Ubuntu16.04搭建caffe環境（cpu-only)與Python調用

本文參考caffe官網教程以及網上的兩篇教程：Ubuntu14.04+CPU+Python的Caffe安裝教程、Caffe學習系列(13)：數據可視化環境（python接口)配置編寫而成，由於過程比較波折，記錄下來以備往後查用

安裝編譯caffe的各類依賴

安裝基礎依賴

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler 
sudo apt-get install --no-install-recommends libboost-all-dev

安裝BLAS

sudo apt-get install libatlas-base-dev

其餘的一些依賴

sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

Python Caffe

安裝Python

sudo apt-get install python-dev

###安裝anaconda 從清華的鏡像下載anaconda，根據anaconda官網提供的版本號，從清華鏡像的目錄中查找到對應的版本爲Anaconda2-5.0.1-Linux-x86_64.sh（python2.7版本）

wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda2-5.0.1-Linux-x86_64.sh

下載完成後，運行這個sh。在安裝的過程當中，會問你安裝路徑，直接回車默認就能夠了。有個地方問你是否將anaconda安裝路徑加入到環境變量（.bashrc)中，這個必定要輸入yes。

安裝成功後，會有當前用戶根目錄下生成一個anaconda2的文件夾，裏面就是安裝好的內容。

下載Caffe

git clone https://github.com/BVLC/caffe.git

配置python

將caffe根目錄下的python文件夾加入到環境變量，等編譯好了之後就能夠import caffe來使用了

打開配置文件bashrc

sudo vi ~/.bashrc

在最後面加入

export PYTHONPATH=$(caffe_path)/python:$PYTHONPATH

注意 $(caffe_path) 須要根據本身caffe安裝路徑的實際狀況配置

保存退出，更新配置文件

source ~/.bashrc

修改Makefile.config

首先，在caffe的根目錄下複製Makefile.config.example 成 Makefile.config

cp Makefile.config.example Makefile.config

而後修改Makefile.config，個人配置以下所示

## 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).
# USE_CUDNN := 1

# CPU-only switch (uncomment to build without GPU support).
#由於使用的是cpu模式，因此要放開
 CPU_ONLY := 1

# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 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_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 < 6.0, comment the *_50 through *_61 lines for compatibility.
# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
# For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility.
CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
		-gencode arch=compute_20,code=sm_21 \
		-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_INCLUDE := /usr/include/python2.7 \
		/usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
#由於使用了anaconda因此要配置anaconda的地址，若是不配置則會出現找不到*.h的狀況
 ANACONDA_HOME := $(HOME)/anaconda2
#使用anaconda的頭文件
 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)
# PYTHON_LIBRARIES := boost_python3 python3.5m
# 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.
# PYTHON_LIB := /usr/lib
#使用anaconda的庫
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
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib

# 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.)
# USE_PKG_CONFIG := 1

# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute

# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# 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 ?= @

編譯caffe

make pycaffe -j8
make all -j8
make test -j8
make runtest -j8

運行python

進入caffe/python ，運行python

python
Python 2.7.14 |Anaconda custom (64-bit)| (default, Oct 16 2017, 17:29:19) 
[GCC 7.2.0] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import caffe
>>>

輸入import caffe沒有反應則說明成功，但若是出現問題error ：No module named google.protobuf.internal，則運行如下代碼：

conda install protobuf

接下來，運行caffe自帶的例子，可參考運行caffe自帶的兩個簡單例子