ubuntu 16.04 cpu only 安装caffe

在ubuntu 下，使用CPU模式安装caffe，在这个过程中，其实没有什么太多难点，主要就是各种依赖包的安装，而且还有版本的问题，由于caffe编写的过程中，用了许多第三方库，而且都是和某一个版本对应的，因此安装的过程是需要注意这一点的。

1、首先把opencv安装了：

可参考一下几篇文章：

http://blog.csdn.net/surgewong/article/details/39078251#

http://blog.csdn.net/a1429331875/article/details/31539129

http://blog.csdn.net/tina_ttl/article/details/52745807

具体安装过程：

opencv的github源文件：https://github.com/opencv/opencv，下载master分支，或者2.4版本分支，git clone到本地，然后安装opencv最关键的是安装各种依赖包：

mkdir build，进入cmake ..，make -j8,，make install等即可：如下所示

安装完成后，便要做一些收尾工作：

至此完成了opencv的安装。

2、完成caffe的安装：

在可能的情况下，安装anaconda2或者anaconda3，这里采用anaconda2，已有默认~/.bashrc中路径的导入要优先搜索anaconda2（切记）

需要装一些依赖库：

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler  

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

sudo apt-get install libatlas-base-dev

sudo apt-get install libhdf5-serial-dev

等安装完了依赖，便可以进入到caffe的目录中，执行：cp Makefile.config.example Makefile.config

然后修改Makefile.config文件：cpu-only关键的就需要改几个地方：

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

这里安装的主要是opencv3，（github下面master分支），因此是opencv3的版本，可以进入到/usr/local/lib下面查看opencv的版本

# Uncomment if you're using OpenCV 3
OPENCV_VERSION := 3

注释掉python的路径，替换成anaconda的路径：

# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
ANACONDA_HOME := $(HOME)/anaconda2
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
$(ANACONDA_HOME)/include/python2.7 \
$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include

# We need to be able to find libpythonX.X.so or .dylib.
# PYTHON_LIB := /usr/lib
PYTHON_LIB := $(ANACONDA_HOME)/lib

# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial

修改之后，便可以mkdir build, cd build, cmake .., make all -j8即完成编译链接。

然后修改python的路径：

去到CAFFE文件夹里面的python文件夹, 把当前路径记录下来(pwd). 然后输入以下命令(把记下的路径放在相应地方)

export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH

之后便可以跑一些demo了。

在这个过程中，发现关键是opencv和google protobuf的安装版本对应问题：

make all -j8编译链接到81%的时候，总是出错，说protobuf找不到，有冲突，主要是因为apt-get 安装了protobuf，conda install 也安装了protobuf，这样版本不一致。

解决方案是：使用conda list protobuf查看当前conda 下安装了几个版本。然后使用conda remove protobuf来删除掉。现在只留apt-get install 下的protobuf，然后安装，在安装完之后，import caffe的话，会出现没有protobuf的问题，此时再conda install protobuf来重新安装python版的protobuf，这样就可以import caffe了。

一些问题和解决方案：

python caffe报错：No module named google.protobuf.internal: http://blog.csdn.net/wuzuyu365/article/details/52431062

caffe支持pycaffe的安装:ImportError: No module named google.protobuf.internal : http://blog.csdn.net/liyaohhh/article/details/50876777

ImportError: /home/kzl/anaconda2/bin/../lib/libstdc++.so.6: version `GLIBCXX_3.4.21' not found : http://blog.csdn.net/lwgkzl/article/details/77658269

ubuntu14.04安装caffe-master 报错：./include/caffe/util/mkl_alternate.hpp:11:19:fatal error:cblas.h: No such file or directory  #include<cblas.h>: http://blog.csdn.net/feelingjun/article/details/68512659

在安装的过程中，发现google::protobuf总是在链接的过程中出现问题，报错内容是

Caffe compile error: undefined reference to `google::protobuf #4558，原因是conda中安装了libprotobuf和protobuf，与系统使用apt-get install方式安装的版本冲突，果断采用conda list protobuf和conda remove protobuf来删除，最终编译通过。

protobuf的安装：http://blog.csdn.net/hailong0715/article/details/52057873

安装caffe的方法：

http://blog.csdn.net/fromlimbo/article/details/51038749

http://www.linuxidc.com/Linux/2016-09/135034.htmX

https://chunml.github.io/ChunML.github.io/project/Installing-Caffe-CPU-Only/

(1) MKL，ATLAS的问题;http://blog.csdn.net/feelingjun/article/details/68512659

(2) GLIBCXX_3.4.21' not found: http://blog.csdn.net/lwgkzl/article/details/77658269

(3) ImportError: No module named google.protobuf.internal: http://blog.csdn.net/liyaohhh/article/details/50876777, http://blog.csdn.net/wuzuyu365/article/details/52431062

(4) python can't import _caffe module: https://github.com/BVLC/caffe/issues/263

(5) recipe for target '.build_release/tools/extract_features.bin' failed: 发现编译链接之后，缺少文件，重新编译一下

(6) Error: 'make all' 'make test' #2348: https://github.com/BVLC/caffe/issues/2348

(7) build failed in "make -j8 && make pycaffe" step #72: https://github.com/rbgirshick/fast-rcnn/issues/72

(8) Google protobuf in Linux: https://stackoverflow.com/questions/2456664/google-protobuf-in-linux， https://github.com/BVLC/caffe/issues/3046

(9) protobuf安装: http://www.cnblogs.com/luoxn28/p/5303517.html

(10) Caffe compile error: undefined reference to `google::protobuf #4558: https://github.com/BVLC/caffe/issues/4558

Caffe样例测试：

http://blog.sciencenet.cn/blog-1583812-843207.html

http://blog.csdn.net/wangxing233/article/details/51734080

http://blog.csdn.net/forest_world/article/details/51376554

Makefile.config

## 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).
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.
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.
ANACONDA_HOME := $(HOME)/anaconda2
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
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 /usr/include/hdf5/serial
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.)
# 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 ?= @