py-faster-rcnn编译过程中的问题记录

博主系统环境如下：

编译py-faster-rcnn

参考如下：

• caffe官网
• py-faster-rnn源码github
• 博客：搭建faster-rcnn进行目标检测的环境
• 论坛：faster RCNN python 安装

编译步骤：

• 从github上下载源码

#cd 进入自己放置faster rcnn位置
git clone --recursive https://github.com/rbgirshick/py-faster-rcnn.git
 
 
   
   
   
   

    
    
    
    
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建议加上–recursive，这样可以一并下载caffe-faster-rcnn包

• 生成Cython模块 
  修改py-faster-rcnn/lib/setup.py文件第135行: 
  ‘nvcc’: [‘-arch=sm_35’,

根据自己的显卡计算能力进行修改,查询网址为: CUDA-Enabled GeForce Products 

博主机子是GeForce GTX 980,所以这里我改成了52 
 
然后：

$ py-faster-rcnn/lib
make   #make -j8
 
 
   
   
   
   

    
    
    
    
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• 生成Caffe和pycaffe

$ py-faster-rcnn/caffe-fast-rnn
cp Makefile.config.example Makefile.config
 
 
   
   
   
   

    
    
    
    
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Makefile.config文件博主的如下：

## 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 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 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 := /usr/local/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
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib

# 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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保存退出。 
终端输入：

$ py-faster-rcnn/caffe-fast-rnn
mkdir build
cd build
cmake ..
make -j8  
make test
make runtest 
make pycaffe
 
 
   
   
   
   

    
    
    
    
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问题

但博主在make -j8的时候，报以下错误：

全都是和cudnn有关，错误个数非常多。因为faster-rcnn的caffe所用的cudnn为旧版本的，与我的cudnn版本(v5.1)不兼容，导致出现如上错误。所以这里我们需要修改cudnn加速文件

1. py-faster-rcnn/caffe-fast-rcnn/include/caffe/util/cudnn.hpp
2. py-faster-rcnn/caffe-fast-rcnn/src/caffe/util/cudnn.cpp
3. py-faster-rcnn/caffe-fast-rcnn/include/caffe/layers/目录下8个cudnn_开头的文件
4. py-faster-rcnn/caffe-fast-rcnn/src/caffe/layers/ 目录下16个cudnn_开头的文件

将以上文件用选择caffe-master版的相应文件进行替换,这样编译就不会出错啦。（亲测有效）

还遇到这个问题： 

执行第一次时，发现后面还会再出现这类问题，所以我又执行了一次，下一次就可以正常了。 
参考方法： 
caffe配置问题与解决方法集锦 
使用caffe时编译出错

后面执行demo.py的时候会遇到： 

解决办法： 

发现找不到pip和easy_install,所以需要安装如下：

sudo apt-get install python-setuptools
 
 
   
   
   
   

    
    
    
    
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再执行：

sudo easy_install pip
 
 
   
   
   
   

    
    
    
    
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最后执行：

sudo pip install easydict
 
 
   
   
   
   

    
    
    
    
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终结

• 运行demo.py

$ py-faster-rcnn/tools
./demo.py #或者 python demo.py
 
 
   
   
   
   

    
    
    
    
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py-faster rcnn训练自己的数据

参考： 
将数据集做成VOC2007格式用于Faster-RCNN训练 
Faster-RCNN+ZF用自己的数据集训练模型(Python版本)