软硬件环境
- ubuntu 18.04 64bit
- NVidia GTX 1070Ti
- anaconda with python 3.7
- CUDA 10.1
- cuDNN 7.6
- opencv 3.4.2
- caffe 1.0.0
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简介
先说一下环境，使用 anaconda 的 python 虚拟环境，支持 opencv，支持 CUDA 和 cuDNN 加速，支持在 python 中调用 caffe。基础组件部分可以参考前面的文章，本文就不赘述了
基础环境准备
安装依赖包和工具
sudo apt install build-essential cmake git ffmpeg libatlas-base-dev libtiff-dev pkg-config python3-dev libavcodec-dev libavformat-dev libswscale-dev libtbb-dev libjpeg-dev libpng-dev libavcodec-dev libavformat-dev libswscale-dev libv4l-dev libx264-dev libboost-all-dev libhdf5-serial-dev libleveldb-dev liblmdb-dev libhdf5-dev
pip install protobufopencv
这里把 opencv 单独拿出来说，是因为 opencv 的安装方法非常多
- apt install python3-opencv
- conda install opencv
- 源码编译
通过 apt install 安装最简单，也是最不容易出错的方法；其次是 conda install，最容易出问题的是自己编译源码，编译参数复杂，依赖库繁多，而且还有版本差异。
安装完成后，建议使用 opencv_version 命令来查看当前版本，默认 ubuntu 18.04 源提供的是 3.2.0 版本，conda 的会更高一些，这里是 3.4.0，源码安装的话，注意在 sudo make install 后再执行一句 sudo ldconfig。本文以 conda 的方式进行安装。
编译caffe
接下来就可以编译 caffe 了
git clone https://github.com/BVLC/caffe.git
cd caffe
cp Makefile.config.example Makefile.config编辑文件 Makefile.config，主要是一些路径的修改，贴上已经修改好的
## 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).
# 启用cuDNN加速
USE_CUDNN := 1
# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1
# uncomment to disable IO dependencies and corresponding data layers
# 启用opencv
USE_OPENCV := 1
# USE_LEVELDB := 0
# USE_LMDB := 0
# This code is taken from https://github.com/sh1r0/caffe-android-lib
USE_HDF5 := 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大版本号是3，这里一定要注意
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 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.
# -gencode arch=compute_20,code=sm_21
# CUDA_ARCH := -gencode arch=compute_20,code=sm_20
# 这里使用的是CUDA10.1，所以要注释掉前两行
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 := 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_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头文件路径，再加上numpy的头文件路径
PYTHON_INCLUDE := /home/xugaoxiang/anaconda3/include/python3.7m \
/home/xugaoxiang/anaconda3/lib/python3.7/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)
# 默认是python2，这里使用python3，一定要改，不然后面会报错相应没人在用python2了吧
PYTHON_LIBRARIES := boost_python3 python3.7m
# 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.
# libpython*.so库的路径
PYTHON_LIB := /home/xugaoxiang/anaconda3/lib
# 如果设置了ANACONDA_HOME环境变量，可以使用下面的设置方法，作用一样
# 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 /home/xugaoxiang/anaconda3/include
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 $LIBRARY_DIRS.)
# 启用pkg_config，方便caffe找到opencv
USE_PKG_CONFIG := 1
# N.B. both build and distribute dirs are cleared on `make clean`
# 默认编译的目录，所有的目标文件、可执行文件、库都存放在这里
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# 是否打开debug信息
# DEBUG := 1
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0
# enable pretty build (comment to see full commands)
Q ?= @完成后，执行
make all -j12参数 -j 指的是使用多少个 cpu 核心，目的是加快编译速度，根据自己的实际情况设定
为了能在 python 中调用 caffe，还需要执行
make pycaffe -j12至此，整个编译就结束了。
验证
使用 ipython 环境测试
如果细心一点，会发现，新开一个 terminal，同样打开 ipython，同样 import caffe，但是会报错，这是什么原因？
在不报错的 terminal 中，查看环境变量就会发现端倪
在编译 caffe 的过程中，会 export 环境变量 PYTHONPATH，所以我们在使用前也需要这样做
为了简便，可以将声明语句写入 ~/.bashrc 中，就不用每次都去执行了
export PYTHONPATH=/home/xugaoxiang/Works/github/caffe/python:$PYTHONPATHQ & A
Q1
编译过程碰到了 tiff 相关的错误
这是由于之前 opencv 源码编译引起的，这里特别要注意一点，如果是从源码开始编译 opencv，那么在配置的时候一定要加上选项 -D BUILD_TIFF=ON。还有就是尽量不要同时拥有 apt 和 conda 安装的2种环境，对新手来说比较容易出错。
Q2
进入 ipython 中，import caffe 报错
将 libhdf5_hl.so.100 的路径加入 LD_LIBRARY_PATH 中
export LD_LIBRARY_PATH=/home/xugaoxiang/anaconda3/lib:$LD_LIBRARY_PATHQ3
进入 ipython 中，import caffe 报错
修改 Makefile.config，修改 PYTHON_LIBRARIES 为
PYTHON_LIBRARIES := boost_python3 python3.7m默认的是 python2
Q4
关于 caffe 中使用源码编译的 opencv4，由于 opencv4 的版本差异，都会报错
这是由于在 opencv4 中，原来版本中的宏 CV_LOAD_IMAGE_COLOR 和 CV_LOAD_IMAGE_GRAYSCALE 已经改成了 cv::IMREAD_COLOR 和 cv::ImreadModes::IMREAD_GRAYSCALE，所以，需要在 caffe 源码目录中查找并替换，才能够编译成功
Q5
编译过程中报 hdf5 头文件找不到？即使是执行了安装命令也一样
sudo apt install libhdf5-devsrc/caffe/layers/hdf5_data_layer.cu:10:10: fatal error: hdf5.h: No such file or directory
#include "hdf5.h"
^~~~~~~~
compilation terminated.
Makefile:604: recipe for target '.build_release/cuda/src/caffe/layers/hdf5_data_layer.o' failed
make: *** [.build_release/cuda/src/caffe/layers/hdf5_data_layer.o] Error 1
make: *** Waiting for unfinished jobs....这时候需要修改 Makefile.config 文件，在 INCLUDE_DIR 后添加路径 /usr/include/hdf5/serial，在 LIBRARY_DIR 后添加 /usr/lib/x86_64-linux-gnu/hdf5/serial
