caffe pycaffe以及matcaffe安装

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 dependencies
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
# Python-dev
sudo apt-get install python-dev # for building the pycaffe interface.
# Remaining dependencies, 14.04
sudo 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/python
for 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 clean
make all -j 8
# 测试
make test -j 8
make 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PATH
export PATH = /mnt/sda4/MATLAB/R2015b/bin:$PATH
source /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/download tar xzvf boost_1_60_0.tar.gz
cd boost_1_60_0/
sudo apt-get update
sudo apt-get install build-essential g++ python-dev autotools-dev libicu-dev build-essential libbz2-dev libboost-all-dev
. ./bootstrap.sh
./b2
sudo ./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 := 0
USE_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 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 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 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/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/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 ?= @

参考文献

[1]安装boost参考