win 10 mmdetection 配置

-----2019-7-22 更新

mmdetection 维护人员看来很用心啊，已经适配到了最新的pytorch 1.1 并且修改了编译的方式，看来是要官方支持Windows了，但是目前win下有两个bug 。

sigmoid_focal_loss 和 mask 那个层的编译有问题。官方作者在github 上已经给予了回复。貌似是torch的bug

目前如果想用的话，只能先注释掉相关的代码。

商汤科技（2018 COCO 目标检测挑战赛冠军）和香港中文大学最近开源了一个基于Pytorch实现的深度学习目标检测工具箱mmdetection，支持Faster-RCNN，Mask-RCNN，Fast-RCNN等主流的目标检测框架，后续会加入Cascade-RCNN以及其他一系列目标检测框架。

相比于Facebook开源的Detectron框架，作者声称mmdetection有三点优势：performance稍高、训练速度稍快、所需显存稍小。

我很早就听说了这个工具箱，但是一直没有开源。现在总算是开源了，发现官方没有对Windows系统进行适配，于是就迫不及待地对win10 进行了适配。下面将记录一下

首先官方给出的编译的方法是./compile.sh 我们发现这里面其实是执行了4 个python脚本，但是这4个setup.py 在win下执行会报错，我修改了一个版本。

首先dcn 目录下的setup.py 修改为两个文件，否则链接时候会出现错误。分别为setup_conv.py setup_pool.py

import os

from setuptools import setup

from torch.utils.cpp_extension import BuildExtension, CUDAExtension,CppExtension,CUDA_HOME

import torch

def get_extensions():

this_dir = os.path.dirname(os.path.abspath(__file__))

extension = CppExtension

extra_compile_args = {"cxx": []}

define_macros = []

sources=[

'src/deform_conv_cuda.cpp',

'src/deform_conv_cuda_kernel.cu']

if torch.cuda.is_available() and CUDA_HOME is not None:

extension = CUDAExtension

extra_compile_args["nvcc"] = [

"-DCUDA_HAS_FP16=1",

"-D__CUDA_NO_HALF_OPERATORS__",

"-D__CUDA_NO_HALF_CONVERSIONS__",

"-D__CUDA_NO_HALF2_OPERATORS__",

]

ext_modules = [

extension(

"deform_conv_cuda",

sources,

extra_compile_args=extra_compile_args,

]

return ext_modules

setup(

name='deform_conv',

ext_modules=get_extensions(),

cmdclass={'build_ext': BuildExtension})

import os

from setuptools import setup

from torch.utils.cpp_extension import BuildExtension, CUDAExtension,CppExtension,CUDA_HOME

import torch

def get_extensions():

this_dir = os.path.dirname(os.path.abspath(__file__))

extension = CppExtension

extra_compile_args = {"cxx": []}

define_macros = []

sources=[

'src/deform_pool_cuda.cpp',

'src/deform_pool_cuda_kernel.cu'

]

if torch.cuda.is_available() and CUDA_HOME is not None:

extension = CUDAExtension

extra_compile_args["nvcc"] = [

"-DCUDA_HAS_FP16=1",

"-D__CUDA_NO_HALF_OPERATORS__",

"-D__CUDA_NO_HALF_CONVERSIONS__",

"-D__CUDA_NO_HALF2_OPERATORS__",

]

ext_modules = [

extension(

"deform_pool_cuda",

sources,

extra_compile_args=extra_compile_args,

]

return ext_modules

setup(

name='deform_conv',

ext_modules=get_extensions(),

cmdclass={'build_ext': BuildExtension})

接着 nms 目录下，

import os.path as osp

from setuptools import setup, Extension

import numpy as np

from Cython.Build import cythonize

from Cython.Distutils import build_ext

from torch.utils.cpp_extension import BuildExtension, CUDAExtension

ext_args = dict(

include_dirs=[np.get_include()],

language='c++',

extra_compile_args={

'cc': ['-Wno-unused-function', '-Wno-write-strings'],

'nvcc': ['-c', '--compiler-options', '-fPIC'],

)

extensions = [

Extension('soft_nms_cpu', ['src/soft_nms_cpu.pyx'], **ext_args),

]

def customize_compiler_for_nvcc(self):

"""inject deep into distutils to customize how the dispatch

to cc/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')

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):

if osp.splitext(src)[1] == '.cu':

# use the cuda for .cu files

self.set_executable('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['cc']

super(obj, src, ext, cc_args, postargs, pp_opts)

# inject our redefined _compile method into the class

self._compile = _compile

class custom_build_ext(build_ext):

def build_extensions(self):

customize_compiler_for_nvcc(self.compiler)

build_ext.build_extensions(self)

setup(

name='soft_nms',

cmdclass={'build_ext': custom_build_ext},

ext_modules=cythonize(extensions),

)

setup(

name='nms_cuda',

ext_modules=[

CUDAExtension('nms_cuda', [

'src/nms_cuda.cpp',

'src/nms_kernel.cu',

]),

CUDAExtension('nms_cpu', [

'src/nms_cpu.cpp',

]),

cmdclass={'build_ext': BuildExtension})

roi_align 目录下

import os

from setuptools import setup

from torch.utils.cpp_extension import BuildExtension, CUDAExtension,CppExtension,CUDA_HOME

import torch

def get_extensions():

this_dir = os.path.dirname(os.path.abspath(__file__))

extension = CppExtension

extra_compile_args = {"cxx": []}

define_macros = []

sources=[

'src/roi_align_cuda.cpp',

'src/roi_align_kernel.cu']

if torch.cuda.is_available() and CUDA_HOME is not None:

extension = CUDAExtension

extra_compile_args["nvcc"] = [

"-DCUDA_HAS_FP16=1",

"-D__CUDA_NO_HALF_OPERATORS__",

"-D__CUDA_NO_HALF_CONVERSIONS__",

"-D__CUDA_NO_HALF2_OPERATORS__",

]

ext_modules = [

extension(

"roi_align_cuda",

sources,

extra_compile_args=extra_compile_args,

)

]

return ext_modules

setup(

name='roi_align_cuda',

ext_modules=get_extensions(),

cmdclass={'build_ext': BuildExtension})

roi_pool 目录下

import os

from setuptools import setup

from torch.utils.cpp_extension import BuildExtension, CUDAExtension,CppExtension,CUDA_HOME

import torch

def get_extensions():

this_dir = os.path.dirname(os.path.abspath(__file__))

extension = CppExtension

extra_compile_args = {"cxx": []}

define_macros = []

sources=[

'src/roi_pool_cuda.cpp',

'src/roi_pool_kernel.cu']

if torch.cuda.is_available() and CUDA_HOME is not None:

extension = CUDAExtension

extra_compile_args["nvcc"] = [

"-DCUDA_HAS_FP16=1",

"-D__CUDA_NO_HALF_OPERATORS__",

"-D__CUDA_NO_HALF_CONVERSIONS__",

"-D__CUDA_NO_HALF2_OPERATORS__",

]

ext_modules = [

extension(

"roi_pool_cuda",

sources,

extra_compile_args=extra_compile_args,

)

]

return ext_modules

setup(

name='roi_pool_cuda',

ext_modules=get_extensions(),

cmdclass={'build_ext': BuildExtension})

至此，就修改完了全部的setup.py 然后去各个目录分别执行python setup.py build_ext --inplace

执行完成之后，到项目根目录下执行 python setuy.py install 即可。

注意，VC++ 版本要和cuda 版本对应上，pytorch版本1.0 以上，

Windows不支持distribution 库，训练时候，可能需要相应的修改源码，否则会报错。

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