Caffe 学习系列（五）：Faster RCNN安装与测试

Caffe 学习系列（五）：Faster RCNN安装与测试

版本：CPU
前提：caffe/pycafffe/opencv已安装完成
说明：本博客opencv版本为3.3

1、源码下载

git clone --recursive https://github.com/rbgirshick/py-faster-rcnn.git

sudo pip install cython 
sudo pip install easydict   # 不然后续会报错缺少该软件

2、编译cython模块

切换至 lib 目录下，对 set.py 文件进行修改，注释掉 GPU 相关代码

cd py-faster-rcnn/lib

注释内容参考如下：（…表示中间又不用注释跳过去的内容）

… 
#CUDA = locate_cuda() 
… 
#            self.set_executable(‘compiler_so’, CUDA[‘nvcc’]) 
… 
#    Extension(‘nms.gpu_nms’, 
#        [‘nms/nms_kernel.cu’, ‘nms/gpu_nms.pyx’], 
#        library_dirs=[CUDA[‘lib64’]], 
#        libraries=[‘cudart’], 
#        language=’c++’, 
#        runtime_library_dirs=[CUDA[‘lib64’]], 
#        # this syntax is specific to this build system 
#        # we’re only going to use certain compiler args with nvcc and not with 
#        # gcc the implementation of this trick is in customize_compiler() below 
#        extra_compile_args={‘gcc’: [“-Wno-unused-function”], 
#                            ‘nvcc’: [‘-arch=sm_35’, 
#                                     ‘–ptxas-options=-v’,** 
#                                     ‘-c’, 
#                                     ‘–compiler-options’, 
#                                     “’-fPIC’”]}, 
#        include_dirs = [numpy_include, CUDA[‘include’]] 
#    ), 
… 

修改后 set.py 内容如下：

# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------

import os
from os.path import join as pjoin
from setuptools import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
import subprocess
import numpy as np

def find_in_path(name, path):
    "Find a file in a search path"
    # Adapted fom
    # http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/
    for dir in path.split(os.pathsep):
        binpath = pjoin(dir, name)
        if os.path.exists(binpath):
            return os.path.abspath(binpath)
    return None


def locate_cuda():
    """Locate the CUDA environment on the system

    Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64'
    and values giving the absolute path to each directory.

    Starts by looking for the CUDAHOME env variable. If not found, everything
    is based on finding 'nvcc' in the PATH.
    """

    # first check if the CUDAHOME env variable is in use
    if 'CUDAHOME' in os.environ:
        home = os.environ['CUDAHOME']
        nvcc = pjoin(home, 'bin', 'nvcc')
    else:
        # otherwise, search the PATH for NVCC
        default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin')
        nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path)
        if nvcc is None:
            raise EnvironmentError('The nvcc binary could not be '
                'located in your $PATH. Either add it to your path, or set $CUDAHOME')
        home = os.path.dirname(os.path.dirname(nvcc))

    cudaconfig = {'home':home, 'nvcc':nvcc,
                  'include': pjoin(home, 'include'),
                  'lib64': pjoin(home, 'lib64')}
    for k, v in cudaconfig.iteritems():
        if not os.path.exists(v):
            raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v))

    return cudaconfig
# CUDA = locate_cuda()


# Obtain the numpy include directory.  This logic works across numpy versions.
try:
    numpy_include = np.get_include()
except AttributeError:
    numpy_include = np.get_numpy_include()

def customize_compiler_for_nvcc(self):
    """inject deep into distutils to customize how the dispatch
    to gcc/nvcc works.

    If you subclass UnixCCompiler, it's not trivial to get your subclass
    injected in, and still have the right customizations (i.e.
    distutils.sysconfig.customize_compiler) run on it. So instead of going
    the OO route, I have this. Note, it's kindof like a wierd functional
    subclassing going on."""

