軟硬件環境

• ubuntu 18.04 64bit
• NVidia GTX 1070Ti
• anaconda with python 3.7
• CUDA 10.1
• cuDNN 7.6
• opencv 3.4.2
• caffe 1.0.0

簡介

先說一下環境，使用anaconda的python虛擬環境，支持opencv，支持CUDA和cuDNN加速，支持在python中調用caffe。基礎組件部分可以參考前面的文章，本文就不贅述了

• anaconda基本使用 https://xugaoxiang.com/2019/12/08/anaconda/
• ubuntu安裝CUDA和cuDNN https://xugaoxiang.com/2019/12/13/ubuntu-cuda/
• opencv源碼編譯，支持CUDA https://xugaoxiang.com/2019/12/17/opencv-cuda/

基礎環境準備

安裝依賴包和工具

sudo?apt?install?build-essential?cmake?git?ffmpeg?libatlas-base-dev?libtiff-dev?pkg-config?python3-dev?libavcodec-dev?libavformat-dev?libswscale-dev?libtbb-dev?libjpeg-dev?libpng-dev?libavcodec-dev?libavformat-dev?libswscale-dev?libv4l-dev?libx264-dev?libboost-all-dev?libhdf5-serial-dev?libleveldb-dev?liblmdb-dev
pip?install?protobuf

opencv

這里把opencv單獨拿出來說，是因為opencv的安裝方法非常多

• apt install python3-opencv
• conda install opencv
• 源碼編譯

通過apt install安裝最簡單，也是最不容易出錯的方法；其次是conda install，最容易出問題的是自己編譯源碼，編譯參數復雜，依賴庫繁多，而且還有版本差異。

安裝完成后，建議使用opencv_version命令來查看當前版本，默認ubuntu 18.04源提供的是3.2.0版本，conda的會更高一些，這里是3.4.0，源碼安裝的話，注意在sudo make install后再執行一句sudo ldconfig。本文以conda的方式進行安裝。

編譯caffe

接下來就可以編譯caffe了

git?clone?https://github.com/BVLC/caffe.git
cd?caffe
cp?Makefile.config.example?Makefile.config

編輯文件Makefile.config，主要是一些路徑的修改，貼上已經修改好的

##?Refer?to?http://caffe.berkeleyvision.org/installation.html
#?Contributions?simplifying?and?improving?our?build?system?are?welcome!

#?cuDNN?acceleration?switch?(uncomment?to?build?with?cuDNN).
#?啟用cuDNN加速
USE_CUDNN?:=?1

#?CPU-only?switch?(uncomment?to?build?without?GPU?support).
#?CPU_ONLY?:=?1

#?uncomment?to?disable?IO?dependencies?and?corresponding?data?layers
#?啟用opencv
USE_OPENCV?:=?1
#?USE_LEVELDB?:=?0
#?USE_LMDB?:=?0
#?This?code?is?taken?from?https://github.com/sh1r0/caffe-android-lib
#?USE_HDF5?:=?0

#?uncomment?to?allow?MDB_NOLOCK?when?reading?LMDB?files?(only?if?necessary)
#?You?should?not?set?this?flag?if?you?will?be?reading?LMDBs?with?any
#?possibility?of?simultaneous?read?and?write
#?ALLOW_LMDB_NOLOCK?:=?1

#?Uncomment?if?you're?using?OpenCV?3
#?opencv大版本號是3，這里一定要注意
OPENCV_VERSION?:=?3

#?To?customize?your?choice?of?compiler,?uncomment?and?set?the?following.
#?N.B.?the?default?for?Linux?is?g++?and?the?default?for?OSX?is?clang++
#?CUSTOM_CXX?:=?g++

#?CUDA?directory?contains?bin/?and?lib/?directories?that?we?need.
CUDA_DIR?:=?/usr/local/cuda
#?On?Ubuntu?14.04,?if?cuda?tools?are?installed?via
#?"sudo?apt-get?install?nvidia-cuda-toolkit"?then?use?this?instead:
#?CUDA_DIR?:=?/usr

