def create_extension(name, headers, sources, verbose=True, with_cuda=False,
      package=False, relative_to='.', **kwargs):
 """Creates and configures a cffi.FFI object, that builds PyTorch extension.

 Arguments:
  name (str): package name. Can be a nested module e.g. ``.ext.my_lib``.
  headers (str or List[str]): list of headers, that contain only exported
   functions
  sources (List[str]): list of sources to compile.
  verbose (bool, optional): if set to ``False``, no output will be printed
   (default: True).
  with_cuda (bool, optional): set to ``True`` to compile with CUDA headers
   (default: False)
  package (bool, optional): set to ``True`` to build in package mode (for modules
   meant to be installed as pip packages) (default: False).
  relative_to (str, optional): path of the build file. Required when
   ``package is True``. It's best to use ``__file__`` for this argument.
  kwargs: additional arguments that are passed to ffi to declare the
   extension. See `Extension API reference`_ for details.

 .. _`Extension API reference`: https://docs.python.org/3/distutils/apiref.html#distutils.core.Extension
 """
 base_path = os.path.abspath(os.path.dirname(relative_to))
 name_suffix, target_dir = _create_module_dir(base_path, name)
 if not package:
  cffi_wrapper_name = '_' + name_suffix
 else:
  cffi_wrapper_name = (name.rpartition('.')[0] +
        '.{0}._{0}'.format(name_suffix))

 wrapper_source, include_dirs = _setup_wrapper(with_cuda)
 include_dirs.extend(kwargs.pop('include_dirs', []))

 if os.sys.platform == 'win32':
  library_dirs = glob.glob(os.getenv('CUDA_PATH', '') + '/lib/x64')
  library_dirs += glob.glob(os.getenv('NVTOOLSEXT_PATH', '') + '/lib/x64')

  here = os.path.abspath(os.path.dirname(__file__))
  lib_dir = os.path.join(here, '..', '..', 'lib')

  library_dirs.append(os.path.join(lib_dir))
 else:
  library_dirs = []
 library_dirs.extend(kwargs.pop('library_dirs', []))

 if isinstance(headers, str):
  headers = [headers]
 all_headers_source = ''
 for header in headers:
  with open(os.path.join(base_path, header), 'r') as f:
   all_headers_source += f.read() + '\n\n'

 ffi = cffi.FFI()
 sources = [os.path.join(base_path, src) for src in sources]
 # NB: TH headers are C99 now
 kwargs['extra_compile_args'] = ['-std=c99'] + kwargs.get('extra_compile_args', [])
 ffi.set_source(cffi_wrapper_name, wrapper_source + all_headers_source,
     sources=sources,
     include_dirs=include_dirs,
     library_dirs=library_dirs, **kwargs)
 ffi.cdef(_typedefs + all_headers_source)

 _make_python_wrapper(name_suffix, '_' + name_suffix, target_dir)

 def build():
  _build_extension(ffi, cffi_wrapper_name, target_dir, verbose)
 ffi.build = build
 return ffi

# -----------------------------------------------------------------------------
# Standard ResNet models
# -----------------------------------------------------------------------------
# ResNet-50 (包括所有的阶段)
# ResNet 分为５个阶段，但是第一个阶段都相同，变化是从第二个阶段开始的，所以下面的index是从第二个阶段开始编号的。其中block_count为该阶段区块的个数
ResNet50StagesTo5 = tuple(
 StageSpec(index=i, block_count=c, return_features=r)
 for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 6, False), (4, 3, True))
)
# ResNet-50 up to stage 4 (excludes stage 5)
ResNet50StagesTo4 = tuple(
 StageSpec(index=i, block_count=c, return_features=r)
 for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 6, True))
)
# ResNet-101 (including all stages)
ResNet101StagesTo5 = tuple(
 StageSpec(index=i, block_count=c, return_features=r)
 for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 23, False), (4, 3, True))
)
# ResNet-101 up to stage 4 (excludes stage 5)
ResNet101StagesTo4 = tuple(
 StageSpec(index=i, block_count=c, return_features=r)
 for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 23, True))
)
# ResNet-50-FPN (including all stages)
ResNet50FPNStagesTo5 = tuple(
 StageSpec(index=i, block_count=c, return_features=r)
 for (i, c, r) in ((1, 3, True), (2, 4, True), (3, 6, True), (4, 3, True))
)
# ResNet-101-FPN (including all stages)
ResNet101FPNStagesTo5 = tuple(
 StageSpec(index=i, block_count=c, return_features=r)
 for (i, c, r) in ((1, 3, True), (2, 4, True), (3, 23, True), (4, 3, True))
)
# ResNet-152-FPN (including all stages)
ResNet152FPNStagesTo5 = tuple(
 StageSpec(index=i, block_count=c, return_features=r)
 for (i, c, r) in ((1, 3, True), (2, 8, True), (3, 36, True), (4, 3, True))
)

