git clone --recursive



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

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

#change for windows, by MrX
nvcc_bin = 'nvcc.exe'
lib_dir = 'lib/x64'

def find_in_path(name, path):
    "Find a file in a search path"
    # Adapted fom
    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 'CUDA_PATH' in os.environ:
        home = os.environ['CUDA_PATH']
        print("home = %s\n" % home)
        nvcc = pjoin(home, 'bin', nvcc_bin)
        # otherwise, search the PATH for NVCC
        default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin')
        nvcc = find_in_path(nvcc_bin, 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 $CUDA_PATH')
        home = os.path.dirname(os.path.dirname(nvcc))
        print("home = %s, nvcc = %s\n" % (home, nvcc))

    cudaconfig = {'home':home, 'nvcc':nvcc,
                  'include': pjoin(home, 'include'),
                  'lib64': pjoin(home, lib_dir)}
    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.
    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 cl/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

    # save references to the default compiler_so and _comple methods
    #default_compiler_so = self.spawn 
    #default_compiler_so = self.rc
    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(sources, output_dir=None, macros=None, include_dirs=None, debug=0, extra_preargs=None, extra_postargs=None, depends=None):
        if postfix == '.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']
            postargs = extra_postargs['cl']

        return super(sources, output_dir, macros, include_dirs, debug, extra_preargs, postargs, depends)
        # reset the default compiler_so, which we might have changed for cuda
        #self.rc = 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):

ext_modules = [
    # unix _compile: obj, src, ext, cc_args, extra_postargs, pp_opts
        #extra_compile_args={'cl': ['/link', '/DLL', '/OUT:cython_bbox.dll']},
        #extra_compile_args={'cl': ['/LD']},
        extra_compile_args={'cl': []},
        include_dirs = [numpy_include]
        extra_compile_args={'cl': []},
        include_dirs = [numpy_include],
        sources=['pycocotools\\maskApi.c', 'pycocotools\\_mask.pyx'],
        include_dirs = [numpy_include, 'pycocotools'],
        extra_compile_args={'cl': []},
    #Extension(   # just used to get nms\gpu_nms.obj
    #    "nms.gpu_nms",
    #    sources=['nms\\gpu_nms.pyx'],
    #    language='c++',
    #    extra_compile_args={'cl': []},
    #    include_dirs = [numpy_include]

    # inject our custom trigger
    cmdclass={'build_ext': custom_build_ext},


#!/usr/bin/env python

import numpy as np
import os
# on Windows, we need the original PATH without Anaconda's compiler in it:
PATH = os.environ.get('PATH')
from distutils.spawn import spawn, find_executable
from setuptools import setup, find_packages, Extension
from setuptools.command.build_ext import build_ext
import sys

# CUDA specific config
# nvcc is assumed to be in user's PATH
nvcc_compile_args = ['-O', '--ptxas-options=-v', '-arch=sm_35', '-c', '--compiler-options=-fPIC']
nvcc_compile_args = os.environ.get('NVCCFLAGS', '').split() + nvcc_compile_args
cuda_libs = ['cublas']

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

cudamat_ext = Extension('nms.gpu_nms',
                        include_dirs = [numpy_include, 'C:\\Programming\\CUDA\\v7.5\\include'])

class CUDA_build_ext(build_ext):
    Custom build_ext command that compiles CUDA files.
    Note that all extension source files will be processed with this compiler.
    def build_extensions(self):
        self.compiler.set_executable('compiler_so', 'nvcc')
        self.compiler.set_executable('linker_so', 'nvcc --shared')
        if hasattr(self.compiler, '_c_extensions'):
            self.compiler._c_extensions.append('.cu')  # needed for Windows
        self.compiler.spawn = self.spawn

