1. Toy模板网首页
  2. > Toy博客

ResNet代码复现＋超详细注释（PyTorch）

9月前 作者：路人贾'ω' 分类：Toy博客 阅读(42) 违法举报

这篇具有很好参考价值的文章主要介绍了ResNet代码复现＋超详细注释（PyTorch）。希望对大家有所帮助。如果存在错误或未考虑完全的地方，请大家不吝赐教，您也可以点击"举报违法"按钮提交疑问。

关于ResNet的原理和具体细节，可参见上篇解读：经典神经网络论文超详细解读（五）——ResNet（残差网络）学习笔记（翻译＋精读＋代码复现）

接下来我们就来复现一下代码。

源代码比较复杂，感兴趣的同学可以上官网学习： 

https://github.com/pytorch/vision/tree/master/torchvision

本篇是简化版本 

 一、BasicBlock模块

BasicBlock结构图如图所示：

ResNet代码复现＋超详细注释（PyTorch）

 BasicBlock是基础版本，主要用来构建ResNet18和ResNet34网络，里面只包含两个卷积层，使用了两个 3*3 的卷积，通道数都是64，卷积后接着 BN 和 ReLU。

右边的曲线就是Shortcut Connections，将输入x加到输出。

代码：

'''-------------一、BasicBlock模块-----------------------------'''
# 用于ResNet18和ResNet34基本残差结构块
class BasicBlock(nn.Module):
    def __init__(self, inchannel, outchannel, stride=1):
        super(BasicBlock, self).__init__()
        self.left = nn.Sequential(
            nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(outchannel),
            nn.ReLU(inplace=True), #inplace=True表示进行原地操作，一般默认为False，表示新建一个变量存储操作
            nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(outchannel)
        )
        self.shortcut = nn.Sequential()
        #论文中模型架构的虚线部分，需要下采样
        if stride != 1 or inchannel != outchannel:
            self.shortcut = nn.Sequential(
                nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(outchannel)
            )

    def forward(self, x):
        out = self.left(x) #这是由于残差块需要保留原始输入
        out += self.shortcut(x)#这是ResNet的核心，在输出上叠加了输入x
        out = F.relu(out)
        return out

二、Bottleneck 模块

Bottleneck结构图如图所示：

ResNet代码复现＋超详细注释（PyTorch）

Bottleneck主要用在ResNet50及以上的网络结构，与BasicBlock不同的是这里有 3 个卷积，分别为 1*1，3*3，1*1大小的卷积核，分别用于压缩维度、卷积处理、恢复维度。
这里的通道数是变化的，1*1卷积层的作用就是用于改变特征图的通数，使得可以和恒等映射x相叠加，另外这里的1*1卷积层改变维度的很重要的一点是可以降低网络参数量，这也是为什么更深层的网络采用BottleNeck而不是BasicBlock的原因。

注意：这里outchannel / 4是因为Bottleneck层输出通道都是输入的4倍

代码：

'''-------------二、Bottleneck模块-----------------------------'''
# 用于ResNet50及以上的残差结构块
class Bottleneck(nn.Module):
    def __init__(self, inchannel, outchannel, stride=1):
        super(Bottleneck, self).__init__()
        self.left = nn.Sequential(
            nn.Conv2d(inchannel, int(outchannel / 4), kernel_size=1, stride=stride, padding=0, bias=False),
            nn.BatchNorm2d(int(outchannel / 4)),
            nn.ReLU(inplace=True),
            nn.Conv2d(int(outchannel / 4), int(outchannel / 4), kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(int(outchannel / 4)),
            nn.ReLU(inplace=True),
            nn.Conv2d(int(outchannel / 4), outchannel, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(outchannel),
        )
        self.shortcut = nn.Sequential()
        if stride != 1 or inchannel != outchannel:
            self.shortcut = nn.Sequential(
                nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(outchannel)
            )

    def forward(self, x):
        out = self.left(x)
        y = self.shortcut(x)
        out += self.shortcut(x)
        out = F.relu(out)
        return out

三、ResNet主体

介绍了上述BasicBlock基础块和BotteNeck结构后，我们就可以搭建ResNet结构了。

5种不同层数的ResNet结构图如图所示：

ResNet代码复现＋超详细注释（PyTorch）

代码： 

ResNet18

'''----------ResNet18----------'''
class ResNet_18(nn.Module):
    def __init__(self, ResidualBlock, num_classes=10):
        super(ResNet_18, self).__init__()
        self.inchannel = 64
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(),
        )
        self.layer1 = self.make_layer(ResidualBlock, 64, 2, stride=1)
        self.layer2 = self.make_layer(ResidualBlock, 128, 2, stride=2)
        self.layer3 = self.make_layer(ResidualBlock, 256, 2, stride=2)
        self.layer4 = self.make_layer(ResidualBlock, 512, 2, stride=2)
        self.fc = nn.Linear(512, num_classes)

    def make_layer(self, block, channels, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)  # strides=[1,1]
        layers = []
        for stride in strides:
            layers.append(block(self.inchannel, channels, stride))
            self.inchannel = channels
        return nn.Sequential(*layers)

    def forward(self, x):  # 3*32*32
        out = self.conv1(x)  # 64*32*32
        out = self.layer1(out)  # 64*32*32
        out = self.layer2(out)  # 128*16*16
        out = self.layer3(out)  # 256*8*8
        out = self.layer4(out)  # 512*4*4
        out = F.avg_pool2d(out, 4)  # 512*1*1
        out = out.view(out.size(0), -1)  # 512
        out = self.fc(out)
        return out

