复盘caffe安装
最近因之前的服務器上的caffe奔潰了，不得已重新安裝這一古老的深度學習框架，之前也嘗試了好幾次，每次都失敗，這次總算是成功了，因此及時地總結一下。
以下安裝的caffe主要是針對之前虹膜分割和鞏膜分割所需的caffe版本。之前已經出過一個安裝指南，部分內容與之重疊。
其實整個caffe安裝最困難的部分就是各個依賴軟件的安裝，因此我們只要把這部分安裝好了就可以了。
注意：安裝指南，請參考《深度學習：21天實戰caffe》的第5天那一章，只要按照上面所說的，將所有依賴文件安裝在一個目錄下，即可。最后安裝好的列表應該為：
最后，修改caffe目錄下的Makefile.config，找到如下幾行：
完整的Makefile.config參考：
## 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 layersUSE_OPENCV := 1 # 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 3OPENCV_VERSION := 2# 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. 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 := 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 := /opt/OpenBLAS/include BLAS_LIB := /opt/OpenBLAS/lib# 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/R2015a/ # 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)/anaconda2PYTHON_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/libPYTHON_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 :=/home/caiyong.wang/bin/caffe_local_install/include $(PYTHON_INCLUDE) /usr/local/include LIBRARY_DIRS :=/home/caiyong.wang/bin/caffe_local_install/lib $(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 $LIBRARY_DIRS.) USE_PKG_CONFIG := 1# N.B. both build and distribute dirs are cleared on `make clean` BUILD_DIR := build DISTRIBUTE_DIR := distribute# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171 # DEBUG := 1# The ID of the GPU that 'make runtest' will use to run unit tests. TEST_GPUID := 0# enable pretty build (comment to see full commands) Q ?= @LINKFLAGS := -Wl,-rpath,$(ANACONDA_HOME)/lib
下面是一些細節：
1. 首先必須安裝python2, 推薦直接安裝anaconda2, 例如：https://repo.anaconda.com/archive/Anaconda2-5.0.0.1-Linux-x86_64.sh?，并添加路徑到PATH環境變量。
2. opencv的安裝
對于opencv,需要安裝兩個版本，一個是python的，一個是系統的。
對于python，可以安裝?
pip install opencv-python==4.2.0.34直接安裝pip install opencv-python,可能出現 “ModuleNotFoundError: No module named 'skbuild'?#105”，因此推薦降級安裝。
參考：https://github.com/openvinotoolkit/dlstreamer_gst/issues/105
對于系統的安裝，方法為：
opencv一般安裝opencv 2.4.9, 首先下載opencv-2.4.9.zip源碼，并解壓：
unzip opencv-2.4.9.zip cd opencv-2.4.9/ mkdir build; cd build/ cmake .. ccmake ..這里要用到cmake與ccmake，可以自己安裝，參考：?https://www.24kplus.com/linux/557.html
https://www.cnblogs.com/tosser/p/9905597.html
https://blog.csdn.net/sheqianweilong/article/details/102582668
注意編譯過程中需要加入：?
./bootstrap --prefix=/usr --datadir=share/cmake --docdir=doc/cmake --qt-gui && make // 參數--qt-gui是安裝cmake-gui，不想安裝可以省略?繼續回到opencv的安裝，注意在caffe中僅僅使用圖像讀取和縮放等在CPU上的簡單模塊，因此可以禁用大部分無關模塊以節省編譯時間。
然后進行編譯安裝，
make && make install至此安裝成功。安裝成功后，可以將opencv涉及到的bin,lib,pkgconfig填到~/.bashrc，
export PATH=/home/caiyong.wang/bin/caffe_local_install/bin:$PATH export LD_LIBRARY_PATH=/home/caiyong.wang/bin/caffe_local_install/lib/:$LD_LIBRARY_PATHexport PKG_CONFIG_PATH=/home/caiyong.wang/bin/caffe_local_install/lib/pkgconfig/:$PKG_CONFIG_PATH然后source ~/.bashrc刷新。
opencv安裝成功后，可以運行以下的程序進行測試，測試通過后，證明opencv安裝成功，可以順利安裝caffe.
先編輯一個main函數，命名為main.cpp：
#include <stdio.h> #include <opencv2/opencv.hpp> using namespace cv; int main(void) { printf("%s\r\n", CV_VERSION); printf("%u.%u.%u\r\n", CV_MAJOR_VERSION, CV_MINOR_VERSION, CV_SUBMINOR_VERSION); }然后執行：
?g++ -o main main.cpp `pkg-config opencv --cflags --libs`最后執行： ./main?
PS：對于pkgconfig的一些用法，如下：
[caiyong.wang@omnisky opencv_test]$ pkg-config --cflags opencv -I/home/caiyong.wang/bin/caffe_local_install/include/opencv -I/home/caiyong.wang/bin/caffe_local_install/include [caiyong.wang@omnisky opencv_test]$ pkg-config --libs opencv /home/caiyong.wang/bin/caffe_local_install/lib/libopencv_calib3d.so /home/caiyong.wang/bin/caffe_local_install/lib/libopencv_core.so /home/caiyong.wang/bin/caffe_local_install/lib/libopencv_features2d.so /home/caiyong.wang/bin/caffe_local_install/lib/libopencv_flann.so /home/caiyong.wang/bin/caffe_local_install/lib/libopencv_highgui.so /home/caiyong.wang/bin/caffe_local_install/lib/libopencv_imgproc.so /home/caiyong.wang/bin/caffe_local_install/lib/libopencv_ml.so /home/caiyong.wang/bin/caffe_local_install/lib/libopencv_objdetect.so /home/caiyong.wang/bin/caffe_local_install/lib/libopencv_photo.so /home/caiyong.wang/bin/caffe_local_install/lib/libopencv_stitching.so /home/caiyong.wang/bin/caffe_local_install/lib/libopencv_ts.a -lrt -lpthread -lm -ldl?
3.?protobuf的安裝
同上，也必須安裝兩個版本，一個是python，一個是系統。
python安裝：?直接pip install protobuf或者conda install protobuf。
系統安裝：參考：https://wangcaiyong.blog.csdn.net/article/details/78283336
參考：https://blog.csdn.net/dgyuanshaofeng/article/details/78151510
?
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