237-bece22ac675-releases/2021/1
[ WARNING ]  
Detected not satisfied dependencies:
	protobuf: installed: 3.0.0, required: >= 3.6.1

Please install required versions of components or use install_prerequisites script
/opt/intel/openvino_2021.1.110/deployment_tools/model_optimizer/install_prerequisites/install_prerequisites_caffe.sh
Note that install_prerequisites scripts may install additional components.

[ SUCCESS ] Generated IR version 10 model.
[ SUCCESS ] XML file: /home/minimonk/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml
[ SUCCESS ] BIN file: /home/minimonk/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.bin
[ SUCCESS ] Total execution time: 6.45 seconds. 
[ SUCCESS ] Memory consumed: 83 MB. 



###################################################

Build Inference Engine samples

-- The C compiler identification is GNU 7.5.0
-- The CXX compiler identification is GNU 7.5.0
-- Check for working C compiler: /usr/bin/cc
-- Check for working C compiler: /usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Looking for C++ include unistd.h
-- Looking for C++ include unistd.h - found
-- Looking for C++ include stdint.h
-- Looking for C++ include stdint.h - found
-- Looking for C++ include sys/types.h
-- Looking for C++ include sys/types.h - found
-- Looking for C++ include fnmatch.h
-- Looking for C++ include fnmatch.h - found
-- Looking for strtoll
-- Looking for strtoll - found
-- Found InferenceEngine: /opt/intel/openvino_2021/deployment_tools/inference_engine/lib/intel64/libinference_engine.so (Required is at least version "2.1") 
CMake Warning at /opt/intel/openvino_2021/deployment_tools/inference_engine/share/ie_parallel.cmake:6 (find_package):
  By not providing "FindTBB.cmake" in CMAKE_MODULE_PATH this project has
  asked CMake to find a package configuration file provided by "TBB", but
  CMake did not find one.

  Could not find a package configuration file provided by "TBB" with any of
  the following names:

    TBBConfig.cmake
    tbb-config.cmake

  Add the installation prefix of "TBB" to CMAKE_PREFIX_PATH or set "TBB_DIR"
  to a directory containing one of the above files.  If "TBB" provides a
  separate development package or SDK, be sure it has been installed.
Call Stack (most recent call first):
  /opt/intel/openvino_2021/deployment_tools/inference_engine/share/InferenceEngineConfig.cmake:170 (include)
  CMakeLists.txt:141 (find_package)


CMake Warning at /opt/intel/openvino_2021/deployment_tools/inference_engine/share/InferenceEngineConfig.cmake:32 (message):
  TBB was not found by the configured TBB_DIR/TBBROOT path.  SEQ method will
  be used.
Call Stack (most recent call first):
  /opt/intel/openvino_2021/deployment_tools/inference_engine/share/ie_parallel.cmake:14 (ext_message)
  /opt/intel/openvino_2021/deployment_tools/inference_engine/share/InferenceEngineConfig.cmake:170 (include)
  CMakeLists.txt:141 (find_package)


-- Configuring done
-- Generating done
-- Build files have been written to: /home/minimonk/inference_engine_samples_build
Scanning dependencies of target gflags_nothreads_static
Scanning dependencies of target format_reader
[  9%] Building CXX object thirdparty/gflags/CMakeFiles/gflags_nothreads_static.dir/src/gflags_reporting.cc.o
[ 18%] Building CXX object thirdparty/gflags/CMakeFiles/gflags_nothreads_static.dir/src/gflags_completions.cc.o
[ 27%] Building CXX object thirdparty/gflags/CMakeFiles/gflags_nothreads_static.dir/src/gflags.cc.o
[ 36%] Building CXX object common/format_reader/CMakeFiles/format_reader.dir/opencv_wraper.cpp.o
[ 45%] Building CXX object common/format_reader/CMakeFiles/format_reader.dir/bmp.cpp.o
[ 54%] Building CXX object common/format_reader/CMakeFiles/format_reader.dir/MnistUbyte.cpp.o
[ 63%] Building CXX object common/format_reader/CMakeFiles/format_reader.dir/format_reader.cpp.o
[ 72%] Linking CXX static library ../../intel64/Release/lib/libgflags_nothreads.a
[ 72%] Built target gflags_nothreads_static
[ 81%] Linking CXX shared library ../../intel64/Release/lib/libformat_reader.so
[ 81%] Built target format_reader
Scanning dependencies of target classification_sample_async
[ 90%] Building CXX object classification_sample_async/CMakeFiles/classification_sample_async.dir/main.cpp.o
[100%] Linking CXX executable ../intel64/Release/classification_sample_async
[100%] Built target classification_sample_async


###################################################

Run Inference Engine classification sample

Run ./classification_sample_async -d MYRIAD -i /opt/intel/openvino_2021/deployment_tools/demo/car.png -m /home/minimonk/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml

[ INFO ] InferenceEngine: 
	API version ............ 2.1
	Build .................. 2021.1.0-1237-bece22ac675-releases/2021/1
	Description ....... API
[ INFO ] Parsing input parameters
[ INFO ] Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ]     /opt/intel/openvino_2021/deployment_tools/demo/car.png
[ INFO ] Creating Inference Engine
	MYRIAD
	myriadPlugin version ......... 2.1
	Build ........... 2021.1.0-1237-bece22ac675-releases/2021/1

[ INFO ] Loading network files
[ INFO ] Preparing input blobs
[ WARNING ] Image is resized from (787, 259) to (227, 227)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the device
E: [ncAPI] [      4953] [classification_] ncDeviceOpen:1011	Failed to find booted device after boot
[ ERROR ] Can not init Myriad device: NC_ERROR
Error on or near line 217; exiting with status 1

 

장치를 연결하고 했다고 먼가 달라지진 않네.. -_-

[ INFO ] Loading model to the device
E: [ncAPI] [    640146] [classification_] ncDeviceOpen:1011	Failed to find booted device after boot
[ ERROR ] Can not init Myriad device: NC_ERROR
Error on or near line 217; exiting with status 1

 

혹시나 해서 리부팅 하고 해보니 된다.

앞에는 매번 패키지 업데이트 하고 그런다고 시간 다 잡아 먹고

정작 가장 끝에는 순식같에 끝나는데.. 시간이 재지지 않으니 알수가 없네

/opt/intel/openvino_2021/deployment_tools/demo$ ./demo_squeezenet_download_convert_run.sh -d MYRIAD
target = MYRIAD
target_precision = FP16
[setupvars.sh] OpenVINO environment initialized


###################################################



Downloading the Caffe model and the prototxt
Installing dependencies
기존:1 https://packages.cloud.google.com/apt coral-edgetpu-stable InRelease    
기존:2 https://apt.repos.intel.com/openvino/2021 all InRelease                 
기존:3 https://packages.microsoft.com/repos/vscode stable InRelease            
기존:4 http://kr.archive.ubuntu.com/ubuntu bionic InRelease                    
기존:5 http://security.ubuntu.com/ubuntu bionic-security InRelease             
기존:6 http://dl.openfoam.org/ubuntu bionic InRelease                          
기존:7 http://kr.archive.ubuntu.com/ubuntu bionic-updates InRelease            
기존:8 http://kr.archive.ubuntu.com/ubuntu bionic-backports InRelease          
기존:9 http://apt.postgresql.org/pub/repos/apt bionic-pgdg InRelease           
패키지 목록을 읽는 중입니다... 완료     
의존성 트리를 만드는 중입니다       
상태 정보를 읽는 중입니다... 완료
8 packages can be upgraded. Run 'apt list --upgradable' to see them.
Run sudo -E apt -y install build-essential python3-pip virtualenv cmake libcairo2-dev libpango1.0-dev libglib2.0-dev libgtk2.0-dev libswscale-dev libavcodec-dev libavformat-dev libgstreamer1.0-0 gstreamer1.0-plugins-base