ResNet34

'''----------ResNet34----------'''
class ResNet_34(nn.Module):
    def __init__(self, ResidualBlock, num_classes=10):
        super(ResNet_34, self).__init__()
        self.inchannel = 64
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(),
        )
        self.layer1 = self.make_layer(ResidualBlock, 64, 3, stride=1)
        self.layer2 = self.make_layer(ResidualBlock, 128, 4, stride=2)
        self.layer3 = self.make_layer(ResidualBlock, 256, 6, stride=2)
        self.layer4 = self.make_layer(ResidualBlock, 512, 3, stride=2)
        self.fc = nn.Linear(512, num_classes)

    def make_layer(self, block, channels, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)  # strides=[1,1]
        layers = []
        for stride in strides:
            layers.append(block(self.inchannel, channels, stride))
            self.inchannel = channels
        return nn.Sequential(*layers)

    def forward(self, x):  # 3*32*32
        out = self.conv1(x)  # 64*32*32
        out = self.layer1(out)  # 64*32*32
        out = self.layer2(out)  # 128*16*16
        out = self.layer3(out)  # 256*8*8
        out = self.layer4(out)  # 512*4*4
        out = F.avg_pool2d(out, 4)  # 512*1*1
        out = out.view(out.size(0), -1)  # 512
        out = self.fc(out)
        return out

ResNet50

'''---------ResNet50--------'''
class ResNet_50(nn.Module):
    def __init__(self, ResidualBlock, num_classes=10):
        super(ResNet_50, self).__init__()
        self.inchannel = 64
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(),
        )
        self.layer1 = self.make_layer(ResidualBlock, 256, 3, stride=1)
        self.layer2 = self.make_layer(ResidualBlock, 512, 4, stride=2)
        self.layer3 = self.make_layer(ResidualBlock, 1024, 6, stride=2)
        self.layer4 = self.make_layer(ResidualBlock, 2048, 3, stride=2)
        self.fc = nn.Linear(512 * 4, num_classes)
        # **************************

    def make_layer(self, block, channels, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)  # strides=[1,1]
        layers = []
        for stride in strides:
            layers.append(block(self.inchannel, channels, stride))
            self.inchannel = channels
        return nn.Sequential(*layers)

    def forward(self, x):  # 3*32*32
        out = self.conv1(x)  # 64*32*32
        out = self.layer1(out)  # 64*32*32
        out = self.layer2(out)  # 128*16*16
        out = self.layer3(out)  # 256*8*8
        out = self.layer4(out)  # 512*4*4
        out = F.avg_pool2d(out, 4)  # 512*1*1
        # print(out.size())
        out = out.view(out.size(0), -1)  # 512
        out = self.fc(out)
        return out

四、完整代码

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


'''-------------一、BasicBlock模块-----------------------------'''
# 用于ResNet18和ResNet34基本残差结构块
class BasicBlock(nn.Module):
    def __init__(self, inchannel, outchannel, stride=1):
        super(BasicBlock, self).__init__()
        self.left = nn.Sequential(
            nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(outchannel),
            nn.ReLU(inplace=True), #inplace=True表示进行原地操作，一般默认为False，表示新建一个变量存储操作
            nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(outchannel)
        )
        self.shortcut = nn.Sequential()
        #论文中模型架构的虚线部分，需要下采样
        if stride != 1 or inchannel != outchannel:
            self.shortcut = nn.Sequential(
                nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(outchannel)
            )

    def forward(self, x):
        out = self.left(x) #这是由于残差块需要保留原始输入
        out += self.shortcut(x)#这是ResNet的核心，在输出上叠加了输入x
        out = F.relu(out)
        return out


'''-------------二、Bottleneck模块-----------------------------'''
# 用于ResNet50及以上的残差结构块
class Bottleneck(nn.Module):
    def __init__(self, inchannel, outchannel, stride=1):
        super(Bottleneck, self).__init__()
        self.left = nn.Sequential(
            nn.Conv2d(inchannel, int(outchannel / 4), kernel_size=1, stride=stride, padding=0, bias=False),
            nn.BatchNorm2d(int(outchannel / 4)),
            nn.ReLU(inplace=True),
            nn.Conv2d(int(outchannel / 4), int(outchannel / 4), kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(int(outchannel / 4)),
            nn.ReLU(inplace=True),
            nn.Conv2d(int(outchannel / 4), outchannel, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(outchannel),
        )
        self.shortcut = nn.Sequential()
        if stride != 1 or inchannel != outchannel:
            self.shortcut = nn.Sequential(
                nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(outchannel)
            )

    def forward(self, x):
        out = self.left(x)
        y = self.shortcut(x)
        out += self.shortcut(x)
        out = F.relu(out)
        return out

