cudnn 5.1版本下跑通 py-faster-rcnn的demo

软件环境：

Ubuntu 16.04 + CUDA8.0 + cuDnn5.1 + python 2.7 + OpenCv 3.1

本文的主要目的，是解决在编译py-faster-rcnn的过程中,与cuDnn的v5版本的冲突问题。编译报错是函数错用。最初是把cuDnn换成了v4。后期在跑demo.py时，没能正确检测出物体，也就是没有出带框的图像。一开始以为是plt的问题，后来发现不是，此demo.py在cpu下运行正常，加上gpu选项就不能正常检测物体。

第一阶段，是按照py-faster-rcnn作者的前半部分步骤来进行。相关链接

1.作者提到，在编译Caffe时，至少需要在Makefile.config设置的两点，这里和Caffe的安装相关了，有很多这样的教程。

# In your Makefile.config, make sure to have this line uncommentedWITH_PYTHON_LAYER := 1# Unrelatedly, it's also recommended that you use CUDNNUSE_CUDNN := 1
2.你需要安装的软件（用apt-get即可）：
cython, python-opencv, easydict
3.命令行下
# Make sure to clone with --recursivegit clone --recursive https://github.com/rbgirshick/py-faster-rcnn.git4. $FRCN_ROOT指克隆过来的根目录
cd $FRCN_ROOT/libmake
==========================================================================分界线==========================================
5.作者用的caffe的版本较旧，为了能和cuDnn v5兼容，需要参考：这里
cd caffe-fast-rcnn  git remote add caffe https://github.com/BVLC/caffe.git  git fetch caffe  git merge -X theirs caffe/master  整合以后，需要修改:
Remove self_.attr("phase") = static_cast<int>(this->phase_); from include/caffe/layers/python_layer.hpp after merging.========================================分界线============================================================================
6.回到原作者的教程。为编译做准备。原作者的Makefile.config文件我不建议用，他里面有一些老旧的设定，比如gcc版本，Matlab的一些设定。我贴上我的来，并把一些坑具体的说说，其实这个配置文件每一项猜起来比较容易。
cd $FRCN_ROOT/caffe-fast-rcnn在这个目录下我们需要准备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).使用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# 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#我的opencv版本是3.1 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 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_50,code=compute_50# BLAS choice:# atlas for ATLAS (default)# mkl for MKL# open for OpenBlasBLAS := 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.#下面这个目录和左边的这个头文件关联PYTHON_INCLUDE := /usr/include/python2.7 /		/usr/local/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)#这里需要设为1 WITH_PYTHON_LAYER := 1 # Whatever else you find you need goes here.#这里是为了能找到hdf5.h的文件，具体位置根据个人情况修改INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serialLIBRARY_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.)#这里根据一些博文上说应该设为1 USE_PKG_CONFIG := 1BUILD_DIR := buildDISTRIBUTE_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 ?= @编辑好Makefile.config文件以后可以执行：
make -j8 && make pycaffe在执行过程中，难免会有错误，比如少xx.h，有可能你真的没有装相关的软件，有可能你有但没被发现。可通过find命令进行寻找。
find /usr -name xxx.h这是在/usr里进行寻找。这类错误比较容易解决。
7. 下面就是下载模型(脚本需要运行两次）和跑demo.py了
cd $FRCN_ROOT./data/scripts/fetch_faster_rcnn_models.sh./data/scripts/fetch_faster_rcnn_models.sh./tools/demo.py