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caffe-ssd

crazybird123 2018-06-01 18:52 原文

1.安装依赖

1 sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
2 sudo apt-get install --no-install-recommends libboost-all-dev
3 sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
4 sudo apt-get install libatlas-base-dev
5 sudo apt-get install python-dev

sudo apt-get install libopenblas-dev
    1.   1)sudo apt-get install python-numpy python-scipy python-matplotlib python-sklearn python-skimage  
    2.     python-h5py python-protobuf python-leveldb python-networkx python-nose python-pandas python-gflags  
    3.     Cython ipython 
    4.         2)sudo apt-get update 
    5.         3)切换到caffe目录下，执行: 
    6.              python 
    7. 　　　　　　　　　　　　　import caffe 
 sudo pip install protobuf
 sudo apt-get install liblapack3

2、下载caffe 

https://github.com/BVLC/caffe

 

3.编译Caffe 
（1）切换到Caffe所在目录

cp Makefile.config.example Makefile.config

（2）修改配置文件Makefile.config

  • CPU_ONLY := 1
  • 配置引用文件（解决新版本下，HDF5的路径问题）
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/hdf5/serial
BLAS := atlas

opencv问题：

1.$ pkg-config --modversion opencv查看是否安装

2. Makefile文件中，找到LIBRARIES（在PYTHON_LIBRARIES := boost_python python2.7 前一行）

LIBRARIES += glog gflags protobuf leveldb snappy lmdb boost_system hdf5_hl hdf5 m opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs opencv_videoio

3.将Makefile.config中OPENCV_VERSION := 3取消注释;

 

cuda环境变量

sudo vim ~/.bashrc
export PATH=/usr/local/cuda-9.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64$LD_LIBRARY_PATH
source ~/.bashrc

 

（3）编译 Caffe

make all -j8
make test -j8
make runtest -j8

 

4.编译Python接口 
Caffe拥有python\C++\shell接口，在Caffe使用python特别方便，在实例中都有接口的说明。

sudo apt-get install python-pip
  • 执行安装依赖
cd ~/caffe
sudo apt-get install gfortran
cd ./python
for req in $(cat requirements.txt); do pip install $req; done

安装完成以后，再次回到caffe根目录我们可以执行：

sudo pip install -r python/requirements.txt

就会看到，安装成功的，都会显示Requirement already satisfied, 没有安装成功的，会继续安装。

  • 编译python接口
make pycaffe -j8

--结果显示ALL TESTS PASSED就安装好了！

  • 运行python结构
复制代码
$ python2.7
Python 2.7.12 (default, Jul  1 2016, 15:12:24) 
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import caffe
>>>
复制代码

如果没有报错，说明caffe安装全部完成(注意：要进入caffe/python再执行python命令，否则import caffe会提示找不到caffe)！

5.在Mnist运行Lenet

  • 获取数据源
./data/mnist/get_mnist.sh
./examples/mnist/create_mnist.sh
  • 因为是CPU运行，所以修改在examples文件下的Mnist下的lenet_solver.prototxt中的solver_mode:CPU
solver_mode: CPU
  • 训练模型
./examples/mnist/train_lenet.sh

 

 

添加python目录

vi ~/.bashrc
export PYTHONPATH=/home/username/caffe/python：$PYTHONPATH
source ~/.bashrc

 

 

 

 

caffe  cpu  python3

# OpenCV
$ sudo apt install python3-opencv
# Numpy
$ sudo apt-get install python3-numpy
# BLAS
$ sudo apt-get install libatlas-base-dev     # Atlas
or
$ sudo apt-get install libopenblas-dev       # OpenBLAS
# Other dependencies
$ sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
$ sudo apt-get install --no-install-recommends libboost-all-dev
$ sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
$ sudo pip3 install protobuf
$ sudo apt-get install the python3-dev
$ cp Makefile.config.example Makefile.config

OPENCV_VERSION := 3 # if you are using OpenCV 3 or above
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 := atlas # if you’ve installed Atlas (default)
or
BLAS := open # if you’ve installed OpenBLAS
#PYTHON_INCLUDE := /usr/include/python2.7 \
# /usr/lib/python2.7/dist-packages/numpy/core/include
PYTHON_LIBRARIES := boost_python3 python3.5m
PYTHON_INCLUDE := /usr/include/python3.5m \
/usr/lib/python3.5/dist-packages/numpy/core/include
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib/x86_64-linux-gnu/hdf5/serial

python3.5

cd /usr/lib/x86_64-linux-gnu
sudo ln -s libboost_python-py35.so libboost_python3.so
make all -j4
make pycaffe

再配置python路径即可

 

## 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 lines after *_35 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_61,code=sm_61

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
# BLAS := atlas
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 := /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)/anaconda2
# 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/include/hdf5/serial
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

# 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 ?= @

 

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