패키지 목록을 읽는 중입니다... 완료
의존성 트리를 만드는 중입니다       
상태 정보를 읽는 중입니다... 완료
패키지 build-essential는 이미 최신 버전입니다 (12.4ubuntu1).
패키지 libgtk2.0-dev는 이미 최신 버전입니다 (2.24.32-1ubuntu1).
패키지 virtualenv는 이미 최신 버전입니다 (15.1.0+ds-1.1).
패키지 cmake는 이미 최신 버전입니다 (3.10.2-1ubuntu2.18.04.1).
패키지 gstreamer1.0-plugins-base는 이미 최신 버전입니다 (1.14.5-0ubuntu1~18.04.1).
패키지 libcairo2-dev는 이미 최신 버전입니다 (1.15.10-2ubuntu0.1).
패키지 libglib2.0-dev는 이미 최신 버전입니다 (2.56.4-0ubuntu0.18.04.6).
패키지 libgstreamer1.0-0는 이미 최신 버전입니다 (1.14.5-0ubuntu1~18.04.1).
패키지 libpango1.0-dev는 이미 최신 버전입니다 (1.40.14-1ubuntu0.1).
패키지 libavcodec-dev는 이미 최신 버전입니다 (7:3.4.8-0ubuntu0.2).
패키지 libavformat-dev는 이미 최신 버전입니다 (7:3.4.8-0ubuntu0.2).
패키지 libswscale-dev는 이미 최신 버전입니다 (7:3.4.8-0ubuntu0.2).
패키지 python3-pip는 이미 최신 버전입니다 (9.0.1-2.3~ubuntu1.18.04.3).
다음 패키지가 자동으로 설치되었지만 더 이상 필요하지 않습니다:
  linux-headers-5.4.0-48-generic linux-hwe-5.4-headers-5.4.0-48
  linux-image-5.4.0-48-generic linux-modules-5.4.0-48-generic
  linux-modules-extra-5.4.0-48-generic
Use 'sudo apt autoremove' to remove them.
0개 업그레이드, 0개 새로 설치, 0개 제거 및 8개 업그레이드 안 함.
패키지 목록을 읽는 중입니다... 완료
의존성 트리를 만드는 중입니다       
상태 정보를 읽는 중입니다... 완료
패키지 libpng-dev는 이미 최신 버전입니다 (1.6.34-1ubuntu0.18.04.2).
다음 패키지가 자동으로 설치되었지만 더 이상 필요하지 않습니다:
  linux-headers-5.4.0-48-generic linux-hwe-5.4-headers-5.4.0-48
  linux-image-5.4.0-48-generic linux-modules-5.4.0-48-generic
  linux-modules-extra-5.4.0-48-generic
Use 'sudo apt autoremove' to remove them.
0개 업그레이드, 0개 새로 설치, 0개 제거 및 8개 업그레이드 안 함.
The directory '/home/minimonk/.cache/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
The directory '/home/minimonk/.cache/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
Requirement already satisfied: pyyaml in /usr/lib/python3/dist-packages (from -r /opt/intel/openvino_2021/deployment_tools/demo/../open_model_zoo/tools/downloader/requirements.in (line 1))
Requirement already satisfied: requests in /usr/lib/python3/dist-packages (from -r /opt/intel/openvino_2021/deployment_tools/demo/../open_model_zoo/tools/downloader/requirements.in (line 2))
Run python3 /opt/intel/openvino_2021/deployment_tools/open_model_zoo/tools/downloader/downloader.py --name squeezenet1.1 --output_dir /home/minimonk/openvino_models/models --cache_dir /home/minimonk/openvino_models/cache

################|| Downloading squeezenet1.1 ||################

========== Retrieving /home/minimonk/openvino_models/models/public/squeezenet1.1/squeezenet1.1.prototxt from the cache

========== Retrieving /home/minimonk/openvino_models/models/public/squeezenet1.1/squeezenet1.1.caffemodel from the cache

========== Replacing text in /home/minimonk/openvino_models/models/public/squeezenet1.1/squeezenet1.1.prototxt



Target folder /home/minimonk/openvino_models/ir/public/squeezenet1.1/FP16 already exists. Skipping IR generation  with Model Optimizer.If you want to convert a model again, remove the entire /home/minimonk/openvino_models/ir/public/squeezenet1.1/FP16 folder. Then run the script again



###################################################

Build Inference Engine samples

-- The C compiler identification is GNU 7.5.0
-- The CXX compiler identification is GNU 7.5.0
-- Check for working C compiler: /usr/bin/cc
-- Check for working C compiler: /usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Looking for C++ include unistd.h
-- Looking for C++ include unistd.h - found
-- Looking for C++ include stdint.h
-- Looking for C++ include stdint.h - found
-- Looking for C++ include sys/types.h
-- Looking for C++ include sys/types.h - found
-- Looking for C++ include fnmatch.h
-- Looking for C++ include fnmatch.h - found
-- Looking for strtoll
-- Looking for strtoll - found
-- Found InferenceEngine: /opt/intel/openvino_2021/deployment_tools/inference_engine/lib/intel64/libinference_engine.so (Required is at least version "2.1") 
CMake Warning at /opt/intel/openvino_2021/deployment_tools/inference_engine/share/ie_parallel.cmake:6 (find_package):
  By not providing "FindTBB.cmake" in CMAKE_MODULE_PATH this project has
  asked CMake to find a package configuration file provided by "TBB", but
  CMake did not find one.

  Could not find a package configuration file provided by "TBB" with any of
  the following names:

    TBBConfig.cmake
    tbb-config.cmake

  Add the installation prefix of "TBB" to CMAKE_PREFIX_PATH or set "TBB_DIR"
  to a directory containing one of the above files.  If "TBB" provides a
  separate development package or SDK, be sure it has been installed.
Call Stack (most recent call first):
  /opt/intel/openvino_2021/deployment_tools/inference_engine/share/InferenceEngineConfig.cmake:170 (include)
  CMakeLists.txt:141 (find_package)


CMake Warning at /opt/intel/openvino_2021/deployment_tools/inference_engine/share/InferenceEngineConfig.cmake:32 (message):
  TBB was not found by the configured TBB_DIR/TBBROOT path.  SEQ method will
  be used.
Call Stack (most recent call first):
  /opt/intel/openvino_2021/deployment_tools/inference_engine/share/ie_parallel.cmake:14 (ext_message)
  /opt/intel/openvino_2021/deployment_tools/inference_engine/share/InferenceEngineConfig.cmake:170 (include)
  CMakeLists.txt:141 (find_package)