#?CUDA?architecture?setting:?going?with?all?of?them.
#?For?CUDA?#?For?CUDA?#?For?CUDA?>=?9.0,?comment?the?*_20?and?*_21?lines?for?compatibility.#?-gencode?arch=compute_20,code=sm_21#?CUDA_ARCH?:=?-gencode?arch=compute_20,code=sm_20?#?這里使用的是CUDA10.1，所以要注釋掉前兩行
CUDA_ARCH?:=?-gencode?arch=compute_30,code=sm_30?\
??-gencode?arch=compute_35,code=sm_35?\
??-gencode?arch=compute_50,code=sm_50?\
??-gencode?arch=compute_52,code=sm_52?\
??-gencode?arch=compute_60,code=sm_60?\
??-gencode?arch=compute_61,code=sm_61?\
??-gencode?arch=compute_61,code=compute_61#?BLAS?choice:#?atlas?for?ATLAS?(default)#?mkl?for?MKL#?open?for?OpenBlas
BLAS?:=?atlas#?Custom?(MKL/ATLAS/OpenBLAS)?include?and?lib?directories.#?Leave?commented?to?accept?the?defaults?for?your?choice?of?BLAS#?(which?should?work)!#?BLAS_INCLUDE?:=?/path/to/your/blas#?BLAS_LIB?:=?/path/to/your/blas#?Homebrew?puts?openblas?in?a?directory?that?is?not?on?the?standard?search?path#?BLAS_INCLUDE?:=?$(shell?brew?--prefix?openblas)/include#?BLAS_LIB?:=?$(shell?brew?--prefix?openblas)/lib#?This?is?required?only?if?you?will?compile?the?matlab?interface.#?MATLAB?directory?should?contain?the?mex?binary?in?/bin.#?MATLAB_DIR?:=?/usr/local#?MATLAB_DIR?:=?/Applications/MATLAB_R2012b.app#?NOTE:?this?is?required?only?if?you?will?compile?the?python?interface.#?We?need?to?be?able?to?find?Python.h?and?numpy/arrayobject.h.#?Python頭文件路徑，再加上numpy的頭文件路徑
PYTHON_INCLUDE?:=?/home/xugaoxiang/anaconda3/include/python3.7m?\
??/home/xugaoxiang/anaconda3/lib/python3.7/site-packages/numpy/core/include#?Anaconda?Python?distribution?is?quite?popular.?Include?path:#?Verify?anaconda?location,?sometimes?it's?in?root.#?ANACONDA_HOME?:=?$(HOME)/anaconda#?PYTHON_INCLUDE?:=?$(ANACONDA_HOME)/include?\#?$(ANACONDA_HOME)/include/python2.7?\#?$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include#?Uncomment?to?use?Python?3?(default?is?Python?2)#?默認是python2，這里使用python3，一定要改，不然后面會報錯相應沒人在用python2了吧
PYTHON_LIBRARIES?:=?boost_python3?python3.7m#?PYTHON_INCLUDE?:=?/usr/include/python3.5m?\#?????????????????/usr/lib/python3.5/dist-packages/numpy/core/include#?We?need?to?be?able?to?find?libpythonX.X.so?or?.dylib.#?libpython*.so庫的路徑
PYTHON_LIB?:=?/home/xugaoxiang/anaconda3/lib#?如果設置了ANACONDA_HOME環境變量，可以使用下面的設置方法，作用一樣#?PYTHON_LIB?:=?$(ANACONDA_HOME)/lib#?Homebrew?installs?numpy?in?a?non?standard?path?(keg?only)#?PYTHON_INCLUDE?+=?$(dir?$(shell?python?-c?'import?numpy.core;?print(numpy.core.__file__)'))/include#?PYTHON_LIB?+=?$(shell?brew?--prefix?numpy)/lib#?Uncomment?to?support?layers?written?in?Python?(will?link?against?Python?libs)#?WITH_PYTHON_LAYER?:=?1#?Whatever?else?you?find?you?need?goes?here.
INCLUDE_DIRS?:=?$(PYTHON_INCLUDE)?/usr/local/include?/home/xugaoxiang/anaconda3/include
LIBRARY_DIRS?:=?$(PYTHON_LIB)?/usr/local/lib?/usr/lib?/usr/lib/x86_64-linux-gnu/hdf5/serial/#?If?Homebrew?is?installed?at?a?non?standard?location?(for?example?your?home?directory)?and?you?use?it?for?general?dependencies#?INCLUDE_DIRS?+=?$(shell?brew?--prefix)/include#?LIBRARY_DIRS?+=?$(shell?brew?--prefix)/lib#?NCCL?acceleration?switch?(uncomment?to?build?with?NCCL)#?https://github.com/NVIDIA/nccl?(last?tested?version:?v1.2.3-1+cuda8.0)#?USE_NCCL?:=?1#?Uncomment?to?use?`pkg-config`?to?specify?OpenCV?library?paths.#?(Usually?not?necessary?--?OpenCV?libraries?are?normally?installed?in?one?of?the?above?$LIBRARY_DIRS.)#?啟用pkg_config，方便caffe找到opencv
USE_PKG_CONFIG?:=?1#?N.B.?both?build?and?distribute?dirs?are?cleared?on?`make?clean`#?默認編譯的目錄，所有的目標文件、可執行文件、庫都存放在這里
BUILD_DIR?:=?build
DISTRIBUTE_DIR?:=?distribute#?Uncomment?for?debugging.?Does?not?work?on?OSX?due?to?https://github.com/BVLC/caffe/issues/171#?是否打開debug信息#?DEBUG?:=?1#?The?ID?of?the?GPU?that?'make?runtest'?will?use?to?run?unit?tests.
TEST_GPUID?:=?0#?enable?pretty?build?(comment?to?see?full?commands)
Q??=?@

