最近在看一篇博客，
Faster R-CNN 源码解析（Tensorflow版）（https://blog.csdn.net/u012457308/article/details/79566195）
使用的代码是https://github.com/endernewton/tf-faster-rcnn
问题是这个代码window10编译是个坑，所以又找了一堆资料，最后找到这个
https://github.com/endernewton/tf-faster-rcnn/issues/335
原文：
Modified, with gpu_nms supports
I ran it on windows successfully (Python 3.6.6, VS2015 compiler, CUDA 9.0 with CUDNN v7), just modify some code.
Tested both cpu_nms and gpu_nms (but don't know why gpu_nms is a little slower than cpu_nms).
Note: If any strange error occurred, I suggest deleting all the generated .c and .cpp files (lib/nms/cpu_nms.c, lib/nms/gpu_nms.cpp, lib/utils/bbox.c) and everything in lib/build
file lib/nms/cpu_nms.pyx:
change (line 25)
cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1]
to
cdef np.ndarray[np.int64_t, ndim=1] order = scores.argsort()[::-1]
file lib/nms/gpu_nms.pyx:
change (line 25)
cdef np.ndarray[np.int_t, ndim=1] \
to
cdef np.ndarray[np.int64_t, ndim=1] \
file lib/datasets/pascal_voc.py:
change (line 226)
'{:s}.xml')
to
'{0:s}.xml')
file lib/datasets/voc_eval.py
change (line 121)
with open(cachefile, 'w') as f:
to
with open(cachefile, 'wb') as f:
file lib/setup.py (nearly full modified, new code is as follow):
# --------------------------------------------------------
# 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
nvcc = find_in_path('nvcc.exe', os.environ['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, 'lib', 'x64')}
for k, v in iter(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):
# _msvccompiler.py imports:
import os
import shutil
import stat
import subprocess
import winreg
from distutils.errors import DistutilsExecError, DistutilsPlatformError, \
CompileError, LibError, LinkError
from distutils.ccompiler import CCompiler, gen_lib_options
from distutils import log
from distutils.util import get_platform
from itertools import count
super = self.compile
self.src_extensions.append('.cu')
# find python include
import sys
py_dir = sys.executable.replace('\\', '/').split('/')[:-1]
py_include = pjoin('/'.join(py_dir), 'include')
# override method in _msvccompiler.py, starts from line 340
def compile(sources,
output_dir=None, macros=None, include_dirs=None, debug=0,
extra_preargs=None, extra_postargs=None, depends=None):
if not self.initialized:
self.initialize()
compile_info = self._setup_compile(output_dir, macros, include_dirs,
sources, depends, extra_postargs)
macros, objects, extra_postargs, pp_opts, build = compile_info
compile_opts = extra_preargs or []
compile_opts.append('/c')
if debug:
compile_opts.extend(self.compile_options_debug)
else:
compile_opts.extend(self.compile_options)
add_cpp_opts = False
for obj in objects:
try:
src, ext = build[obj]
except KeyError:
continue
if debug:
# pass the full pathname to MSVC in debug mode,
# this allows the debugger to find the source file
# without asking the user to browse for it
src = os.path.abspath(src)
if ext in self._c_extensions:
input_opt = "/Tc" + src
elif ext in self._cpp_extensions:
input_opt = "/Tp" + src
add_cpp_opts = True
elif ext in self._rc_extensions:
# compile .RC to .RES file
input_opt = src
output_opt = "/fo" + obj
try:
self.spawn([self.rc] + pp_opts + [output_opt, input_opt])
except DistutilsExecError as msg:
raise CompileError(msg)
continue
elif ext in self._mc_extensions:
# Compile .MC to .RC file to .RES file.
# * '-h dir' specifies the directory for the
# generated include file
# * '-r dir' specifies the target directory of the
# generated RC file and the binary message resource
# it includes
#
# For now (since there are no options to change this),
# we use the source-directory for the include file and
# the build directory for the RC file and message
# resources. This works at least for win32all.
h_dir = os.path.dirname(src)
rc_dir = os.path.dirname(obj)
try:
# first compile .MC to .RC and .H file
self.spawn([self.mc, '-h', h_dir, '-r', rc_dir, src])
base, _ = os.path.splitext(os.path.basename(src))
rc_file = os.path.join(rc_dir, base + '.rc')
# then compile .RC to .RES file
self.spawn([self.rc, "/fo" + obj, rc_file])
except DistutilsExecError as msg:
raise CompileError(msg)
continue
elif ext == '.cu':
# a trigger for cu compile
try:
# 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']
arg = [CUDA['nvcc']] + sources + ['-odir', pjoin(output_dir, 'nms')]
for include_dir in include_dirs:
arg.append('-I')
arg.append(include_dir)
arg += ['-I', py_include]
# arg += ['-lib', CUDA['lib64']]
arg += ['-Xcompiler', '/EHsc,/W3,/nologo,/Ox,/MD']
arg += postargs
self.spawn(arg)
continue
except DistutilsExecError as msg:
# raise CompileError(msg)
continue
else:
# how to handle this file?
raise CompileError("Don't know how to compile {} to {}"
.format(src, obj))
args = [self.cc] + compile_opts + pp_opts
if add_cpp_opts:
args.append('/EHsc')
args.append(input_opt)
args.append("/Fo" + obj)
args.extend(extra_postargs)
try:
self.spawn(args)
except DistutilsExecError as msg:
raise CompileError(msg)
return objects
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(
"nms.cpu_nms",
["nms/cpu_nms.pyx"],
include_dirs = [numpy_include]
),
Extension('nms.gpu_nms',
['nms/nms_kernel.cu', 'nms/gpu_nms.pyx'],
library_dirs=[CUDA['lib64']],
libraries=['cudart'],
language='c++',
# 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_52',
'--ptxas-options=-v',
'-c']},
include_dirs = [numpy_include, CUDA['include']]
)
]
setup(
name='tf_faster_rcnn',
ext_modules=ext_modules,
# inject our custom trigger
cmdclass={'build_ext': custom_build_ext},
)
It's take me some time to understand how MSVC compiler works, so I made an ugly patch for the compiler by overriding the compile method.
Note: Change the -arch=sm_52 above to fit your graphic card, otherwise using gpu_nms will get the corrupt result. You can follow the original installation guide.
Now, turn to lib directory, run python setup.py build_ext. It possible reports an error:
nms\gpu_nms.cpp(2075): error C2664: "void _nms(int *,int *,const float *,int,int,float,int)": cannot convert parameter 1 from '__pyx_t_5numpy_int32_t *' to 'int *'
Modify one line in the generated file in lib/nms/gpu_nms.cpp, search the keyword _nms, find the following line:
change (line 2075 in my generated cpp file)
_nms((&(*__Pyx_BufPtrStrided1d(__pyx_t_5numpy_int32_t *, __pyx_pybuffernd_keep.rcbuffer->pybuffer.buf, __pyx_t_10, ...
to
_nms((&(*__Pyx_BufPtrStrided1d(int *, __pyx_pybuffernd_keep.rcbuffer->pybuffer.buf, __pyx_t_10, ...
At last, run python setup.py build_ext again, copy everything in lib/build/lib.win-amd64-3.6 to lib.
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