'''-------------ResNet18---------------'''
class ResNet_18(nn.Module):
    def __init__(self, ResidualBlock, num_classes=10):
        super(ResNet_18, self).__init__()
        self.inchannel = 64
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(),
        )
        self.layer1 = self.make_layer(ResidualBlock, 64, 2, stride=1)
        self.layer2 = self.make_layer(ResidualBlock, 128, 2, stride=2)
        self.layer3 = self.make_layer(ResidualBlock, 256, 2, stride=2)
        self.layer4 = self.make_layer(ResidualBlock, 512, 2, stride=2)
        self.fc = nn.Linear(512, num_classes)

    def make_layer(self, block, channels, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)  # strides=[1,1]
        layers = []
        for stride in strides:
            layers.append(block(self.inchannel, channels, stride))
            self.inchannel = channels
        return nn.Sequential(*layers)

    def forward(self, x):  # 3*32*32
        out = self.conv1(x)  # 64*32*32
        out = self.layer1(out)  # 64*32*32
        out = self.layer2(out)  # 128*16*16
        out = self.layer3(out)  # 256*8*8
        out = self.layer4(out)  # 512*4*4
        out = F.avg_pool2d(out, 4)  # 512*1*1
        out = out.view(out.size(0), -1)  # 512
        out = self.fc(out)
        return out

'''-------------ResNet34---------------'''
class ResNet_34(nn.Module):
    def __init__(self, ResidualBlock, num_classes=10):
        super(ResNet_34, self).__init__()
        self.inchannel = 64
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(),
        )
        self.layer1 = self.make_layer(ResidualBlock, 64, 3, stride=1)
        self.layer2 = self.make_layer(ResidualBlock, 128, 4, stride=2)
        self.layer3 = self.make_layer(ResidualBlock, 256, 6, stride=2)
        self.layer4 = self.make_layer(ResidualBlock, 512, 3, stride=2)
        self.fc = nn.Linear(512, num_classes)

    def make_layer(self, block, channels, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)  # strides=[1,1]
        layers = []
        for stride in strides:
            layers.append(block(self.inchannel, channels, stride))
            self.inchannel = channels
        return nn.Sequential(*layers)

    def forward(self, x):  # 3*32*32
        out = self.conv1(x)  # 64*32*32
        out = self.layer1(out)  # 64*32*32
        out = self.layer2(out)  # 128*16*16
        out = self.layer3(out)  # 256*8*8
        out = self.layer4(out)  # 512*4*4
        out = F.avg_pool2d(out, 4)  # 512*1*1
        out = out.view(out.size(0), -1)  # 512
        out = self.fc(out)
        return out

'''-------------ResNet50---------------'''
class ResNet_50(nn.Module):
    def __init__(self, ResidualBlock, num_classes=10):
        super(ResNet_50, self).__init__()
        self.inchannel = 64
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(),
        )
        self.layer1 = self.make_layer(ResidualBlock, 256, 3, stride=1)
        self.layer2 = self.make_layer(ResidualBlock, 512, 4, stride=2)
        self.layer3 = self.make_layer(ResidualBlock, 1024, 6, stride=2)
        self.layer4 = self.make_layer(ResidualBlock, 2048, 3, stride=2)
        self.fc = nn.Linear(512 * 4, num_classes)
        # **************************

    def make_layer(self, block, channels, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)  # strides=[1,1]
        layers = []
        for stride in strides:
            layers.append(block(self.inchannel, channels, stride))
            self.inchannel = channels
        return nn.Sequential(*layers)

    def forward(self, x):  # 3*32*32
        out = self.conv1(x)  # 64*32*32
        out = self.layer1(out)  # 64*32*32
        out = self.layer2(out)  # 128*16*16
        out = self.layer3(out)  # 256*8*8
        out = self.layer4(out)  # 512*4*4
        out = F.avg_pool2d(out, 4)  # 512*1*1
        # print(out.size())
        out = out.view(out.size(0), -1)  # 512
        out = self.fc(out)
        return out


def ResNet18():
    return ResNet_18(BasicBlock)


def ResNet34():
    return ResNet_34(BasicBlock)


def ResNet50():
    return ResNet_50(Bottleneck)

本篇到这里就结束啦，有什么问题，欢迎大家留言讨论~文章来源地址https://www.toymoban.com/news/detail-500118.html

到了这里，关于ResNet代码复现＋超详细注释（PyTorch）的文章就介绍完了。如果您还想了解更多内容，请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章，希望大家以后多多支持TOY模板网！

本文来自互联网用户投稿，该文观点仅代表作者本人，不代表本站立场。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如若转载，请注明出处： 如若内容造成侵权/违法违规/事实不符，请点击违法举报进行投诉反馈，一经查实，立即删除！

分享到：
领支付宝红包 赞助服务器费用

相关文章

觉得文章有用就打赏一下文章作者

支付宝扫一扫打赏

博客赞助

微信扫一扫打赏

请作者喝杯咖啡吧~博客赞助

支付宝扫一扫领取红包，优惠每天领

二维码1

领取红包

二维码2

领红包