removed exmple apps code

[ealt-edge.git] / example-apps / PDD / pcb-defect-detection / libs / box_utils / cython_utils / setup.py

diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/setup.py b/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/setup.py
deleted file mode 100755 (executable)
index 1227d34..0000000
--- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/setup.py
+++ /dev/null
@@ -1,133 +0,0 @@
-# --------------------------------------------------------
-# 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.items():
-        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(
-        "cython_bbox",
-        ["bbox.pyx"],
-        extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]},
-        include_dirs = [numpy_include]
-    ),
-    Extension(
-        "cython_nms",
-        ["nms.pyx"],
-        extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]},
-        include_dirs = [numpy_include]
-    )
-    # Extension(
-    #     "cpu_nms",
-    #     ["cpu_nms.pyx"],
-    #     extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]},
-    #     include_dirs = [numpy_include]
-    # )
-]
-
-setup(
-    name='tf_faster_rcnn',
-    ext_modules=ext_modules,
-    # inject our custom trigger
-    cmdclass={'build_ext': custom_build_ext},
-)