Windows7平台下基于Caffe框架，Python2.7配置 Faster-RCNN:
Caffe编译采用Vsiual Studio 2013
Step 1:安装cuda7.5(cuda 的支持依赖对显卡没有版本限制，凡是出现在列表中的显卡均是支持cuda各版本的，请放心使用)
https://developer.nvidia.com/cuda-75-downloads-archive
Step 2:下载cuDNN 5.0 (请根据cuda的版本选择相应的支持包)
https://developer.nvidia.com/cudnn
Step 3:下载Windows版Caffe
https://github.com/Microsoft/caffe
Step 4:编译caffe的libcaffe需要mcrosoft.net 3.5的支持(编译中出现无法读取libcaffe.lib的错误表示没有安装mcrosofe .net 3.5)
https://www.microsoft.com/en-us/download/details.aspx?id=21
Step 5:下载python的包含库Miniconda2 (其中已经包含了一个版本的Python因此不需要单独下载安装python)
https://conda.io/miniconda.html
Step 5.1: 安装numpy的库
Pip install numpy
Step 5.2: 在opencv下的Python文件夹下找到cv2.pyd将其考到Miniconda2的目录下
Step 5.3: 安装PIL-1.1.7.win32-py2.7的时候，不能再注册表中识别出来python2.7
新建一个register.py 文件，把一下代码贴进去，保存
#
#script to register Python 2.0 or later for use with win32all
#and other extensions that require Python registry settings
#
#written by Joakim Loew for Secret Labs AB / PythonWare
#
#source:
#http://www.pythonware.com/products/works/articles/regpy20.htm
#
#modified by Valentine Gogichashvili as described in http://www.mail-archive.com/[email protected]/msg10512.html
import sys
from _winreg import *
#tweak as necessary
version= sys.version[:3]
installpath= sys.prefix
regpath= "SOFTWARE\\Python\\Pythoncore\\%s\\" % (version)
installkey= "InstallPath"
pythonkey= "PythonPath"
pythonpath= "%s;%s\\Lib\\;%s\\DLLs\\" % (
installpath, installpath, installpath
)
def RegisterPy():
try:
reg = OpenKey(HKEY_CURRENT_USER,regpath)
except EnvironmentError as e:
try:
reg = CreateKey(HKEY_CURRENT_USER,regpath)
SetValue(reg, installkey, REG_SZ,installpath)
SetValue(reg, pythonkey, REG_SZ,pythonpath)
CloseKey(reg)
except:
print"*** Unable to register!"
return
print"--- Python", version, "is now registered!"
return
if (QueryValue(reg, installkey) == installpath and
QueryValue(reg, pythonkey) ==pythonpath):
CloseKey(reg)
print"=== Python", version, "is already registered!"
return
CloseKey(reg)
print"*** Unable to register!"
print"*** You probably have another Python installation!"
if__name__ == "__main__":
RegisterPy()
运行代码，显示如下：
Step 5.4:安装python-yaml库
http://pyyaml.org/download/pyyaml/PyYAML-3.10.win32-py2.7.exe
运行并安装
Step 6:复制Windows下CommonSettings.props.example，后缀改为CommonSettings.props
Step 6:修改 caffe-master/windows下的CommonSettings.props 文件修改内容如下
(此处用Python的外部接口做试验因此Modify 1 中MATLAB依赖设置成false，Modify 3可以不用修改)
Modify 1:
<PropertyGroup Label="UserMacros">
<BuildDir>$(SolutionDir)..\Build</BuildDir>
<!--NOTE: CpuOnlyBuild and UseCuDNNflags can't be set at the same time.-->
<CpuOnlyBuild>false</CpuOnlyBuild>
<UseCuDNN>true</UseCuDNN>
<CudaVersion>7.5</CudaVersion>
<!-- NOTE: If Python support isenabled, PythonDir (below) needs to be
set tothe root of your Python installation. If your Python installation
does not contain debug libraries, debug build will not work.-->
<PythonSupport>true</PythonSupport>
<!-- NOTE: If Matlab support isenabled, MatlabDir (below) needs to be
set to the root of your Matlab installation. -->
<MatlabSupport>false</MatlabSupport>
<CudaDependencies></CudaDependencies>
<!-- Set CUDAarchitecture suitable for your GPU.