    # tell the compiler it can processes .cu
    self.src_extensions.append('.cu')

    # save references to the default compiler_so and _comple methods
    default_compiler_so = self.compiler_so
    super = self._compile

    # now redefine the _compile method. This gets executed for each
    # object but distutils doesn't have the ability to change compilers
    # based on source extension: we add it.
    def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts):
        if os.path.splitext(src)[1] == '.cu':
            # use the cuda for .cu files
            # self.set_executable('compiler_so', CUDA['nvcc'])
            # use only a subset of the extra_postargs, which are 1-1 translated
            # from the extra_compile_args in the Extension class
            postargs = extra_postargs['nvcc']
        else:
            postargs = extra_postargs['gcc']

        super(obj, src, ext, cc_args, postargs, pp_opts)
        # reset the default compiler_so, which we might have changed for cuda
        self.compiler_so = default_compiler_so

    # inject our redefined _compile method into the class
    self._compile = _compile


# run the customize_compiler
class custom_build_ext(build_ext):
    def build_extensions(self):
        customize_compiler_for_nvcc(self.compiler)
        build_ext.build_extensions(self)


ext_modules = [
    Extension(
        "utils.cython_bbox",
        ["utils/bbox.pyx"],
        extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]},
        include_dirs = [numpy_include]
    ),
    Extension(
        "nms.cpu_nms",
        ["nms/cpu_nms.pyx"],
        extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]},
        include_dirs = [numpy_include]
    ),
#    Extension('nms.gpu_nms',
#        ['nms/nms_kernel.cu', 'nms/gpu_nms.pyx'],
#        library_dirs=[CUDA['lib64']],
#        libraries=['cudart'],
#        language='c++',
#        runtime_library_dirs=[CUDA['lib64']],
#        # this syntax is specific to this build system
#        # we're only going to use certain compiler args with nvcc and not with
#        # gcc the implementation of this trick is in customize_compiler() below
#        extra_compile_args={'gcc': ["-Wno-unused-function"],
#                            'nvcc': ['-arch=sm_35',
#                                     '--ptxas-options=-v',
#                                     '-c',
#                                     '--compiler-options',
#                                     "'-fPIC'"]},
#        include_dirs = [numpy_include, CUDA['include']]
#    ),
    Extension(
        'pycocotools._mask',
        sources=['pycocotools/maskApi.c', 'pycocotools/_mask.pyx'],
        include_dirs = [numpy_include, 'pycocotools'],
        extra_compile_args={
            'gcc': ['-Wno-cpp', '-Wno-unused-function', '-std=c99']},
    ),
]

setup(
    name='fast_rcnn',
    ext_modules=ext_modules,
    # inject our custom trigger
    cmdclass={'build_ext': custom_build_ext},
)

修改后保存退出，在 lib 目录下执行以下指令进行编译

make

3、修改配置文件并编译

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

打开Makefile.config，修改方式和caffe中的修改方式一样，具体参考 caffe 安装教程的修改内容。

编译

make -j8 && make pycaffe

4、运行demo测试

首先下载训练好的数据集

cd ~/py-faster-rcnn
./data/scripts/fetch_faster_rcnn_models.sh

下载后

cd py-faster-rcnn/data

把文件放在这里，并解压

tar zxvf faster_rcnn_models.tgz -C ./

CPU版本需要做以下修改

A：修改/py-faster-rcnn/lib/fast_rcnn/config.py文件（True改成False）
# Use GPU implementation of non-maximum suppression
__C.USE_GPU_NMS = False

B：将/py-faster-rcnn/tools/test_net.py和 /py-faster-rcnn/tools/train_net.py的caffe.set_mode_gpu()修改为
caffe.set_mode_cpu().

C：修改/py-faster-rcnn/lib/fast_rcnn/nms_wrapper.py文件（注释该引用，并将False改成True）
#from nms.gpu_nms import gpu_nms
def nms(dets, thresh, force_cpu=True):

最后，运行demo：

cd ~/py-faster-rcnn
./tools/demo.py --cpu

参考来源
https://blog.youkuaiyun.com/zyb19931130/article/details/53842791
https://blog.youkuaiyun.com/sinat_17196995/article/details/53410292
https://blog.youkuaiyun.com/zoro_lov3/article/details/60581174
https://blog.youkuaiyun.com/qq_27664915/article/details/56673764