def _make_stage(
 transformation_module,
 in_channels,
 bottleneck_channels,
 out_channels,
 block_count,
 num_groups,
 stride_in_1x1,
 first_stride,
 dilation=1,
 dcn_config={}
):
 blocks = []
 stride = first_stride
 # 根据不同的配置，构造不同的卷基层
 for _ in range(block_count):
  blocks.append(
   transformation_module(
    in_channels,
    bottleneck_channels,
    out_channels,
    num_groups,
    stride_in_1x1,
    stride,
    dilation=dilation,
    dcn_config=dcn_config
   )
  )
  stride = 1
  in_channels = out_channels
 return nn.Sequential(*blocks)

_STAGE_SPECS = Registry({
 "R-50-C4": ResNet50StagesTo4,
 "R-50-C5": ResNet50StagesTo5,
 "R-101-C4": ResNet101StagesTo4,
 "R-101-C5": ResNet101StagesTo5,
 "R-50-FPN": ResNet50FPNStagesTo5,
 "R-50-FPN-RETINANET": ResNet50FPNStagesTo5,
 "R-101-FPN": ResNet101FPNStagesTo5,
 "R-101-FPN-RETINANET": ResNet101FPNStagesTo5,
 "R-152-FPN": ResNet152FPNStagesTo5,
})

class Bottleneck(nn.Module):
 def __init__(
  self,
  in_channels,
  bottleneck_channels,
  out_channels,
  num_groups,
  stride_in_1x1,
  stride,
  dilation,
  norm_func,
  dcn_config
 ):
  super(Bottleneck, self).__init__()
  # 区块旁边的旁支
  self.downsample = None
  if in_channels != out_channels:
   # 获得卷积的步长　使用一个长度为１的卷积核对输入特征进行卷积，使得其输出通道数等于主体部分的输出通道数
   down_stride = stride if dilation == 1 else 1
   self.downsample = nn.Sequential(
    Conv2d(
     in_channels, out_channels,
     kernel_size=1, stride=down_stride, bias=False
    ),
    norm_func(out_channels),
   )
   for modules in [self.downsample,]:
    for l in modules.modules():
     if isinstance(l, Conv2d):
      nn.init.kaiming_uniform_(l.weight, a=1)
 
  if dilation > 1:
   stride = 1 # reset to be 1
 
  # The original MSRA ResNet models have stride in the first 1x1 conv
  # The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have
  # stride in the 3x3 conv
  # 步长
  stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride)
  #　区块中主体部分，这一部分为固定结构
  # 使得特征经过长度大小为１的卷积核
  self.conv1 = Conv2d(
   in_channels,
   bottleneck_channels,
   kernel_size=1,
   stride=stride_1x1,
   bias=False,
  )
  self.bn1 = norm_func(bottleneck_channels)
  # TODO: specify init for the above
  with_dcn = dcn_config.get("stage_with_dcn", False)
  if with_dcn:
   # 使用dcn网络
   deformable_groups = dcn_config.get("deformable_groups", 1)
   with_modulated_dcn = dcn_config.get("with_modulated_dcn", False)
   self.conv2 = DFConv2d(
    bottleneck_channels, 
    bottleneck_channels, 
    defrost=with_modulated_dcn,
    kernel_size=3, 
    stride=stride_3x3, 
    groups=num_groups,
    dilation=dilation,
    deformable_groups=deformable_groups,
    bias=False
   )
  else:
   # 使得特征经过长度大小为３的卷积核
   self.conv2 = Conv2d(
    bottleneck_channels,
    bottleneck_channels,
    kernel_size=3,
    stride=stride_3x3,
    padding=dilation,
    bias=False,
    groups=num_groups,
    dilation=dilation
   )
   nn.init.kaiming_uniform_(self.conv2.weight, a=1)
 
