content: # --------------------------------------------------------
  # 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
  import numpy as np
  from distutils.core import setup
  from distutils.extension import Extension
  from Cython.Distutils import build_ext
  
  
  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):
          print (extra_postargs)
          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(
          "utils.cython_nms",
          ["utils/nms.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},
  )
  