本文將在 QNAP NAS TS-1685 在 Linux Station - Ubuntu 16.04 配上 NVIDIA GeForce GTX 1060 上，使用 darkflow 加上已訓練好的 yolo model 做即時影像偵測，成果如下，它會將 webcam 資料讀進來分析，影片中，會依序出現，人、剪刀、搖控器，三樣東西一起出現。
開工
進入 Ubuntu shell
個人習慣是全都用終端機，不用任何圖型化界面，但要從 NAS 進入 Ubuntu 終端機比較麻煩點，ssh 進入 NAS 後，可用下列指令
[~] # lxc-attach -n ubuntu_1604 -P `getcfg ubuntu-hd Install_Path -f /etc/config/qpkg.conf`/lxc -- sudo -u admin -i
To run a command as administrator (user "root"), use "sudo
See "man sudo_root" for details.
admin@ubuntu_1604:~$
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便能看到 Ubuntu 16.04 用 admin 登入的 prompt。接下來都將用 $ 取代原本的 prompt。
更新系統
$ sudo apt update
$ sudo apt upgrade
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安裝 Python 環境
$ sudo apt install -y git
$ git clone https://github.com/pyenv/pyenv.git ~/.pyenv $ echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.bashrc $ echo 'export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bashrc $ echo 'eval "$(pyenv init -)"' >> ~/.bashrc |
重新進入 shell
$ git clone https://github.com/pyenv/pyenv-virtualenv.git $(pyenv root)/plugins/pyenv-virtualenv
$ echo 'eval "$(pyenv virtualenv-init -)"' >> ~/.bashrc |
重新進入 shell
接著安裝 Python 3.6.3 及預設使用 virtualenv darkflow
$ sudo apt-get install -y libbz2-dev libreadline-dev libssl-dev libsqlite3-dev
$ pyenv install 3.6.3
$ pyenv shell 3.6.3 $ pyenv virtualenv darkflow $ pyenv global darkflow |
重新進入 shell 後，便能看到以下這樣的 prompt。
(darkflow) admin@ubuntu_1604:~$
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安裝 darkflow
$ git clone https://github.com/thtrieu/darkflow
$ cd darkflow $ pip install Cython numpy opencv-python tensorflow $ pip install -e . |
編譯 opencv
正常 Python 環境下是不用重編 opencv，但是我們用了特殊版本的 Python，Python 在讀取 cv2.so 會異常，而且沒有明顯的錯誤訊息，這步做就對了，不要問太多。
$ mkdir ~/opencv
$ curl https://github.com/opencv/opencv/archive/3.3.1.zip > ~/opencv/3.3.1.zip $ cd ~/opencv/
$ unzip 3.3.1.zip
$ cd opencv* $ mkdir build $ cd build $ sudo apt install -y cmake libgtk2.0-dev $ export PREFIX_MAIN=`pyenv virtualenv-prefix` PREFIX=`pyenv prefix`
$ cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX="$PREFIX" \ -D PYTHON3_LIBRARY="$PREFIX_MAIN"/lib/libpython*m.a \ -D PYTHON3_INCLUDE_DIRS="$PREFIX_MAIN"/include/python*m \ -D PYTHON3_EXECUTABLE="$PREFIX"/bin/python3 \ -D PYTHON3_PACKAGES_PATH="$PREFIX"/lib/python*/site-packages/ \ -D PYTHON3_NUMPY_INCLUDE_DIRS="$PREFIX"/lib/python3.6/site-packages/numpy/core/include \ -D BUILD_opencv_python3=ON \ .. $ make -j`grep processor /proc/cpuinfo| wc -l` $ make install $ cp lib/python3/cv2.cpython-*-linux-gnu.so $PREFIX/lib/python*/site-packages/cv2/ |
使用 CPU 測試
下載訓練好的資料
$ cd ~/darkflow
$ mkdir bin
$ curl https://pjreddie.com/media/files/yolo.weights > ~/darkflow/bin/yolo.weights |
測試
$ flow --model cfg/yolo.cfg --load bin/yolo.weights --demo camera --saveVideo
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編譯 TensorFlow
重新編譯 TensorFlow 是為了支援此 NAS 支援的 CPU 指令集 (SSE4) 及 GPU。首先需先
確定 QTS 已安裝 NVIDIA Driver
進入 Ubuntu shell 安裝跟 NAS 一樣的 NVIDIA driver
$ sudo add-apt-repository ppa:graphics-drivers/ppa
$ sudo apt update $ sudo apt install -y nvidia-381 |
安裝 CUDA
需先下載安裝檔，參考此網址
安裝過程中，務必選擇不要安裝 NVIDIA driver，整個流程如下：
$ sudo sh cuda_*_linux.run
>>> Do you accept the previously read EULA? accept/decline/quit: accept Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 375.26? (y)es/(n)o/(q)uit: n Install the CUDA 8.0 Toolkit? (y)es/(n)o/(q)uit: y Enter Toolkit Location [ default is /usr/local/cuda-8.0 ]: Do you want to install a symbolic link at /usr/local/cuda? (y)es/(n)o/(q)uit: y Install the CUDA 8.0 Samples? (y)es/(n)o/(q)uit: y Enter CUDA Samples Location [ default is /home/admin ]:
將執行檔放入 PATH
echo 'export PATH=/usr/local/cuda-8.0/bin:$PATH' >> ~/.bashrc |
將執行檔放入 PATH
$ echo 'export PATH=/usr/local/cuda-8.0/bin:$PATH' >> ~/.bashrc
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安裝 cuDNN
$ sudo apt install -y libcupti-dev openjdk-8-jdk
$ mkdir cudnn
$ tar xvf cudnn*.tgz -C cudnn/
$ cd cudnn
$ sudo cp cuda/include/cudnn.h /usr/local/cuda/include
$ sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 $ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
$ echo /usr/local/cuda-8.0/lib64/ | sudo tee /etc/ld.so.conf.d/cudnn.conf
$ sudo ldconfig
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安裝 bazel
$ echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
$ curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add - $ sudo apt-get update && sudo apt-get install bazel |
下載 TensorFlow
$ git clone https://github.com/tensorflow/tensorflow
$ cd tensorflow
$ git checkout -b r1.4 origin/r1.4
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設定 TensorFlow 編譯參數
很多 Y/N 要填，特別要注意 CUDA 要寫 Y，預設是 N。
$ ./configure
Extracting Bazel installation...
You have bazel 0.7.0 installed. Please specify the location of python. [Default is /home/admin/.pyenv/versions/darkflow/bin/python]: Found possible Python library paths: /home/admin/.pyenv/versions/darkflow/lib/python3.6/site-packages Please input the desired Python library path to use. Default is [/home/admin/.pyenv/versions/darkflow/lib/python3.6/site-packages] Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: jemalloc as malloc support will be enabled for TensorFlow. Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: Google Cloud Platform support will be enabled for TensorFlow. Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: Hadoop File System support will be enabled for TensorFlow. Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: n No Amazon S3 File System support will be enabled for TensorFlow. Do you wish to build TensorFlow with XLA JIT support? [y/N]: n No XLA JIT support will be enabled for TensorFlow. Do you wish to build TensorFlow with GDR support? [y/N]: n No GDR support will be enabled for TensorFlow. Do you wish to build TensorFlow with VERBS support? [y/N]: n No VERBS support will be enabled for TensorFlow. Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n No OpenCL SYCL support will be enabled for TensorFlow. Do you wish to build TensorFlow with CUDA support? [y/N]: y CUDA support will be enabled for TensorFlow. Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 8.0]: Please specify the location where CUDA 8.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 6.0]: 7.0.4 Please specify the location where cuDNN 7.0.4 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: Please specify a list of comma-separated Cuda compute capabilities you want to build with. You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 3.5,5.2] Do you want to use clang as CUDA compiler? [y/N]: nvcc will be used as CUDA compiler. Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: Do you wish to build TensorFlow with MPI support? [y/N]: No MPI support will be enabled for TensorFlow. Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]: Add "--config=mkl" to your bazel command to build with MKL support. Please note that MKL on MacOS or windows is still not supported. If you would like to use a local MKL instead of downloading, please set the environment variable "TF_MKL_ROOT" every time before build. Configuration finished |
編譯 TensorFlow
要編很久...
$ bazel build --config=cuda -c opt --copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-mfpmath=both --copt=-msse4.2 //tensorflow/tools/pip_package:build_pip_package
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編譯及安裝 Python module
$ ./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
$ pip uninstall tensorflow
$ pip install /tmp/tensorflow_pkg/tensorflow-1.4.*-linux_x86_64.whl
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使用 GPU 測試
$ flow --model cfg/yolo.cfg --load bin/yolo.weights --demo camera --saveVideo --gpu 0.5 --threshold 0.5
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可參考本文開頭影片。
附註：在第一次測試時，發生 CUDA init 失敗，仔細看是 Container 內沒有 /dev/nvidia-uvm，但 Host 有，之後重開 Ubuntu 就好了。
若是自己玩，可以調整一下上述 gpu and threshold 參數，若發生 GPU OOM(out of memory) 可把 GPU 參數調低或重開 Ubuntu，若辨識到太不準，可調整 threshold，其他還有一些參數可調整的，請參考官網。
後記
每次都是半夜整理這些東西，很累很累...
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