  self.bn2 = norm_func(bottleneck_channels)
 
  self.conv3 = Conv2d(
   bottleneck_channels, out_channels, kernel_size=1, bias=False
  )
  self.bn3 = norm_func(out_channels)
 
  for l in [self.conv1, self.conv3,]:
   nn.init.kaiming_uniform_(l.weight, a=1)
 
 def forward(self, x):
  identity = x
 
  out = self.conv1(x)
  out = self.bn1(out)
  out = F.relu_(out)
 
  out = self.conv2(out)
  out = self.bn2(out)
  out = F.relu_(out)
 
  out0 = self.conv3(out)
  out = self.bn3(out0)
 
  if self.downsample is not None:
   identity = self.downsample(x)
 
  out += identity
  out = F.relu_(out)
 
  return out

class BaseStem(nn.Module):
 def __init__(self, cfg, norm_func):
  super(BaseStem, self).__init__()
  # 获取backbone的输出特征层的输出通道数，由用户自定义
  out_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
  # 输入通道数为图像的三原色，输出为输出通道数，这一部分是固定的，又Resnet论文定义的
  self.conv1 = Conv2d(
   3, out_channels, kernel_size=7, stride=2, padding=3, bias=False
  )
  self.bn1 = norm_func(out_channels)
 
  for l in [self.conv1,]:
   nn.init.kaiming_uniform_(l.weight, a=1)
 
 def forward(self, x):
  x = self.conv1(x)
  x = self.bn1(x)
  x = F.relu_(x)
  x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
  return x

class BottleneckWithFixedBatchNorm(Bottleneck):
 def __init__(
  self,
  in_channels,
  bottleneck_channels,
  out_channels,
  num_groups=1,
  stride_in_1x1=True,
  stride=1,
  dilation=1,
  dcn_config={}
 ):
  super(BottleneckWithFixedBatchNorm, self).__init__(
   in_channels=in_channels,
   bottleneck_channels=bottleneck_channels,
   out_channels=out_channels,
   num_groups=num_groups,
   stride_in_1x1=stride_in_1x1,
   stride=stride,
   dilation=dilation,
   norm_func=FrozenBatchNorm2d,
   dcn_config=dcn_config
  )
 
 
class StemWithFixedBatchNorm(BaseStem):
 def __init__(self, cfg):
  super(StemWithFixedBatchNorm, self).__init__(
   cfg, norm_func=FrozenBatchNorm2d
  )
 
 
class BottleneckWithGN(Bottleneck):
 def __init__(
  self,
  in_channels,
  bottleneck_channels,
  out_channels,
  num_groups=1,
  stride_in_1x1=True,
  stride=1,
  dilation=1,
  dcn_config={}
 ):
  super(BottleneckWithGN, self).__init__(
   in_channels=in_channels,
   bottleneck_channels=bottleneck_channels,
   out_channels=out_channels,
   num_groups=num_groups,
   stride_in_1x1=stride_in_1x1,
   stride=stride,
   dilation=dilation,
   norm_func=group_norm,
   dcn_config=dcn_config
  )
 
 
class StemWithGN(BaseStem):
 def __init__(self, cfg):
  super(StemWithGN, self).__init__(cfg, norm_func=group_norm)
 
 
_TRANSFORMATION_MODULES = Registry({
 "BottleneckWithFixedBatchNorm": BottleneckWithFixedBatchNorm,
 "BottleneckWithGN": BottleneckWithGN,
})

_TRANSFORMATION_MODULES = Registry({
 "BottleneckWithFixedBatchNorm": BottleneckWithFixedBatchNorm,
 "BottleneckWithGN": BottleneckWithGN,
})
 
_STEM_MODULES = Registry({
 "StemWithFixedBatchNorm": StemWithFixedBatchNorm,
 "StemWithGN": StemWithGN,
})

class ResNet(nn.Module):
 def __init__(self, cfg):
  super(ResNet, self).__init__()
 
  # If we want to use the cfg in forward(), then we should make a copy
  # of it and store it for later use:
  # self.cfg = cfg.clone()
 