-- Configuring done
-- Generating done
-- Build files have been written to: /home/minimonk/inference_engine_samples_build
[ 36%] Built target gflags_nothreads_static
[ 81%] Built target format_reader
[100%] Built target classification_sample_async


###################################################

Run Inference Engine classification sample

Run ./classification_sample_async -d MYRIAD -i /opt/intel/openvino_2021/deployment_tools/demo/car.png -m /home/minimonk/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml

[ INFO ] InferenceEngine: 
	API version ............ 2.1
	Build .................. 2021.1.0-1237-bece22ac675-releases/2021/1
	Description ....... API
[ INFO ] Parsing input parameters
[ INFO ] Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ]     /opt/intel/openvino_2021/deployment_tools/demo/car.png
[ INFO ] Creating Inference Engine
	MYRIAD
	myriadPlugin version ......... 2.1
	Build ........... 2021.1.0-1237-bece22ac675-releases/2021/1

[ INFO ] Loading network files
[ INFO ] Preparing input blobs
[ WARNING ] Image is resized from (787, 259) to (227, 227)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the device
[ INFO ] Create infer request
[ INFO ] Start inference (10 asynchronous executions)
[ INFO ] Completed 1 async request execution
[ INFO ] Completed 2 async request execution
[ INFO ] Completed 3 async request execution
[ INFO ] Completed 4 async request execution
[ INFO ] Completed 5 async request execution
[ INFO ] Completed 6 async request execution
[ INFO ] Completed 7 async request execution
[ INFO ] Completed 8 async request execution
[ INFO ] Completed 9 async request execution
[ INFO ] Completed 10 async request execution
[ INFO ] Processing output blobs

Top 10 results:

Image /opt/intel/openvino_2021/deployment_tools/demo/car.png

classid probability label
------- ----------- -----
817     0.6708984   sports car, sport car
479     0.1922607   car wheel
511     0.0936890   convertible
436     0.0216064   beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
751     0.0075760   racer, race car, racing car
656     0.0049667   minivan
717     0.0027428   pickup, pickup truck
581     0.0019779   grille, radiator grille
468     0.0014219   cab, hack, taxi, taxicab
661     0.0008636   Model T

[ INFO ] Execution successful

[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool


###################################################

Demo completed successfully.

 

 

아무튼 intel NCS2 작동은 확인! 개발환경도 완료!

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설치 부터가 드럽게 친절하지 않네

일단 openVINO가 intel 사이트에 통합된게 아니라

web 버전으로 받아 보니 rpm이 잔뜩 있어서 아닌것 같고 apt로 받으려고 가니 이상한 소리만 하고 있다.

 

 

openVINO 랑 참조해서 아래의 명령어들을 이용해서 설치하면 되긴한데

(다음글에서 개고생한걸 생각하면 openvino 2021이 아니라 약간 구버전을 쓰면

python 버전과 tensorflow 버전 문제로 고생을 덜하지 않았을까 하는 생각이 들긴한데

2020 버전에 대한 gpg를 찾지 못하겠다)

 

$ wget "https://apt.repos.intel.com/openvino/2021/GPG-PUB-KEY-INTEL-OPENVINO-2021"
$ sudo apt-key add GPG-PUB-KEY-INTEL-OPENVINO-2021
$ echo "deb https://apt.repos.intel.com/openvino/2021 all main" | sudo tee /etc/apt/sources.list.d/intel-openvino-2021.list
deb https://apt.repos.intel.com/openvino/2021 all main
$ sudo apt-get update
$ sudo apt-cache search intel-openvino-dev-ubuntu18
intel-openvino-dev-ubuntu18-2021.1.110 - Intel® Deep Learning Deployment Toolkit 2021.1 for Linux*
$ sudo apt-get install intel-openvino-dev-ubuntu18-2021.1.110
$ cd /opt/intel/openvino_2021

 

[링크 : https://docs.openvinotoolkit.org/latest/openvino_docs_install_guides_installing_openvino_apt.html]

 

[링크: https://software.intel.com/content/www/us/en/develop/articles/get-started-with-neural-compute-stick.html]

[링크 : https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit/download.html...]

 

+

한번 해봤는데 GPG 키는 동일하게 나온다. 그 아래의 openvino/2021 all main 대신 2020으로 해봐야 하나 귀찮아..

