linux ubuntu16.04下caffe的安装和编译

怼烎@ 2022-02-04 02:59 275阅读 0赞

*  已安装好Anoconda 2：[https://blog.csdn.net/weixin\_41770169/article/details/89886093][https_blog.csdn.net_weixin_41770169_article_details_89886093]
 *  已安装好cuda：查看版本号：[https://blog.csdn.net/weixin\_41770169/article/details/89886355][https_blog.csdn.net_weixin_41770169_article_details_89886355]
 *  已安装好cudnn：查看版本号：[https://blog.csdn.net/weixin\_41770169/article/details/89886355][https_blog.csdn.net_weixin_41770169_article_details_89886355]

1、安装caffe依赖库

进入官网[http://caffe.berkeleyvision.org/installation.html][http_caffe.berkeleyvision.org_installation.html]，可以看到编译caffe需要的依赖库

`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 libgflags-dev libgoogle-glog-dev liblmdb-dev`  
`sudo apt-get install libatlas-dev`  
`sudo apt-get install liblapack-dev`  
`sudo apt-get install libatlas-base-dev`

2、caffe代码下载

git clone [https://github.com/BVLC/caffe.git][https_github.com_BVLC_caffe.git]

3、修改makefile文件

## 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
    # This code is taken from https://github.com/sh1r0/caffe-android-lib
    # USE_HDF5 := 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-8.0
    # 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_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 := 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
    
    # 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.
    ANACONDA_HOME := /home/xuqiong/anaconda2
    PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
             $(ANACONDA_HOME)/include/python2.7 \
             $(ANACONDA_HOME)/lib/python2.7/site-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 /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 ?= @

如果如下编译过程中，出现cv错误；则查看当前cv版本。

pkg-config --modversion opencv

caffe 默认opencv版本为2，如果版本为3，则将上述：OPENCV\_VERSION := 3打开。

如opencv版本为2报错，查看博文：

[https://blog.csdn.net/weixin\_41770169/article/details/90413895][https_blog.csdn.net_weixin_41770169_article_details_90413895]

4、编译

（1）sudo make all -j8

（2）sudo make test -j4

（3）sudo make runtest -j4

![20190521200725911.png][]

如果报错：找不到libhdf\_hl\_so.100；原因：没有将anaconda的lib路径加入LD\_LIBRARY\_PATH

执行如下命令，在.bashrc中添加，即可：

export LD\_LIBRARY\_PATH="$LD\_LIBRARY\_PATH:/home/xuqiong/anaconda2/lib" >> ~/.bashrc

sudo ldconfig

（4）sudo make pycaffe

![20190521200858974.png][]

5、测试是否安装成功

返回上一级目录，import caffe

（1）报错：ImportError: numpy.core.multiarray failed to import

解决方法：conda install scikit-image

（2）报错：ImportError: No module named google.protobuf.internal

解决方法：conda install protobuf

（3）报错：no module named caffe

解决方法：PYTHONPATH路径加上caffe/python路径，执行以下指令

export PYTHONPATH="$PYTHONPATH:/home/xuqiong/makeall/caffe/python" >> ~/.bashrc

sudo ldconfig

成功解决：

![20190506193350996.png][]

另外：查看某个环境变量值

echo $PYTHONPATH

参考：

[https://www.jianshu.com/p/0cad02ad323b][https_www.jianshu.com_p_0cad02ad323b]

[https://blog.csdn.net/CAU\_Ayao/article/details/80582900][https_blog.csdn.net_CAU_Ayao_article_details_80582900]

[https://www.cnblogs.com/taopanpan/p/4263951.html][https_www.cnblogs.com_taopanpan_p_4263951.html]

[https://blog.csdn.net/CAU\_Ayao/article/details/80578600][https_blog.csdn.net_CAU_Ayao_article_details_80578600]

[https_blog.csdn.net_weixin_41770169_article_details_89886093]: https://blog.csdn.net/weixin_41770169/article/details/89886093
[https_blog.csdn.net_weixin_41770169_article_details_89886355]: https://blog.csdn.net/weixin_41770169/article/details/89886355
[http_caffe.berkeleyvision.org_installation.html]: https://links.jianshu.com/go?to=http%3A%2F%2Fcaffe.berkeleyvision.org%2Finstallation.html
[https_github.com_BVLC_caffe.git]: https://github.com/BVLC/caffe.git
[https_blog.csdn.net_weixin_41770169_article_details_90413895]: https://blog.csdn.net/weixin_41770169/article/details/90413895
[20190521200725911.png]: /images/20220204/6fbff2b34b164b489b2c85a0a6e46daa.png
[20190521200858974.png]: /images/20220204/5d38f0e8de274a4fb73d8714c59ecfb7.png
[20190506193350996.png]: /images/20220204/5d72eb801a0140b1af94c88533fd347f.png
[https_www.jianshu.com_p_0cad02ad323b]: https://www.jianshu.com/p/0cad02ad323b
[https_blog.csdn.net_CAU_Ayao_article_details_80582900]: https://blog.csdn.net/CAU_Ayao/article/details/80582900
[https_www.cnblogs.com_taopanpan_p_4263951.html]: https://www.cnblogs.com/taopanpan/p/4263951.html
[https_blog.csdn.net_CAU_Ayao_article_details_80578600]: https://blog.csdn.net/CAU_Ayao/article/details/80578600