倒置残差与线性瓶颈层从原理到实现

发布于:2021-10-25 02:08:15

The MobileNet v2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input. MobileNet v2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, non-linearities in the narrow layers were removed in order to maintain representational power.

Evolution of separable convolution blocks. The diagonally hatched texture(斜线阴影条纹) indicates layers that do not contain non-linearities. The last (lightly colored) layer indicates the beginning of the next block. Note: 2d and 2c are equivalent blocks when stacked. Best viewed in color.


ResNet

原始的瓶颈结构



ResNet的核心内容之一,即“Deeper Bottleneck Architectures”(简称DBA),bottleneck是一种特殊的残差结构。
Resnet论文里的原图如上(即Bottleneck V1 ),左图是普通的残差结构,右图是瓶颈结构。


下面在来看整体resnet构造之前看一下基本单元的构造,包括stem(主干)和Bottleneck(瓶颈结构),通过这些堆叠,就构成了整个resnet:其中stem(主干)部分就是上图中conv1的7×7卷积和con2_x中的3×3的max pool;而对于Bottleneck(瓶颈结构),可以看到对于resnet50,resnet101,resnet152都是一样的结构,只是block在每个stage的数量不同,所以可以用循环很好地构造出来:



# Copyright (c) *, Inc. and its affiliates. All Rights Reserved.
"""
Variant of the resnet module that takes cfg as an argument.
Example usage. Strings may be specified in the config file.
model = ResNet(
"StemWithFixedBatchNorm",
"BottleneckWithFixedBatchNorm",
"ResNet50StagesTo4",
)
OR:
model = ResNet(
"StemWithGN",
"BottleneckWithGN",
"ResNet50StagesTo4",
)
Custom implementations may be written in user code and hooked in via the
`register_*` functions.
"""
# 上面是一个使用说明,下面先导入一些必要包
from collections import namedtuple

import torch
import torch.nn.functional as F
from torch import nn

from fcos_core.layers import FrozenBatchNorm2d
from fcos_core.layers import Conv2d
from fcos_core.layers import DFConv2d
from fcos_core.modeling.make_layers import group_norm
from fcos_core.utils.registry import Registry


# ResNet stage specification 通过一个命名元组来设定resnet各阶段的参数
StageSpec = namedtuple(
"StageSpec",
[
"index", # Index of the stage, eg 1, 2, ..,. 5
"block_count", # Number of residual blocks in the stage
"return_features", # True => return the last feature map from this stage
],
)

# -----------------------------------------------------------------------------
# Standard ResNet models
# -----------------------------------------------------------------------------
# 下面这些元组会通过_STAGE_SPECS[cfg.MODEL.BACKBONE.CONV_BODY]来选定,我只放了resnet50的
# ResNet-50 (including all stages)
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)只使用到第四阶段输出的特征图
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-50-FPN (including all stages)由于fpn需要用到每一个阶段输出的特征图, 故return_features参数均为True
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))
)

# 这个指定resnet的Bottleneck结构用FixedBatchNorm还是GroupNorm
_TRANSFORMATION_MODULES = Registry({
"BottleneckWithFixedBatchNorm": BottleneckWithFixedBatchNorm,
"BottleneckWithGN": BottleneckWithGN,
})
# 这个指定resnet的Stem结构用FixedBatchNorm还是GroupNorm
_STEM_MODULES = Registry({
"StemWithFixedBatchNorm": StemWithFixedBatchNorm,
"StemWithGN": StemWithGN,
})
# 这个指定具体构建resnet的哪个深度的模型,并且到第几个stage
_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,
})


# 主干 BaseStem
class BaseStem(nn.Module):
def __init__(self, cfg, norm_func):
super(BaseStem, self).__init__()

out_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS #64->主干的输出通道数

self.conv1 = Conv2d(
3, out_channels, kernel_size=7, stride=2, padding=3, bias=False
)
self.bn1 = norm_func(out_channels) # 通过对应的norm_func归一化层

for l in [self.conv1,]: # 凯明初始化
nn.init.kaiming_uniform_(l.weight, a=1)

def forward(self, x): # 定义前向传播过程
x = self.conv1(x)
x = self.bn1(x)
x = F.relu_(x) # 这里stem也包括了max pool,因为无参数,直接写在forward里
x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
return x

# 下面的StemWithFixedBatchNorm和StemWithGN继承了类BaseStem
# 只不过初始化的时候是FrozenBatchNorm2d还是group_norm
class StemWithFixedBatchNorm(BaseStem):
def __init__(self, cfg):
super(StemWithFixedBatchNorm, self).__init__(
cfg, norm_func=FrozenBatchNorm2d
)

class StemWithGN(BaseStem):
def __init__(self, cfg):
super(StemWithGN, self).__init__(cfg, norm_func=group_norm)


class Bottleneck(nn.Module):
def __init__(
self,
in_channels, # bottleneck的输入channels
bottleneck_channels, # bottleneck压缩后的channels
out_channels, # bottleneck的输出channels
num_groups, # bottleneck分组的num
stride_in_1x1, # 在每个stage的开始的1x1conv中的stride
stride, # 卷积步长
dilation, # 膨胀卷积的间隔
norm_func, # 用哪一个归一化函数
dcn_config # Deformable Convolutional Networks配置情况
):
super(Bottleneck, self).__init__()

