caffe是深度学习在图像领域广泛使用的框架,其model zoo有大量的预训练好的模型提供使用。大部分图像相关的应用部分将用到caffe。
墙裂建议大家使用linux系统,原因如下。
linux系统(大部分公司为CentOS或者Ubuntu),才是实际开发中用到的系统,提前熟悉一下命令行,对于实际应用和开发都很有必要。
相对于Windows而言,linux下的依赖包安装非常简单。
Windows下编译经常会出现大量问题,且现在网上Windows下caffe出现问题的解决方案非常少
大量的二次开发和扩展都只有linux版本
下面是安装caffe的一些笔记,写的不明白的,欢迎大家提问^_^
这个说明是关于linux系统的,最好是centOS 7.0以上,或者ubuntu 14.04 以上
因为低版本的装不上兼容合适的boost,opencv等库
不配的话很多依赖库都需要自己手动编译和指定caffe编译路径,耗时且经常编译不成功
在国内的话用sohu或者163的源
rpm -Uvh http://mirrors.sohu.com/fedora-epel/7/x86_64/e/epel-release-7-2.noarch.rpm
让新的源生效
yum repolist
要确认一下,所有的库都装上了,否则编译出来可能不能使用。
其中protobuf是用来定义layers的,leveldb是训练时存储图片数据的数据库,opencv是图像处理库,boost是通用C++库,等等...
sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
sudo yum install openblas-devel.x86_64 gcc-c++.x86_64 numpy.x86_64 scipy.x86_64 python-matplotlib.x86_64 lapack-devel.x86_64 python-pillow.x86_64 libjpeg-turbo-devel.x86_64 freetype-devel.x86_64 libpng-devel.x86_64 openblas-devel.x86_64
sudo yum install gflags-devel glog-devel lmdb-devel
yum install git
git clone https://github.com/BVLC/caffe.git
wget --no-check-certificate https://bootstrap.pypa.io/ez_setup.py
python ez_setup.py --insecure
wget https://bootstrap.pypa.io/get-pip.py
python get-pip.py
cd caffe/python
执行
for req in $(cat requirements.txt); do pip install $req; done
这步安装也有点慢,别急,等会儿,先去干点别的 ^_^
cd caffe
cp Makefile.config.example Makefile.config
vim Makefile.config
Makefile.config里面有依赖库的路径,及各种编译配置,如果是没有GPU的情况下,可以参照我下面帮你改的配置文件内容:
## 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 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 BLAS := open # 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 BLAS_INCLUDE := /usr/include/openblas # 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)/anaconda # 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 # 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 # 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 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 ?= @
make -j4
编译可能会有点慢,你可以先去干点别的事情
测试一下编译结果
make test
make runtest
make pycaffe -j4
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