    def spawn(self, cmd, search_path=1, verbose=0, dry_run=0):
        Perform any CUDA specific customizations before actually launching
        compile/link etc. commands.
        if (sys.platform == 'darwin' and len(cmd) >= 2 and cmd[0] == 'nvcc' and
                cmd[1] == '--shared' and cmd.count('-arch') > 0):
            # Versions of distutils on OSX earlier than 2.7.9 inject
            # '-arch x86_64' which we need to strip while using nvcc for
            # linking
            while True:
                    index = cmd.index('-arch')
                    del cmd[index:index+2]
                except ValueError:
        elif self.compiler.compiler_type == 'msvc':
            # There are several things we need to do to change the commands
            # issued by MSVCCompiler into one that works with nvcc. In the end,
            # it might have been easier to write our own CCompiler class for
            # nvcc, as we're only interested in creating a shared library to
            # load with ctypes, not in creating an importable Python extension.
            # - First, we replace the cl.exe or link.exe call with an nvcc
            #   call. In case we're running Anaconda, we search cl.exe in the
            #   original search path we captured further above -- Anaconda
            #   inserts a MSVC version into PATH that is too old for nvcc.
            cmd[:1] = ['nvcc', '--compiler-bindir',
                       os.path.dirname(find_executable("cl.exe", PATH))
                       or cmd[0]]
            # - Secondly, we fix a bunch of command line arguments.
            for idx, c in enumerate(cmd):
                # create .dll instead of .pyd files
                #if '.pyd' in c: cmd[idx] = c = c.replace('.pyd', '.dll')  #20160601, by MrX
                # replace /c by -c
                if c == '/c': cmd[idx] = '-c'
                # replace /DLL by --shared
                elif c == '/DLL': cmd[idx] = '--shared'
                # remove --compiler-options=-fPIC
                elif '-fPIC' in c: del cmd[idx]
                # replace /Tc... by ...
                elif c.startswith('/Tc'): cmd[idx] = c[3:]
                # replace /Fo... by -o ...
                elif c.startswith('/Fo'): cmd[idx:idx+1] = ['-o', c[3:]]
                # replace /LIBPATH:... by -L...
                elif c.startswith('/LIBPATH:'): cmd[idx] = '-L' + c[9:]
                # replace /OUT:... by -o ...
                elif c.startswith('/OUT:'): cmd[idx:idx+1] = ['-o', c[5:]]
                # remove /EXPORT:initlibcudamat or /EXPORT:initlibcudalearn
                elif c.startswith('/EXPORT:'): del cmd[idx]
                # replace cublas.lib by -lcublas
                elif c == 'cublas.lib': cmd[idx] = '-lcublas'
            # - Finally, we pass on all arguments starting with a '/' to the
            #   compiler or linker, and have nvcc handle all other arguments
            if '--shared' in cmd:
                pass_on = '--linker-options='
                # we only need MSVCRT for a .dll, remove CMT if it sneaks in:
                pass_on = '--compiler-options='
            cmd = ([c for c in cmd if c[0] != '/'] +
                   [pass_on + ','.join(c for c in cmd if c[0] == '/')])
            # For the future: Apart from the wrongly set PATH by Anaconda, it
            # would suffice to run the following for compilation on Windows:
            # nvcc -c -O -o <file>.obj <file>.cu
            # And the following for linking:
            # nvcc --shared -o <file>.dll <file1>.obj <file2>.obj -lcublas
            # This could be done by a NVCCCompiler class for all platforms.
        spawn(cmd, search_path, verbose, dry_run)

      description="Performs linear algebra computation on the GPU via CUDA",
      cmdclass={'build_ext': CUDA_build_ext},


python install
python install

如果提示Microsoft Visual C++ 9.0 is required ...,在CMD中输入以下命令:

SET VS90COMNTOOLS=%VS110COMNTOOLS% (如果电脑中装的是vs2012)
SET VS90COMNTOOLS=%VS120COMNTOOLS% (如果电脑中装的是vs2013)

然后最后出现Finished processing ...就编译成功了。


之前已经安装了caffe的windows版,然后要编译pycaffe,打开buildVS2013下的工程,在pycaffe的属性中的预编译器中添加 WITH_PYTHON_LAYER,这样才会有Python类型的layer可以用










------------ 转载请注明出处 ------------
posted @ 2017-06-09 23:18  whlook  阅读(4618)  评论(1编辑  收藏  举报