  # Translate string names to implementations
  # 第一层conv层，也是第一阶段，以stem的形式展现
  stem_module = _STEM_MODULES[cfg.MODEL.RESNETS.STEM_FUNC]
  # 得到指定的backbone结构
  stage_specs = _STAGE_SPECS[cfg.MODEL.BACKBONE.CONV_BODY]
  # 　得到具体bottleneck结构，也就是指出组成backbone基本模块的类型
  transformation_module = _TRANSFORMATION_MODULES[cfg.MODEL.RESNETS.TRANS_FUNC]
 
  # Construct the stem module
  self.stem = stem_module(cfg)
 
  # Constuct the specified ResNet stages
  # 用于group normalization设置的组数
  num_groups = cfg.MODEL.RESNETS.NUM_GROUPS
  # 指定每一组拥有的通道数
  width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP
  # stem是第一层的结构，它的输出也就是第二层一下的组合结构的输入通道数，内部通道数是可以自由定义的
  in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
  # 使用group的数目和每一组的通道数来得出组成backbone基本模块的内部通道数
  stage2_bottleneck_channels = num_groups * width_per_group
  # 第二阶段的输出通道数
  stage2_out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
  self.stages = []
  self.return_features = {}
  for stage_spec in stage_specs:
   name = "layer" + str(stage_spec.index)
   # 以下每一阶段的输入输出层的通道数都可以由stage2层的得到，即２倍关系
   stage2_relative_factor = 2 ** (stage_spec.index - 1)
   bottleneck_channels = stage2_bottleneck_channels * stage2_relative_factor
   out_channels = stage2_out_channels * stage2_relative_factor
   stage_with_dcn = cfg.MODEL.RESNETS.STAGE_WITH_DCN[stage_spec.index -1]
   # 得到每一阶段的卷积结构
   module = _make_stage(
    transformation_module,
    in_channels,
    bottleneck_channels,
    out_channels,
    stage_spec.block_count,
    num_groups,
    cfg.MODEL.RESNETS.STRIDE_IN_1X1,
    first_stride=int(stage_spec.index > 1) + 1,
    dcn_config={
     "stage_with_dcn": stage_with_dcn,
     "with_modulated_dcn": cfg.MODEL.RESNETS.WITH_MODULATED_DCN,
     "deformable_groups": cfg.MODEL.RESNETS.DEFORMABLE_GROUPS,
    }
   )
   in_channels = out_channels
   self.add_module(name, module)
   self.stages.append(name)
   self.return_features[name] = stage_spec.return_features
 
  # Optionally freeze (requires_grad=False) parts of the backbone
  self._freeze_backbone(cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT)
  
# 　 固定某一层的参数不再更新
 def _freeze_backbone(self, freeze_at):
  if freeze_at < 0:
   return
  for stage_index in range(freeze_at):
   if stage_index == 0:
    m = self.stem # stage 0 is the stem
   else:
    m = getattr(self, "layer" + str(stage_index))
   for p in m.parameters():
    p.requires_grad = False
 
 def forward(self, x):
  outputs = []
  x = self.stem(x)
  for stage_name in self.stages:
   x = getattr(self, stage_name)(x)
   if self.return_features[stage_name]:
    outputs.append(x)
  return outputs

class ResNetHead(nn.Module):
 def __init__(
  self,
  block_module,
  stages,
  num_groups=1,
  width_per_group=64,
  stride_in_1x1=True,
  stride_init=None,
  res2_out_channels=256,
  dilation=1,
  dcn_config={}
 ):
  super(ResNetHead, self).__init__()
 
  stage2_relative_factor = 2 ** (stages[0].index - 1)
  stage2_bottleneck_channels = num_groups * width_per_group
  out_channels = res2_out_channels * stage2_relative_factor
  in_channels = out_channels // 2
  bottleneck_channels = stage2_bottleneck_channels * stage2_relative_factor
 
  block_module = _TRANSFORMATION_MODULES[block_module]
 
  self.stages = []
  stride = stride_init
  for stage in stages:
   name = "layer" + str(stage.index)
   if not stride:
    stride = int(stage.index > 1) + 1
   module = _make_stage(
    block_module,
    in_channels,
    bottleneck_channels,
    out_channels,
    stage.block_count,
    num_groups,
    stride_in_1x1,
    first_stride=stride,
    dilation=dilation,
    dcn_config=dcn_config
   )
   stride = None
   self.add_module(name, module)
   self.stages.append(name)
  self.out_channels = out_channels
 
 def forward(self, x):
  for stage in self.stages:
   x = getattr(self, stage)(x)
  return x