本文参考caffe官网教程以及网上的两篇教程:Ubuntu14.04+CPU+Python的Caffe安装教程、Caffe学习系列(13):数据可视化环境(python接口)配置编写而成,因为过程比较波折,记录下来以备日后查用
安装编译caffe的各种依赖
安装基础依赖
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
安装BLAS
sudo apt-get install libatlas-base-dev
其他的一些依赖
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
Python Caffe
安装Python
sudo apt-get install python-dev
###安装anaconda 从清华的镜像下载anaconda,根据anaconda官网提供的版本号,从清华镜像的目录中查找到对应的版本为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
下载完成后,运行这个sh。在安装的过程中,会问你安装路径,直接回车默认就可以了。有个地方问你是否将anaconda安装路径加入到环境变量(.bashrc)中,这个一定要输入yes。
安装成功后,会有当前用户根目录下生成一个anaconda2的文件夹,里面就是安装好的内容。
下载Caffe
git clone https://github.com/BVLC/caffe.git
配置python
将caffe根目录下的python文件夹加入到环境变量,等编译好了以后就可以import caffe来使用了
打开配置文件bashrc
sudo vi ~/.bashrc
在最后面加入
export PYTHONPATH=$(caffe_path)/python:$PYTHONPATH
注意 $(caffe_path) 需要根据自己caffe安装路径的实际情况配置
保存退出,更新配置文件
source ~/.bashrc
修改Makefile.config
首先,在caffe的根目录下复制Makefile.config.example 成 Makefile.config
cp Makefile.config.example Makefile.config
然后修改Makefile.config,我的配置如下所示
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## 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).
#因为使用的是cpu模式,所以要放开
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.
#因为使用了anaconda所以要配置anaconda的地址,如果不配置则会出现找不到*.h的情况
ANACONDA_HOME := $(HOME)/anaconda2
#使用anaconda的头文件
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的库
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 ?= @
编译caffe
make pycaffe -j8
make all -j8
make test -j8
make runtest -j8
运行python
进入caffe/python ,运行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
>>>
输入import caffe没有反应则说明成功,但如果出现问题error :No module named google.protobuf.internal,则运行以下代码:
conda install protobuf
接下来,运行caffe自带的例子,可参考运行caffe自带的两个简单例子