[링크 : https://apt.repos.intel.com/openvino/2020/GPG-PUB-KEY-INTEL-OPENVINO-2020]

[링크 : https://apt.repos.intel.com/openvino/2021/GPG-PUB-KEY-INTEL-OPENVINO-2021]

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yolo 검색하다가 rpi에서 yolo-tiny로 프레임이 잘나온다고 했는데

다시보니 yolo-tiny가 문제가 아니라 NCU가 핵심인 듯 -_-

[링크 : https://www.pyimagesearch.com/...-tiny-yolo-object-detection-on-the-raspberry-pi-and-movidius-ncs/]

 

Intel® Movidius™ Neural Compute Stick (NCS)

[링크 : https://movidius.github.io/ncsdk/ncs.html]

 

Intel® Movidius™ Vision Processing Units (VPUs)

[링크 : https://www.intel.com/content/www/us/en/products/processors/movidius-vpu.html]

[링크 : https://www.intel.com/content/www/us/en/products/processors/movidius-vpu/movidius-myriad-x.html]

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프로그램 사용/gcc2020. 10. 21. 12:47

gcc 4.4 이후 버전 사용가능

#pragma GCC push_options
#pragma GCC optimize ("O0")

your code

#pragma GCC pop_options

 

void __attribute__((optimize("O0"))) foo(unsigned char data) {
    // unmodifiable compiler code
}

[링크 : https://stackoverflow.com/questions/2219829/how-to-prevent-gcc-optimizing-some-statements-in-c]

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프로그램 사용/yolo2020. 10. 21. 11:51

BFLOPs (Billion FLOPs)

[링크 : https://arxiv.org/pdf/1910.03159.pdf]

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프로그램 사용/yolo2020. 10. 20. 19:26

weight 받는데 한참 걸린다(100kbps 정도 뜨는 느낌...)

weight를 받지 않고 실행하면 한참 먼가 계산하고 나서 weight가 없다고 에러나면서 종료된다.

 

git clone https://github.com/pjreddie/darknet
cd darknet
make
wget https://pjreddie.com/media/files/yolov3.weights
./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg

 

결과물은 prediction.jpg로 박스쳐져서 나온다.(옵션을 주면 상자 위치로 나오려나?)

 

 

아무튼 weight 를 받아서 돌려보는데 예상외로 무겁다?

개인 노트북이 i5-2520m 이긴한데 최대 클럭 + 부스트 하도록 설정하고 했는데도 30초 가량 걸린다.

$ time ./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
layer     filters    size              input                output
    0 conv     32  3 x 3 / 1   608 x 608 x   3   ->   608 x 608 x  32  0.639 BFLOPs
    1 conv     64  3 x 3 / 2   608 x 608 x  32   ->   304 x 304 x  64  3.407 BFLOPs
    2 conv     32  1 x 1 / 1   304 x 304 x  64   ->   304 x 304 x  32  0.379 BFLOPs
    3 conv     64  3 x 3 / 1   304 x 304 x  32   ->   304 x 304 x  64  3.407 BFLOPs
    4 res    1                 304 x 304 x  64   ->   304 x 304 x  64
    5 conv    128  3 x 3 / 2   304 x 304 x  64   ->   152 x 152 x 128  3.407 BFLOPs
    6 conv     64  1 x 1 / 1   152 x 152 x 128   ->   152 x 152 x  64  0.379 BFLOPs
    7 conv    128  3 x 3 / 1   152 x 152 x  64   ->   152 x 152 x 128  3.407 BFLOPs
    8 res    5                 152 x 152 x 128   ->   152 x 152 x 128
    9 conv     64  1 x 1 / 1   152 x 152 x 128   ->   152 x 152 x  64  0.379 BFLOPs
   10 conv    128  3 x 3 / 1   152 x 152 x  64   ->   152 x 152 x 128  3.407 BFLOPs
   11 res    8                 152 x 152 x 128   ->   152 x 152 x 128
   12 conv    256  3 x 3 / 2   152 x 152 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   13 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   14 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   15 res   12                  76 x  76 x 256   ->    76 x  76 x 256
   16 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   17 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   18 res   15                  76 x  76 x 256   ->    76 x  76 x 256
   19 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   20 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   21 res   18                  76 x  76 x 256   ->    76 x  76 x 256
   22 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   23 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   24 res   21                  76 x  76 x 256   ->    76 x  76 x 256
   25 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   26 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   27 res   24                  76 x  76 x 256   ->    76 x  76 x 256
   28 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   29 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   30 res   27                  76 x  76 x 256   ->    76 x  76 x 256
   31 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   32 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   33 res   30                  76 x  76 x 256   ->    76 x  76 x 256
   34 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   35 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   36 res   33                  76 x  76 x 256   ->    76 x  76 x 256
   37 conv    512  3 x 3 / 2    76 x  76 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   38 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   39 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   40 res   37                  38 x  38 x 512   ->    38 x  38 x 512
   41 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   42 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   43 res   40                  38 x  38 x 512   ->    38 x  38 x 512
   44 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   45 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   46 res   43                  38 x  38 x 512   ->    38 x  38 x 512
   47 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   48 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   49 res   46                  38 x  38 x 512   ->    38 x  38 x 512
   50 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   51 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   52 res   49                  38 x  38 x 512   ->    38 x  38 x 512
   53 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   54 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   55 res   52                  38 x  38 x 512   ->    38 x  38 x 512
   56 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   57 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   58 res   55                  38 x  38 x 512   ->    38 x  38 x 512
   59 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   60 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   61 res   58                  38 x  38 x 512   ->    38 x  38 x 512
   62 conv   1024  3 x 3 / 2    38 x  38 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   63 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   64 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   65 res   62                  19 x  19 x1024   ->    19 x  19 x1024
   66 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   67 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   68 res   65                  19 x  19 x1024   ->    19 x  19 x1024
   69 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   70 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   71 res   68                  19 x  19 x1024   ->    19 x  19 x1024
   72 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   73 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   74 res   71                  19 x  19 x1024   ->    19 x  19 x1024
   75 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   76 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   77 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   78 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   79 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   80 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   81 conv    255  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 255  0.189 BFLOPs
   82 yolo
   83 route  79
   84 conv    256  1 x 1 / 1    19 x  19 x 512   ->    19 x  19 x 256  0.095 BFLOPs
   85 upsample            2x    19 x  19 x 256   ->    38 x  38 x 256
   86 route  85 61
   87 conv    256  1 x 1 / 1    38 x  38 x 768   ->    38 x  38 x 256  0.568 BFLOPs
   88 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   89 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   90 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   91 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   92 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   93 conv    255  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 255  0.377 BFLOPs
   94 yolo
   95 route  91
   96 conv    128  1 x 1 / 1    38 x  38 x 256   ->    38 x  38 x 128  0.095 BFLOPs
   97 upsample            2x    38 x  38 x 128   ->    76 x  76 x 128
   98 route  97 36
   99 conv    128  1 x 1 / 1    76 x  76 x 384   ->    76 x  76 x 128  0.568 BFLOPs
  100 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
  101 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
  102 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
  103 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
  104 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
  105 conv    255  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 255  0.754 BFLOPs
  106 yolo
Loading weights from yolov3.weights...Done!
data/dog.jpg: Predicted in 29.416936 seconds.
dog: 100%
truck: 92%
bicycle: 99%

