一、事先声明:
1、Ubuntu版本:Ubuntu使用的是16.04。而不是16.04.1或16.04.2,这三个是有区别的。笔者曾有过这样的经历,Git上一个SLAM地图构建程序在Ubuntu14.04.3上可以正常make与工作,而14.04.4却一塌Error。。。
可自己在Google搜索关键字“Ubuntu16.04.1”做“引子”找到历史版本,第一个就是。在此我们放出网址:
http://old-releases.ubuntu.com/releases/16.04.1/
问:有两个“64-bit PC (AMD64) desktop image”怎么办?那个才是16.04?
答:点进去看看ISO前缀名称!
2、为什么免安装OpenCV?很简单,因为Ubuntu自带!但不是OpenCV3,而是OpenCV 2.x.x.x,具体是多少我忘了,你可用命令
- pkg-config --modversion opencv
查询一下,faster-rcnn用Ubuntu自带的OpenCV2的即可正常运行。如果您非要使用CV3,这里也给出参考教程。要相信OpenCV3不是三四行shell就能装好的,不信你去搜搜专门安装CV3的博客。OpenCV3.2.0安装链接如下
https://www.linuxhint.com/how-to-install-opencv-on-ubuntu/
二、软件版本:
Ubuntu16.04
Cuda8.0
1080ti驱动
OpenCV3.2.0
cuDNN5.1 (5.1.10)
glog-0.3.3.tar.gz
注:以上这些都可以事先下载好。
注意:安装完Ubuntu后记得关闭“自动更新”。笔者装机完,正常使用两天后,出现了ubuntu反复登录桌面的问题。我是重装了Unity,也装了GDM。但愿你不会出现这类问题,如果出现了,下面给出解决链接。
http://www.linuxidc.com/Linux/2011-07/39491.htm
http://forum.ubuntu.org.cn/viewtopic.PHP?t=460910
三、安装Caffe参考:
1、主要参考:
迷途de小狼:
http://blog.csdn.NET/u010733679/article/details/52249503
2、辅助参考:
http://www.jianshu.com/p/69a10d0a24b9
http://blog.csdn.Net/yan_song_/article/details/53154611
http://www.linuxidc.com/Linux/2016-09/135016.htm
http://blog.csdn.net/zhangzhenyuancs/article/details/52261004
3、声明与建议:
在此感谢几位网友的博客,正是参考了几位大神的博客才有我的成功安装。如有版权问题,请联系我,谢谢。
另外就是给要安装的朋友们给两个建议,自己搜索、或从上面的参考中至少选择三四篇文章通篇阅读一下,形成适合自己的安装思路。别人(包括本Blog)的环境都可能和屏幕前的你有所出入,所以要有所取舍。
四、安装Caffe步骤:
1、安装Ubuntu16.04
120G SSD:
/boot 400MB
/ SSD剩余所有
swap 内存两倍空间(心疼SSD的可放在2T机械)
2T
/home
2、安装1080ti驱动
- Ctrl+alt+F1//进入字符界面
- sudo service lightdm stop //关闭lightdm登录管理器
- sudo chmod 755 NVIDIA-Linux-x86_64-378.13.run //获取权限
- sudo ./NVIDIA-Linux-x86_64-378.13.run //安装驱动
Accept
Continue installation
安装完成之后
sudo service lightdm start
3、安装Cuda8.0
- sudo sh ./cuda_8.0.61_375.26_linux-run
进入安装命令行
---Do you accept the previously read EULA?
accept/decline/quit: accept
---Install NVIDIA Accelerated Graphics Driver for Linux-x86_64361.62?
(y)es/(n)o/(q)uit: n
---Install the CUDA 8.0 Toolkit?
(y)es/(n)o/(q)uit: y
---Enter Toolkit Location
[ default is /usr/local/cuda-8.0 ]:回车
---Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y
---Install the CUDA 8.0 Samples?
