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# -------------------------------------------------------- | ||
# Fast R-CNN | ||
# Copyright (c) 2015 Microsoft | ||
# Licensed under The MIT License [see LICENSE for details] | ||
# Written by Ross Girshick | ||
# -------------------------------------------------------- | ||
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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 | ||
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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 | ||
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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. | ||
""" | ||
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# 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)) | ||
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cudaconfig = {'home':home, 'nvcc':nvcc, | ||
'include': pjoin(home, 'include'), | ||
'lib64': pjoin(home, 'lib64')} # for ubuntu 'lib\\x64' for windows | ||
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)) | ||
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return cudaconfig | ||
CUDA = locate_cuda() | ||
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# 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() | ||
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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.""" | ||
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# tell the compiler it can processes .cu | ||
self.src_extensions.append('.cu') | ||
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# save references to the default compiler_so and _comple methods | ||
default_compiler_so = self.compiler_so | ||
super = self._compile | ||
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# 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'] | ||
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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 | ||
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# inject our redefined _compile method into the class | ||
self._compile = _compile | ||
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# 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) | ||
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ext_modules = [ | ||
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Extension( | ||
"rotation.rotate_cython_nms", | ||
["rotation/rotate_cython_nms.pyx"], | ||
extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, | ||
include_dirs = [numpy_include] | ||
), | ||
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Extension( | ||
"rotation.rotate_circle_nms", | ||
["rotation/rotate_circle_nms.pyx"], | ||
extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, | ||
include_dirs = [numpy_include] | ||
), | ||
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Extension('rotation.rotate_gpu_nms', | ||
['rotation/rotate_nms_kernel.cu', 'rotation/rotate_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 anrbd 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('rotation.rbbox_overlaps', | ||
['rotation/rbbox_overlaps_kernel.cu', 'rotation/rbbox_overlaps.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('rotation.rotate_polygon_nms', | ||
['rotation/rotate_polygon_nms_kernel.cu', 'rotation/rotate_polygon_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']] | ||
), | ||
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] | ||
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setup( | ||
name='RRPN', | ||
ext_modules=ext_modules, | ||
# inject our custom trigger | ||
cmdclass={'build_ext': custom_build_ext}, | ||
) |
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. | ||
#!/usr/bin/env python | ||
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import glob | ||
import os | ||
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import torch | ||
from setuptools import find_packages | ||
from setuptools import setup | ||
from torch.utils.cpp_extension import CUDA_HOME | ||
from torch.utils.cpp_extension import CppExtension | ||
from torch.utils.cpp_extension import CUDAExtension | ||
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requirements = ["torch", "torchvision"] | ||
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def get_extensions(): | ||
this_dir = os.path.dirname(os.path.abspath(__file__)) | ||
extensions_dir = os.path.join(this_dir, "maskrcnn_benchmark", "csrc") | ||
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main_file = glob.glob(os.path.join(extensions_dir, "*.cpp")) | ||
source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp")) | ||
source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu")) | ||
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sources = main_file + source_cpu | ||
extension = CppExtension | ||
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extra_compile_args = {"cxx": []} | ||
define_macros = [] | ||
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if torch.cuda.is_available() and CUDA_HOME is not None: | ||
extension = CUDAExtension | ||
sources += source_cuda | ||
define_macros += [("WITH_CUDA", None)] | ||
extra_compile_args["nvcc"] = [ | ||
"-DCUDA_HAS_FP16=1", | ||
"-D__CUDA_NO_HALF_OPERATORS__", | ||
"-D__CUDA_NO_HALF_CONVERSIONS__", | ||
"-D__CUDA_NO_HALF2_OPERATORS__", | ||
] | ||
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sources = [os.path.join(extensions_dir, s) for s in sources] | ||
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include_dirs = [extensions_dir] | ||
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ext_modules = [ | ||
extension( | ||
"maskrcnn_benchmark._C", | ||
sources, | ||
include_dirs=include_dirs, | ||
define_macros=define_macros, | ||
extra_compile_args=extra_compile_args, | ||
) | ||
] | ||
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return ext_modules | ||
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setup( | ||
name="maskrcnn_benchmark", | ||
version="0.1", | ||
author="fmassa", | ||
url="https://github.com/facebookresearch/maskrcnn-benchmark", | ||
description="object detection in pytorch", | ||
packages=find_packages(exclude=("configs", "tests",)), | ||
# install_requires=requirements, | ||
ext_modules=get_extensions(), | ||
cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, | ||
) |