# downsample: 当 bottleneck 的输入和输出的 channels 不相等时, 则需要采用一定的策略
# 在原文中, 有 A, B, C三种策略, 本文采用的是 B 策略(也是原文推荐的)
# 即只有在输入输出通道数不相等时才使用 projection shortcuts,
# 也就是利用参数矩阵映射使得输入输出的 channels 相等
self.downsample = None
# 当输入输出通道数不同时, 额外添加一个1×1的卷积层使得输入通道数映射成输出通道数
if in_channels != out_channels:
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
# 这里的意思就是本来论文里的stride=2的卷积用在stage3-5的第一个1x1conv上,现在用在
# 3x3conv里,但是这里因为是原来框架的,我打印出来还是在1x1conv上,系没有删除注释
# 因为下面调用的时候都是stride_in_1x1=True
stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride)

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
# dcn_config字典中有键"stage_with_dcn",则返回对应的值,否则为False
with_dcn = dcn_config.get("stage_with_dcn", False)
# 判断bottleneck的第二层卷积层是否使用可变形卷积
if with_dcn:
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,
with_modulated_dcn=with_modulated_dcn,
kernel_size=3,
stride=stride_3x3,
groups=num_groups,
dilation=dilation,
deformable_groups=deformable_groups,
bias=False
)
else:
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)

# 创建bottleneck的第3层卷积层
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): # 定义前向传播过程
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 # 跳连结构,此时add起来
out = F.relu_(out) # 本地relu

return out

# 当Bottleneck类实现好的时候,BottleneckWithFixedBatchNorm和BottleneckWithGN
# 就是简单的继承它就好了,然后初始化自己的参数,唯一的区别就是norm_func是FrozenBatchNorm2d
# 还是group_norm

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=None
):
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 BottleneckWithGN(Bottleneck):
def __init__(
self,
in_channels,
bottleneck_channels,
out_channels,
num_groups=1,
stride_in_1x1=True,
stride=1,
dilation=1,
dcn_config=None
):
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 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 根据cfg选取具体实现
stem_module = _STEM_MODULES[cfg.MODEL.RESNETS.STEM_FUNC] # eg: "StemWithFixedBatchNorm"
stage_specs = _STAGE_SPECS[cfg.MODEL.BACKBONE.CONV_BODY] # eg: "R-50-FPN-RETINANET"
transformation_module = _TRANSFORMATION_MODULES[cfg.MODEL.RESNETS.TRANS_FUNC]

# Construct the stem module 这里是stem的实现, 也就是resnet的第一阶段conv1
self.stem = stem_module(cfg)

# Constuct the specified ResNet stages resnet conv2_x~conv5_x的实现
num_groups = cfg.MODEL.RESNETS.NUM_GROUPS # eg:1 1时为ResNet, >1 时为ResNeXt
width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP # eg:64
in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS # eg:64
stage2_bottleneck_channels = num_groups * width_per_group # eg:64
stage2_out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS # eg:256
self.stages = []
self.return_features = {}
for stage_spec in stage_specs:
name = "layer" + str(stage_spec.index)
stage2_relative_factor = 2 ** (stage_spec.index - 1)
# 每过一个stage,bottleneck_channels和out_channels 翻倍
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]
# 循环调用_make_stage,依次实现conv2_x~conv5_x
module = _make_stage(
transformation_module, # BottleneckWithFixedBatchNorm
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, # 当处于stage3~5时, 使用stride=2来downsize
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)

def _freeze_backbone(self, freeze_at):
# 根据给定的freeze_at参数冻结相应层的参数更新
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)
# 将stage2~5中需要返回的某些层的特征图以列表形式保存,作为FPN的输入
if self.return_features[stage_name]:
outputs.append(x)
return outputs

def _make_stage(
transformation_module,
in_channels,
bottleneck_channels,
out_channels,
block_count,
num_groups,
stride_in_1x1,
first_stride,
dilation=1,
dcn_config=None
):
blocks = []
stride = first_stride
# 循环调用类Bottleneck,每调用一次构造一个瓶颈结构
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 # 注意就是第一次的stride=first_stride,之后都等于1
in_channels = out_channels
return nn.Sequential(*blocks)



倒置残差卷积主要分三个部分: PointWise1、DepthWise、PointWise2。




倒置残差模块代码

class ConvBNReLU(nn.Sequential):
def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1, norm_layer=None):
padding = (kernel_size - 1) // 2
if norm_layer is None:
norm_layer = nn.BatchNorm2d
super(ConvBNReLU, self).__init__(
nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
norm_layer(out_planes),
nn.ReLU6(inplace=True)
)

class InvertedResidual(nn.Module):
def __init__(self, inp, oup, stride, expand_ratio, norm_layer=None):
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2]

if norm_layer is None:
norm_layer = nn.BatchNorm2d

hidden_dim = int(round(inp * expand_ratio))
self.use_res_connect = self.stride == 1 and inp == oup

layers = []
if expand_ratio != 1:
# pw
layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer))
layers.extend([
# dw
ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim, norm_layer=norm_layer),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
norm_layer(oup),
])
self.conv = nn.Sequential(*layers)

对比 MobileNet V1 和 V2 的网络结构和计算量
MobileNet V1 网络结构和计算量
MobileNet V2 的网络结构和计算量

总结对比

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