real	0m33.904s
user	0m32.813s
sys	0m0.600s

 

tiny가 표준에 비해서 1/30 정도로 가볍긴 하지만, 그렇다고 해서 i5-2520m에서 이정도인데

임베디드 보드에서 실시간은 많이 무리일지도?

$ time ./darknet detect cfg/yolov3-tiny.cfg yolov3.weights data/dog.jpg
layer     filters    size              input                output
    0 conv     16  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  16  0.150 BFLOPs
    1 max          2 x 2 / 2   416 x 416 x  16   ->   208 x 208 x  16
    2 conv     32  3 x 3 / 1   208 x 208 x  16   ->   208 x 208 x  32  0.399 BFLOPs
    3 max          2 x 2 / 2   208 x 208 x  32   ->   104 x 104 x  32
    4 conv     64  3 x 3 / 1   104 x 104 x  32   ->   104 x 104 x  64  0.399 BFLOPs
    5 max          2 x 2 / 2   104 x 104 x  64   ->    52 x  52 x  64
    6 conv    128  3 x 3 / 1    52 x  52 x  64   ->    52 x  52 x 128  0.399 BFLOPs
    7 max          2 x 2 / 2    52 x  52 x 128   ->    26 x  26 x 128
    8 conv    256  3 x 3 / 1    26 x  26 x 128   ->    26 x  26 x 256  0.399 BFLOPs
    9 max          2 x 2 / 2    26 x  26 x 256   ->    13 x  13 x 256
   10 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512  0.399 BFLOPs
   11 max          2 x 2 / 1    13 x  13 x 512   ->    13 x  13 x 512
   12 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024  1.595 BFLOPs
   13 conv    256  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 256  0.089 BFLOPs
   14 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512  0.399 BFLOPs
   15 conv    255  1 x 1 / 1    13 x  13 x 512   ->    13 x  13 x 255  0.044 BFLOPs
   16 yolo
   17 route  13
   18 conv    128  1 x 1 / 1    13 x  13 x 256   ->    13 x  13 x 128  0.011 BFLOPs
   19 upsample            2x    13 x  13 x 128   ->    26 x  26 x 128
   20 route  19 8
   21 conv    256  3 x 3 / 1    26 x  26 x 384   ->    26 x  26 x 256  1.196 BFLOPs
   22 conv    255  1 x 1 / 1    26 x  26 x 256   ->    26 x  26 x 255  0.088 BFLOPs
   23 yolo
Loading weights from yolov3.weights...Done!
data/dog.jpg: Predicted in 1.197222 seconds.

real	0m1.811s
user	0m1.750s
sys	0m0.060s

 

결과가 안나와서 다른 사람들에게 물어보니 tiny용 weight가 따로 있다고 -_ㅠ

wget https://pjreddie.com/media/files/yolov3-tiny.weights
./darknet detect cfg/yolov3-tiny.cfg yolov3-tiny.weights data/dog.jpg

 

인식은 되는데 시간이 달라지진 않네..

