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caffe-gpu源码编译

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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anacondapython虚拟环境,支持opencv,支持CUDAcuDNN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
pip install protobuf

opencv

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

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 := 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

caffe

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?

caffe

在不报错的terminal中,查看环境变量就会发现端倪

caffe

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

caffe

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:$PYTHONPATH

Q & A

Q1

编译过程碰到了tiff相关的错误

caffe

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aptconda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

caffe

libhdf5_hl.so.100的路径加入LD_LIBRARY_PATH

export LD_LIBRARY_PATH=/home/xugaoxiang/anaconda3/lib:$LD_LIBRARY_PATH

Q3

进入ipython中,import caffe报错

caffe

修改Makefile.config,修改PYTHON_LIBRARIES

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

caffe

这是由于在opencv4中,原来版本中的宏CV_LOAD_IMAGE_COLORCV_LOAD_IMAGE_GRAYSCALE已经改成了cv::IMREAD_COLORcv::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

caffe

参考资料

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