RCNN(五):Ubuntu 15.04配置Faster RCNN

gitアドレス:https://github.com/rbgirshick/py-faster-rcnn本文はgit上の要求に対して翻訳をして、いくつか出会う可能性のある穴に対して修正をしました.CUDA、CUDNNなどのソフトウェアのインストールについては、以下を参照してください.http://blog.csdn.net/u011587569/article/details/52054168
Requirements: software

sudo apt-get install git cython python-opencv
sudo pip install cython easydict

Requirements: hardware

For training smaller networks (ZF, VGG_CNN_M_1024) a good GPU (e.g.Titan, K20, K40, …) with at least 3G of memory suffices

For training Fast R-CNN with VGG16, you’ll need a K40 (~11G of memory)

For training the end-to-end version of Faster R-CNN with VGG16, 3G of GPU memory is sufficient (using CUDNN)

Installation
1.Clone the Faster R-CNN repository

#Make sure to clone with --recursive
git clone --recursive https://github.com/rbgirshick/py-faster-rcnn.git

2.Build the Cython modules

cd $FRCN_ROOT/lib
make

note:CUDAが見つからないことをずっと提示している場合はsetup.pyのすべてのCUDA[lib 64]をCUDA[lib]に変更し、以下のようにします.

# --------------------------------------------------------
# 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'),
                  'lib': pjoin(home, 'lib')}
    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['lib']],
        libraries=['cudart'],
        language='c++',
        runtime_library_dirs=[CUDA['lib']],
        # 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},
)

note:G++またはC++に関数が欠けているとプロンプトされた場合は、gccを4.9.2バージョンにアップグレードします.次の手順に従います.

$ cd /usr/bin
$ sudo rm gcc
$ sudo ln -s gcc-4.9 gcc
$ sudo rm g++
$ sudo ln -s g++-4.9 g++

3.Build Caffe and pycaffe前にCaffeのMakefileを構成します.configはcaffe-fast-rcnnフォルダにコピーします.(http://blog.csdn.net/u011587569/article/details/52054168)は次のように変更されました:WITH_PYTHON_LAYER:=1でコンパイル

cd $FRCN_ROOT/caffe-fast-rcnn
sudo make -j8 
sudo make pycaffe

note:sudoを必ず追加しないと、権限が足りない可能性があります.
4.Download pre-computed Faster R-CNN detectors

cd $FRCN_ROOT
./data/scripts/fetch_faster_rcnn_models.sh

note:ダウンロードが遅いかもしれませんが、fetch_を開きます.faster_rcnn_models.sh,ダウンロードリンクを取得し,迅雷などのダウンロードツールを用いてダウンロードしdataの下に置き,解凍すればよい.
Demo

cd $FRCN_ROOT
./tools/demo.py

note:デフォルトのプレゼンテーションはシステムが持参した画像です.もちろん、自分の画像に変更することもできます.
1.自分の画像をdata/demoフォルダの下に置きます.toolsの下のdemo.py

im_names = ['000456.jpg', '000542.jpg', '001150.jpg','001763.jpg', '004545.jpg']

上に私たちの写真の名前を追加すればいいです.
テストセット検証
以下のブログを参照してください.http://blog.csdn.net/u011587569/article/details/52166775