$ time ./darknet detect cfg/yolov3-tiny.cfg yolov3-tiny.weights data/dog.jpg
layer     filters    size              input                output
    0 conv     16  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  16  0.150 BFLOPs
    1 max          2 x 2 / 2   416 x 416 x  16   ->   208 x 208 x  16
    2 conv     32  3 x 3 / 1   208 x 208 x  16   ->   208 x 208 x  32  0.399 BFLOPs
    3 max          2 x 2 / 2   208 x 208 x  32   ->   104 x 104 x  32
    4 conv     64  3 x 3 / 1   104 x 104 x  32   ->   104 x 104 x  64  0.399 BFLOPs
    5 max          2 x 2 / 2   104 x 104 x  64   ->    52 x  52 x  64
    6 conv    128  3 x 3 / 1    52 x  52 x  64   ->    52 x  52 x 128  0.399 BFLOPs
    7 max          2 x 2 / 2    52 x  52 x 128   ->    26 x  26 x 128
    8 conv    256  3 x 3 / 1    26 x  26 x 128   ->    26 x  26 x 256  0.399 BFLOPs
    9 max          2 x 2 / 2    26 x  26 x 256   ->    13 x  13 x 256
   10 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512  0.399 BFLOPs
   11 max          2 x 2 / 1    13 x  13 x 512   ->    13 x  13 x 512
   12 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024  1.595 BFLOPs
   13 conv    256  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 256  0.089 BFLOPs
   14 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512  0.399 BFLOPs
   15 conv    255  1 x 1 / 1    13 x  13 x 512   ->    13 x  13 x 255  0.044 BFLOPs
   16 yolo
   17 route  13
   18 conv    128  1 x 1 / 1    13 x  13 x 256   ->    13 x  13 x 128  0.011 BFLOPs
   19 upsample            2x    13 x  13 x 128   ->    26 x  26 x 128
   20 route  19 8
   21 conv    256  3 x 3 / 1    26 x  26 x 384   ->    26 x  26 x 256  1.196 BFLOPs
   22 conv    255  1 x 1 / 1    26 x  26 x 256   ->    26 x  26 x 255  0.088 BFLOPs
   23 yolo
Loading weights from yolov3-tiny.weights...Done!
data/dog.jpg: Predicted in 1.208611 seconds.
dog: 57%
car: 52%
truck: 56%
car: 62%
bicycle: 59%

real	0m1.822s
user	0m1.770s
sys	0m0.052s

 

[링크 : https://pjreddie.com/darknet/yolo/]

 

[링크 : https://blog.insightdatascience.com/how-to-train-your-own-yolov3-detector-from-scratch-224d10e55de2?gi=87339b7b98d4]

 

+

2020.10.21

[링크 : https://github.com/guichristmann/edge-tpu-tiny-yolo] TPU로 가속은 가능한듯?

 

+

결과 파일 추가! tiny는 쓸 수 있을까?

 

yolov3 / kite.jpg

 

yolov3-tiny / kite.jpg

 

yolov3 / dog.jpg

 

yolov3-tiny / dog.jpg

 

 

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구글 코랄 TPU USB를 한번 사용해 봄

일단은 설치된 python의 버전을 확인해야 하는데 버전에 맞지 않는 런타임을 설치할 경우

아래와 같이 not a supported wheel on this platform 이라는 에러가 발생한다.

$ pip3 install https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp38-cp38-linux_x86_64.whl
tflite_runtime-2.1.0.post1-cp38-cp38-linux_x86_64.whl is not a supported wheel on this platform.
$ python3 --version
Python 3.6.9

[링크 : https://coral.ai/docs/accelerator/get-started/]

 

dmesg로 확인해보니.. 다음과 같이 나온다.

[  957.819504] usb 2-1.2: new high-speed USB device number 4 using ehci-pci
[  957.928960] usb 2-1.2: New USB device found, idVendor=1a6e, idProduct=089a, bcdDevice= 1.00
[  957.928968] usb 2-1.2: New USB device strings: Mfr=0, Product=0, SerialNumber=0

 

lsusb로는 아래와 같이

Bus 002 Device 004: ID 04f2:b242 Chicony Electronics Co., Ltd 
Bus 002 Device 009: ID 1a6e:089a Global Unichip Corp
Bus 002 Device 008: ID 04e8:6860 Samsung Electronics Co., Ltd Galaxy (MTP)
Bus 002 Device 002: ID 8087:0024 Intel Corp. Integrated Rate Matching Hub
Bus 002 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub
Bus 001 Device 004: ID 04b4:6560 Cypress Semiconductor Corp. CY7C65640 USB-2.0 "TetraHub"
Bus 001 Device 002: ID 8087:0024 Intel Corp. Integrated Rate Matching Hub
Bus 001 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub

 

lshw로 확인해보면 아래와 같이 UNCLAIMED 라고 뜬다.

