pcb defect detetcion application

[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
new file mode 100755 (executable)
index 0000000..1227d34
--- /dev/null
+++ b/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/setup.py
@@ -0,0 +1,133 @@
+# --------------------------------------------------------
+# 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},
+)