完成后，執行

make?all?-j12

參數-j指的是使用多少個cpu核心，目的是加快編譯速度，根據自己的實際情況設定

為了能在python中調用caffe，還需要執行

make?pycaffe?-j12

至此，整個編譯就結束了。

驗證

使用ipython環境測試

caffe

如果細心一點，會發現，新開一個terminal，同樣打開ipython，同樣import caffe，但是會報錯，這是什么原因？

caffe

在不報錯的terminal中，查看環境變量就會發現端倪

caffe

在編譯caffe的過程中，會export環境變量PYTHONPATH，所以我們在使用前也需要這樣做

caffe

為了簡便，可以將聲明語句寫入~/.bashrc中，就不用每次都去執行了

export?PYTHONPATH=/home/xugaoxiang/Works/github/caffe/python:$PYTHONPATH

Q & A

Q1

編譯過程碰到了tiff相關的錯誤

caffe

這是由于之前opencv源碼編譯引起的，這里特別要注意一點，如果是從源碼開始編譯opencv，那么在配置的時候一定要加上選項-D BUILD_TIFF=ON。還有就是盡量不要同時擁有apt和conda安裝的2種環境，對新手來說比較容易出錯。

Q2

進入ipython中，import caffe報錯

caffe

將libhdf5_hl.so.100的路徑加入LD_LIBRARY_PATH中

export?LD_LIBRARY_PATH=/home/xugaoxiang/anaconda3/lib:$LD_LIBRARY_PATH

Q3

進入ipython中，import caffe報錯

caffe

修改Makefile.config，修改PYTHON_LIBRARIES為

PYTHON_LIBRARIES?:=?boost_python3?python3.7m

默認的是python2

Q4

關于caffe中使用源碼編譯的opencv4，由于opencv4的版本差異，都會報錯

caffe

這是由于在opencv4中，原來版本中的宏CV_LOAD_IMAGE_COLOR和CV_LOAD_IMAGE_GRAYSCALE已經改成了cv::IMREAD_COLOR和cv::ImreadModes::IMREAD_GRAYSCALE，所以，需要在caffe源碼目錄中查找并替換，才能夠編譯成功

caffe

參考資料

• anaconda基本使用
• ubuntu安裝CUDA和cuDNN
• opencv源碼編譯，支持CUDA
• https://github.com/BVLC/caffe/issues/4436

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