Ubuntu18.04 安装py-fast-rcnn步骤
因工作需要，在跑模型的时候，需要使用到caffe，网上关于ubuntu18.04安装caffe的步骤各式各样，在经历过三天安装卸载的绝望中，终于编译完成，记录这三天中遇到的坑以及安装步骤，方便后续有需要时查看。
1.直接用命令行安装
有一种方法是通过命令行安装，如下：
1.sudo apt install caffe-cuda
2.sudo apt install caffe-cpu但是，笔者这种方法并没有成功，因此觉得自己编译麻烦的小伙伴可以尝试使用这种方法，万一成功了呢。
2.下载caffe并自行编译
注：在下载前一定要明确自己能使用caffe的版本，不然好不容易编译成功，却发现因为版本问题导致某些层没有，是一件很蛋疼的事情。笔者给出了两个常用版本的下载链接，如需其他版本，请自行寻找。
2.1环境安装
sudo apt install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt install --no-install-recommends libboost-all-dev
sudo apt install python-dev
sudo apt install libatlas-base-dev
sudo apt install libgflags-dev libgoogle-glog-dev liblmdb-dev
sudo apt install python-opencv2.2下载caffe
从官方下载的caffe默认的是1.0.0版本的，下载连接如下：
git clone https://github.com/BVLC/caffe.git从py-faster-rcnn中下载的caffe是1.0.0-rc3版本的，下载连接如下：
git clone https://github.com/rbgirshick/py-faster-rcnn.git2.3编译caffe（以py-fast-rcnn为例）
（1）进入下载好的文件夹，在caffe-fast-rcnn目录下找到Makefile.config.example, 复制一份并重命名为Makefile.config
（2）编辑Makefile.config
gedit Makefile.config（3）修改内容，参考如下（一些重要的修改用中文给出了注释）
## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!
# 使用GPU加速，则去除此行注释，前提是你已经安装了cuda，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
# 使用OPEN_CV
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-11.4
# 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_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 := 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_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
# 根据你自己的目录确定相应文件的位置（绝对地址）
PYTHON_LIBRARIES := boost_python3 python3.6m
PYTHON_INCLUDE := /usr/include/python3.6m \
/usr/lib/python3/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
# 根据你自己的目录确定相应文件的位置（绝对地址）
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 ?= @（4）命令行输入make all -j8进行编译，一开始肯定会报一些错误，大部分错误都跟依赖包以及上述配置文件有关，耐心点，一步一步在网上是能找到解决方法的。
（5）编译成功后输入make pycaffe继续编译python接口。
（6）编译完成后，在python中import caffe，如果不报错，则编译成功，如下图：
恭喜你，到这里已经大功告成，因为本文使用的是py-fast-rcnn中提供的caffe，所以相对于直接在caffe目录下编译会省去一部分工作。