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Resnet代码详解

作者：繁依Fanyi0 | 2024-04-05 00:47:02

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resnet代码

这篇文章是用来讲解Resnet(残差网络)代码的，结合代码理解残差网络结构。

可以直接调用torch内置的resnet官方代码。


from torchvision.models import resnet50
model = resnet50()
print("model:", model)

不论是调用resnet50还是resnet101，这些模型都是调用的Resnet模型。因此我们仅需要看这个类就可以。

在ResNet这个类中又由Bottleneck(瓶颈层)、3×3卷积层、1×1卷积层、BasicBlock组成。接下来将逐步解释。

Bottleneck类

拼劲层这个类在resnet50及之后的系列用这个，resnet18、resnet34用BasicBlock

参数说明：

expansion=4：Bottleneck的输出通道数是输入通道数的4背

inplanes:输入通道数

planes:输出通道数

stride:步长

downsample:下采样

groups:分组卷积

base_width:卷积块宽度

dilation:空洞卷积

nor_layer:是否传入norm_layer


class Bottleneck(nn.Module):
    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
    # This variant is also known as ResNet V1.5 and improves accuracy according to
    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
 
    expansion = 4
    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
 
    def forward(self, x):
        identity = x
 
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
 
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)
 
        out = self.conv3(out)
        out = self.bn3(out)
        
        # 表示如果输入和输出通道数不等，那就通过1x1卷积进行升维后的相加操作，否则可以可以直接相加
        if self.downsample is not None:
            identity = self.downsample(x)
 
        out += identity
        out = self.relu(out)
        '''
        if downsample:
            x -->conv_1x1-->bn-->relu-->conv_3x3-->bn-->relu-->conv_1x1-->bn--add-->relu-->out
            |___________downsample____________________________________________|
        else:
            x -->conv_1x1-->bn-->relu-->conv_3x3-->bn-->relu-->conv_1x1-->bn--add-->relu-->out
            |__________________________________________________________________|
        '''
 
        return out

下面这张图是一个Bottleneck结构图，残差边为一个1x1的卷积。

Conv3×3

传入参数：

in_planes:输入通道

out_planes:输出通道

stride:步长

groups:卷积分组数

dilation:可以控制空洞卷积

可以看到这个conv3×3中的kernel_size为3，bias为False，padding的大小和dilation一样。


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)

Conv1×1

in_planes:输入通道数

out_planes:输出通道数

可以看到kernel_size为1，bias为False


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)

BasicBlock

这个BasicBlock当在resnet18、resnet34用这个

传入参数：

inplanes：输入通道数

planes:输出通道数

stride:步长

downsample:下采样

groups:分组数

base_width:宽度

当norm_layer为None的时候，则norm_layer为BN层。当采用groups(分组卷积)或者base_width不为64的时候抛出错误：

'BasicBlock only supports groups=1 and base_width=64'
该错误表示在BasicBlock仅支持groups=1和base_width=64

当dilation>1的时候，表示在BasicBlock采用了空洞卷积，抛出错误。


class BasicBlock(nn.Module):
    expansion = 1
 
    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride
 
    def forward(self, x):
        identity = x
 
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
 
        out = self.conv2(out)
        out = self.bn2(out)
 
        if self.downsample is not None:
            identity = self.downsample(x)
 
        out += identity
        out = self.relu(out)
        """
        if norm_layer is None
            if downsample is not None:
                x -->conv_3x3-->bn-->relu-->conv_3x3-->bn--add--relu-->out
                |____________downsample_____________________|
            if downsample is None:
                x -->conv_3×3-->bn-->relu-->conv_3×3-->bn--add--relu-->out
                |___________________________________________|
        """
        return out

可以看到在BasicBlock(基础块)中当需要进行下采样的时候，残差边需要一次下采样。

ResNet

在正式讲Resnet之前需要讲一下_make_layer函数，因为网络结构中的残差层都是由这个函数决定的。

_make_layer代码解析

参数说明：

block:传入BasicBlock还是Bottleneck

planes:输出通道数

blocks:传入的layer

stride:步长，默认为1

dilate:是否采用空洞卷积，默认为False

这里以不采用空洞卷积，也就是dilate=False，block取Bottlenenck为例。


        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

上面这段代码表示什么时候采用下采样的情况，当stride不为1，或者通道数inplanes(初始默认取值64) ≠ planes * block.expansion(此时block.expansion=4)。输入和输出通道不相等时候，下采样结构定义为：

downsample:

(conv1×1:conv2d(inplanes,4*planes,stride),

norm_layer:BN)


        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion

上面这段代码表示，创建一个空的列表layers，此时的block为Bottleneck，将获得的Bottlenck放入layers列表中。放入以后下一层的inplanes输入通道数为变成上一层输出通道数planes的4倍【也就是为下一个block做准备】。


        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

而上面这一段代码就是表示当前的Bottleneck会重复几次(不过需要注意的是，在每个layer中只在第一个bottleneck用了1x1的残差边)。


    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )
 
        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))
 
        return nn.Sequential(*layers)

接下来是一步一步看ResNet中的代码。

参数说明：

block：表示传入BasicBlock或者Bottleneck层。

layers:传入的是个列表，可以通过获取layers[index]来控制stride,以及是否采用空洞卷积。

num_classes:分类数量

zero_init_residual:初始化

groups:分组数

replace_stride_with_dilation：表示是否传入空洞卷积参数。如果不指定，则赋值为 [False, False, False]，表示不使用空洞卷积。

norm_layer:是否传入norm_layer层，不传入的时候则为BN层。


def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):

代码讲解将以Resnet50为例，那么此时传入的block就为Bottleneck，layer=[3,4,6,3],num_classes=1000,其他Resnet系列可以看下面这张图。在看代码的时候希望大家可以对着下面这个图来看，方便理解。

先看下下面这几行代码，可以看到这三行代码是由一个输入通道为3，输出通道为64，k=7,s=2,paddind=3,bn层，relu函数构成的，这正好就对应到上面图中的conv1。


        # conv1结构代码
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)

然后再看conv2_x。conv2_x是由一个最大池化，还有3个Bottleneck组成(你可以理解为图中的3，4，6，3就是这类结构重复次数)。


        # conv2_x
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])

代码中的layer1调用的是_make_layer函数，

下面这张图为layer1,表示为第一个bottleneck结构。在Resnet的每个Bottlenck中，只在第一个Bottlenck处的残差边会用1x1的卷积进行升维，其他的都是输入和输出直接相加，这个特点需要注意一下。


        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])  #
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])

然后看layer2,3,4，过程和layer1是一样的，只不过这里传入的stride=2.


        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

最后就是连接一个平均池化和全连接用来分类。

完整的ResNet代码：


class ResNet(nn.Module):
 
    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
 
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)
 
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
 
        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)
 
    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )
 
        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))
 
        return nn.Sequential(*layers)
 
    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
 
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
 
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)
 
        return x

所以可以进行一个总结：

_make_layer函数用来制作残差块的结构，参数layer可以用来控制每个残差层是由多少个残差块组成的，在残差块结构中判断是否采用downsample(1x1卷积进行升维)是根据步长或者输入输出通道数是否相等，如果步长为1，输入通道数不等于输出通道数就会采用一个1x1卷积进行升维。每个残差层layer只有第一个残差块是采用了downsample。

后续将结合这一部分做知识蒸馏的讲解，请持续关注

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