maskrcnn-benchmark 代码详解之 resnet.py
１Resnet 结构
Resnet 一般分为５个卷积(conv)层，每一层为一个stage。其中每一个stage中由不同数量的相同的block(区块)构成，这些区块的个数就是block_count, 第一个stage跟其他几个stage结构完全不同，也可以看做是由单独的区块构成的，因此由区块不停堆叠构成的第二层到第５层(即stage2-stage5或conv2-conv5)，分别定义为index1-index4.就像搭积木一样，这四个层可有基本的区块搭成。下图为resnet的基本结构：
Resnet 的结构以及在代码中一些变量的描述
以下代码通过控制区块的多少，搭建出不同的Resnet(包括Resnet50等）：
# -----------------------------------------------------------------------------
# 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))
)根据以上的不同组合方案，maskrcnn benchmark可以搭建起不同的backbone:
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)
这几种不同的backbone之后被集成为一个统一的对象以便于调用，其代码为:
_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,
})２区块(block）结构
2.1 Bottleneck结构
刚刚提到，在Resnet中，第一层卷基层可以看做一种区块，而第二层到第五层由不同的称之为Bottleneck的区块堆叠二层。第一层可以看做一个stem区块。其中Bottleneck的结构如下：
Bottleneck 区块结构
在maskrcnn benchmark中构造以上结构的代码为：
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
2.２ Stem结构
刚刚提到Resnet的第一层可以看做是一个Stem结构，其结构的代码为：
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 x2.３ 两种结构的衍生与封装
在maskrcnn benchmark中，对上面提到的这两种block结构进行的衍生和封装，Bottleneck和Stem分别衍生出带有Batch Normalization 和 Group Normalizetion的封装类，分别为：BottleneckWithFixedBatchNorm, StemWithFixedBatchNorm, BottleneckWithGN, StemWithGN. 其代码过于简单，就不做注释：
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,
})接着，这两种结构关于BN和GN的四种衍生类被封装起来，以便于调用。其封装为：
_TRANSFORMATION_MODULES = Registry({
"BottleneckWithFixedBatchNorm": BottleneckWithFixedBatchNorm,
"BottleneckWithGN": BottleneckWithGN,
})
_STEM_MODULES = Registry({
"StemWithFixedBatchNorm": StemWithFixedBatchNorm,
"StemWithGN": StemWithGN,
})3 Resnet总体结构
３.1 Resnet结构
在以上的基础上，我们可以在以上结构上进一步搭建起真正的Resnet. 其中包括第一层卷基层，和其他四个阶段，代码为：
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 outputs３.２ Resnet head结构
Head，在我理解看来就是完成某种功能的网络结构，Resnet head就是指使用Bottleneck块堆叠成不同的用于构成Resnet的功能网络结构，它内部结构相似，完成某种功能。在此不做过多介绍，因为是上面的Resnet子结构
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