caffe pycaffe以及matcaffe安装

0. 安装环境

• Ubuntu: 16.04
• Python: 2.7
• Caffe: latest

1. 安装依赖

1.1 基本依赖

# general dependenciessudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compilersudo apt-get install --no-install-recommends libboost-all-dev# Python-devsudo apt-get install python-dev # for building the pycaffe interface.# Remaining dependencies, 14.04sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

另外，如果使用Python接口，再安装个画图的依赖：

sudo apt-get install graphviz

1.2 Python依赖

如果需要使用Caffe的Python接口，那么需要安装如下Python包：

• Cython>=0.19.2
• numpy>=1.7.1
• scipy>=0.13.2
• scikit-image>=0.9.3
• matplotlib>=1.3.1
• ipython>=3.0.0
• h5py>=2.2.0
• leveldb>=0.191
• networkx>=1.8.1
• nose>=1.3.0
• pandas>=0.12.0
• python-dateutil>=1.4,<2
• protobuf>=2.5.0
• python-gflags>=2.0
• pyyaml>=3.10
• Pillow>=2.3.0
• six>=1.1.0

一键安装命令：

cd $CAFFE_ROOT/pythonfor req in $(cat requirements.txt); do sudo pip install $req; done

另外，安装pydot用于绘图:

pip install pydot>=1.2.3

1.3 matlab依赖

安装好matlab即可

2. 安装caffe

cd $caffe目录# 配置Makefile.config  cp Makefile.config.example Makefile.config# uncomment CPU_ONLY := 1 in Makefile.config.（仅CPU模式）# uncomment OPENCV_VERSION := 3  if you're using OpenCV 3# 编译make cleanmake all -j 8# 测试make test -j 8make runtest -j 8

3. 安装 pycaffe 以及 matcaffe

3.2 pycaffe

 # set your PYTHON paths in Makefile.config（python 2已经默认配置好了，如果使用python3 需要再配置一下） make pycaffe make pytest

3.1 matcaffe

# 在/etc/profile中配置PATHexport PATH = /mnt/sda4/MATLAB/R2015b/bin:$PATHsource /etc/profile# uncomment MATLAB_DIR := $YOUR MATLAB PATH, AND MATLAB directory should contain the mex binary in /bin.  MATLAB_DIR :=  /mnt/sda4/MATLAB/R2015b # set MATLAB_DIR in Makefile.config make matcaffe make mattest

4. 安装遇到的问题

4.1 did not match C++ signature

• 错误 信息：

======================================================================ERROR: test_save_and_read (test_net.TestNet)----------------------------------------------------------------------Traceback (most recent call last):  File "/home/fujie/tuguanghui/caffe/python/caffe/test/test_net.py", line 141, in test_save_and_read    self.net.save(f.name)ArgumentError: Python argument types in    Net.save(Net, str)did not match C++ signature:    save(caffe::Net<float>, std::string)======================================================================ERROR: test_save_hdf5 (test_net.TestNet)----------------------------------------------------------------------Traceback (most recent call last):  File "/home/fujie/tuguanghui/caffe/python/caffe/test/test_net.py", line 158, in test_save_hdf5    self.net.save_hdf5(f.name)ArgumentError: Python argument types in    Net.save_hdf5(Net, str)did not match C++ signature:    save_hdf5(caffe::Net<float>, std::string)

• 解决方法
  上述问题是由于Boost版本的问题，安装boost_1_60_0来解决。

 wget -o http://sourceforge.net/projects/boost/files/boost/1.60.0/boost_1_60_0.tar.gz/downloadtar xzvf boost_1_60_0.tar.gzcd boost_1_60_0/sudo apt-get updatesudo apt-get install build-essential g++ python-dev autotools-dev libicu-dev build-essential libbz2-dev libboost-all-dev. ./bootstrap.sh./b2sudo ./b2 install sudo ldconfig -v # 更新动态链接库

附： Makefile.config参考

## 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 := 0USE_LEVELDB := 0 USE_LMDB := 1# 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 3OPENCV_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 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_50,code=compute_50# BLAS choice:# atlas for ATLAS (default)# mkl for MKL# open for OpenBlasBLAS := 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/R2015b# 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)/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)# 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/serialLIBRARY_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 := buildDISTRIBUTE_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 ?= @

参考文献

[1]安装boost参考