Setting proper architecture is important to mimize your run and compile time.-->
<CudaArchitecture>compute_35,sm_35;compute_52,sm_52</CudaArchitecture>
<!--CuDNN 3 and 4 are supported -->
<CuDnnPath>D:\cudnn-7.0-win-x64-v3.0-prod\</CuDnnPath>
<ScriptsDir>$(SolutionDir)\scripts</ScriptsDir>
</PropertyGroup>
Modify 2:
<PropertyGroupCondition="'$(PythonSupport)'=='true'">
<PythonDir>D:\Miniconda2\</PythonDir>
<LibraryPath>$(PythonDir)\libs;$(LibraryPath)</LibraryPath>
<IncludePath>$(PythonDir)\include;$(IncludePath)</IncludePath>
</PropertyGroup>
Modify 3:
<PropertyGroupCondition="'$(MatlabSupport)'=='true'">
<MatlabDir>M:\ProgramFiles\MATLAB\R2015b</MatlabDir>
<LibraryPath>$(MatlabDir)\extern\lib\win64\microsoft;$(LibraryPath)</LibraryPath>
<IncludePath>$(MatlabDir)\extern\include;$(IncludePath)</IncludePath>
</PropertyGroup>
(如果编译matlab的外部接口，此处还需要修改include目录路径，因为不同版本中matlab的include路径不一样)
Step 7：运行caffe，windows文件夹下的Caffe.sln, 生成libcaffe工程
(然后会弹出一个窗口，Nuget所需要的第三方库，可能会未响应，慢慢等吧)
Step 8: 修改caffe工程文件libcaffe.vcxproj和libcaffe.vcxproj.filters
修改libcaffe.vcxproj文件
在157行加入：
<ClCompile Include="..\..\src\caffe\layers\roi_pooling_layer.cpp"/>
在265行加入：
<ClInclude Include="..\..\include\caffe\layers\roi_pooling_layer.hpp"/>
在344行加入：
<CudaCompile Include="..\..\src\caffe\layers\roi_pooling_layer.cu" />
修改libcaffe.vcxproj.filters文件
在291行加入：
<ClCompileInclude="..\..\src\caffe\layers\roi_pooling_layer.cpp"> <Filter>src\layers</Filter> </ClCompile>
在569行加入：
<ClIncludeInclude="..\..\include\caffe\layers\roi_pooling_layer.hpp"> <Filter>include\layers</Filter> </ClInclude>
在781行加入：
<CudaCompileInclude="..\..\src\caffe\layers\roi_pooling_layer.cu"> <Filter>cu\layers</Filter> </CudaCompile>
(这一步是为了针对Checkfailed: registry.count(type) == 1 (0 vs. 1) Unknown layer type: ROIPooling的错误)
Step 9：编译生成caffe的python外部依赖接口(在解决方案上点击右键，选择但启动项目，选择pycaffe进行编译)
Step 10: 将生成的python接口下的caffe(此接口在build文件夹下)拷贝到Miniconda2的目录下和frcnn的caffe-master目录下
Step 11: 更改frcnn下的lib，从网上下载windows版的lib
https://codeload.github.com/MrGF/py-faster-rcnn-windows/zip/master
Step 12: 下载faster-rcnn-models
http://www.cs.berkeley.edu/~rbg/faster-rcnn-data/faster_rcnn_models.tgz
http://www.cs.berkeley.edu/~rbg/faster-rcnn-data/imagenet_models.tgz
http://www.cs.berkeley.edu/~rbg/fast-rcnn-data/selective_search_data.tgz
Step 13：运行faster-rcnn demo.py程序
报错： AttributeError:‘ProposalLayer’ object has no attribute ‘param_str_’
修改param_str_为param_str，完成编译
报错： keyerror:’1’
将第64行改为cfg_key= ‘TEST’#str(self.phase), demo可以正常运行