(y)es/(n)o/(q)uit: y
---Enter CUDA Samples Location
[ default is /home/duan ]:回车
4、安装OpenCV(可跳过,Ubuntu默认安装cv2):
见第一章网址,不用修改什么源文件。
注:如果你是4/8线程可用下代码加速编译:
- make -j4 #四核运算
5、安装依赖项:
1)Google Logging Library(glog),下载地址:https://code.google.com/p/google-glog/,然后解压安装:
$ tar zxvf glog-0.3.3.tar.gz
$ cd glog-0.3.3
$./configure
$ make
$ sudo make install
如果没有权限就chmod a+x glog-0.3.3 -R , 或者索性 chmod 777 glog-0.3.3 -R 。
2)其他依赖项,确保都成功
$ sudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler protobuf-c-compiler protobuf-compiler
6、安装Caffe,并用MNIST数据集测试:
1)安装pycaffe必须的一些依赖项:
sudo apt-get install -y Python-numpy python-scipy python-matplotlib python-sklearn python-skimage python-h5py python-protobuf python-leveldb python-networkx python-nose python-pandas python-gflags cython ipython
2)安装配置nVidia cuDNN 加速Caffe模型运算
- 1. $ sudo cp include/cudnn.h /usr/local/include
- 2. $ sudo cp lib64/libcudnn.* /usr/local/lib
- 3. $ sudo ln -sf /usr/local/lib/libcudnn.so.5.1.10 /usr/local/lib/libcudnn.so.5
- 4. $ sudo ln -sf /usr/local/lib/libcudnn.so.5 /usr/local/lib/libcudnn.so
- 5. $ sudo ldconfig -v
注:大家根据自己的cuDNN版本修改;如果懒于敲目录,可在Unity文件管理器下将文件夹拖入终端;终端下的“粘贴”快捷键:Ctrl+Shift+v。
3)切换到Caffe-master的文件夹,生成Makefile.config配置文件,执行:
- $ cp Makefile.config.example Makefile.config
4)配置Makefile.config文件
a.去掉“USE_CUDNN := 1”前面的#
b.根据前面自己的需求,选择性去掉“OPENCV_VERSION :=3”前面的#
c.配置一些引用文件(增加部分主要是解决新版本下,HDF5的路径问题)
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/lib/x86_64-linux-gnu/hdf5/serial/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial
下面贴出我的Makefile.config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
- # 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
- # 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 := 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
- # 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/lib/x86_64-linux-gnu/hdf5/serial/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.)
- # 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 ?= @
5)编译caffe-master!!!"-j4"是使用CPU的多核进行编译,可以极大地加速编译的速度,建议使用。
注:下面的make命令适当的时候加上sudo,建议都已super do来运行。
- $ make all -j4
- $ make test -j4
- $ make runtest -j4
期间会遇到下面的问题:
问题:mkl_alternate.hpp:14:19 fatal error: cblas.h: 没有那个文件或目录
解决:https://github.com/BVLC/caffe/issues/3599
- sudo apt-get install libopenblas-dev
问题:找不到 -lablas、-latlas
解决:
- sudo apt-get install libatlas-base-dev
可能问题:.build_release/lib/libcaffe.so.1.0.0 Error 1 /usr/bin/ld: 找不到 -lopencv_imgcodecs
解决:你没有安装好CV3,可选择加上b的井号,或参考
https://my.oschina.net/peterlie/blog/661994
http://blog.csdn.net/foolsnowman/article/details/50532226
问题:caffe libcudart.so.8.0 cannot open shared object file no such file or directory
解决:https://github.com/NVIDIA/DIGITS/issues/8
- sudo ldconfig /usr/local/cuda/lib64
问题:nvcc warring: the 'compute_20', 'sm_20', and 'sm_21'
解决:我是不理会,不影响使用,想看一眼的可参考以下网址:
http://www.itwendao.com/article/detail/214390.html
6)编译Python和Matlab用到的caffe文件
- $ make pycaffe -j4
7)测试
打开example -> MNIST -> Readme.md
按照最前面,cd到Caffe根目录,运行第一页的两个命令,进行下载和格式转换(不超过30MB的下载量)
再运行最下面的命令,Training,看命令行盖楼。。。
五、安装faster-r-cnn参考
bit_hammer大神:
http://blog.csdn.net/u011635764/article/details/52831167
cuDNN v5、v5.1的问题
http://blog.csdn.net/u010733679/article/details/52221404
大家按照bit_hammer大神的步骤安装就好,关于cuDNN v5.1的处理,两篇文章都一样的。
注:faster-r-cnn一定要从Git Clone,Download ZIP不全。
六、测试faster-r-cnn
从百度云盘下载训练好的模型:
VGG16_faster_rcnn_final.caffemodel (548.3MB)
2F_faster_rcnn_final.caffemodel (237.2MB)
将上述两个文件存放于:
py-faster-rcnn/data/faster_rcnn_models/
下,即可运行步骤五中的Demo。
转自http://blog.csdn.net/dylll321/article/details/72784159
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