Global Unichip corp를 검색해보니 asic 설계 서비스 회사, Fabless 회사라고 나오네.

              *-usb
                   description: USB hub
                   product: Integrated Rate Matching Hub
                   vendor: Intel Corp.
                   physical id: 1
                   bus info: usb@2:1
                   version: 0.00
                   capabilities: usb-2.00
                   configuration: driver=hub slots=8 speed=480Mbit/s
                 *-usb:0 UNCLAIMED
                      description: Generic USB device
                      vendor: Global Unichip Corp.
                      physical id: 2
                      bus info: usb@2:1.2
                      version: 1.00
                      capabilities: usb-2.10
                      configuration: maxpower=498mA speed=480Mbit/s

 

아래는 할 것 다하고 USB 가속기를 설치하지 않았을 경우 발생하는 에러

장치를 발견하지 못했다고 뜨지 않고 Failed to load delegate from libedgetpu.so.1 이라고 뜬다.

$ python3 classify_image.py --model models/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite --labels models/inat_bird_labels.txt --input images/parrot.jpg
Traceback (most recent call last):
  File "/home/minimonk/.local/lib/python3.6/site-packages/tflite_runtime/interpreter.py", line 161, in load_delegate
    delegate = Delegate(library, options)
  File "/home/minimonk/.local/lib/python3.6/site-packages/tflite_runtime/interpreter.py", line 120, in __init__
    raise ValueError(capture.message)
ValueError

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "classify_image.py", line 122, in <module>
    main()
  File "classify_image.py", line 99, in main
    interpreter = make_interpreter(args.model)
  File "classify_image.py", line 73, in make_interpreter
    {'device': device[0]} if device else {})
  File "/home/minimonk/.local/lib/python3.6/site-packages/tflite_runtime/interpreter.py", line 164, in load_delegate
    library, str(e)))
ValueError: Failed to load delegate from libedgetpu.so.1

 

아무튼 USB 꼽고 하니 먼가 결과는 나오는데

맞나? 싶을 정도로 단순하게 문자열로 나온다.

그리고 USB2.0으로 해서 그런가 초기 속도가 상당히 느리게 나온다.

(홈페이지에서는 10ms 미만이었던 것 같은데)

$ python3 classify_image.py --model models/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite --labels models/inat_bird_labels.txt --input images/parrot.jpg
----INFERENCE TIME----
Note: The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory.
103.2ms
10.7ms
10.6ms
10.4ms
10.3ms
-------RESULTS--------
Ara macao (Scarlet Macaw): 0.77734

 

혹시나 해서 찾아본 라이브러리 경로.

$ sudo find / -name libedgetpu.so*
/usr/lib/x86_64-linux-gnu/libedgetpu.so.1
/usr/lib/x86_64-linux-gnu/libedgetpu.so.1.0

 

 

+

회사에서 알게된 장비인데 중고로 구매할까 구매대행으로 할까하고 찾아보니

12만원 넘어서 그냥 한번 써보는걸로 만족하려는 중

 

 

+ 2020.10.21

웹캠으로 받아서 TPU로 90가지 객체가 인식 가능한지 한번 해봐? ㅋㅋㅋ

[링크 : https://ultrakid.tistory.com/6]

    [링크 : https://github.com/google-coral/edgetpu/blob/master/examples/object_detection.py]

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호스트 이름을 지정을 해줄순 있는데 MAC 주소에 따라서 설정을 해야 해서 서버댓수가 많으면 무지 귀찮을 수도?

 

[링크 : http://www.iorchard.net/2016/11/03/dhcp_set_hostname.html]

[링크 : http://itwiki.kr/w/리눅스_dhcpd.conf]

[링크 : https://blog.naver.com/namelessda/114773026]

 

The use-host-decl-names statement
use-host-decl-names flag;
If the use-host-decl-names parameter is true in a given scope, then for every host declaration within that scope, the name provided for the host declaration will be supplied to the client as its hostname. So, for example,

    group {
      use-host-decl-names on;

     host joe {
        hardware ethernet 08:00:2b:4c:29:32;
          fixed-address joe.fugue.com;
      }
    }

is equivalent to

     host joe {
        hardware ethernet 08:00:2b:4c:29:32;
          fixed-address joe.fugue.com;
        option host-name "joe";
      }
An option host-name statement within a host declaration will override the use of the name in the host declaration.
It should be noted here that most DHCP clients completely ignore the host-name option sent by the DHCP server, and there is no way to configure them not to do this. So you generally have a choice of either not having any hostname to client IP address mapping that the client will recognize, or doing DNS updates. It is beyond the scope of this document to describe how to make this determination.

[링크 : https://linux.die.net/man/5/dhcpd.conf]

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