地址：https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
贴代码
import torch.nn as nnimport torch.utils.model_zoo as model_zoo__all__ = [‘ResNet‘, ‘resnet18‘, ‘resnet34‘, ‘resnet50‘, ‘resnet101‘, ??????????‘resnet152‘]model_urls = { ???‘resnet18‘: ‘https://download.pytorch.org/models/resnet18-5c106cde.pth‘, ???‘resnet34‘: ‘https://download.pytorch.org/models/resnet34-333f7ec4.pth‘, ???‘resnet50‘: ‘https://download.pytorch.org/models/resnet50-19c8e357.pth‘, ???‘resnet101‘: ‘https://download.pytorch.org/models/resnet101-5d3b4d8f.pth‘, ???‘resnet152‘: ‘https://download.pytorch.org/models/resnet152-b121ed2d.pth‘,}def conv3x3(in_planes, out_planes, stride=1): ???"""3x3 convolution with padding""" ???return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, ????????????????????padding=1, bias=False)首先导入torch.nn，pytorch的网络模块多在此内，然后导入model_zoo，作用是根据下面的model_urls里的地址加载网络预训练权重。后面还对conv2d进行了一次封装，个人觉得有些多余。
class BasicBlock(nn.Module): ???expansion = 1 ???def __init__(self, inplanes, planes, stride=1, downsample=None): ???????super(BasicBlock, self).__init__() ???????self.conv1 = conv3x3(inplanes, planes, stride) ???????self.bn1 = nn.BatchNorm2d(planes) ???????self.relu = nn.ReLU(inplace=True) ???????self.conv2 = conv3x3(planes, planes) ???????self.bn2 = nn.BatchNorm2d(planes) ???????self.downsample = downsample ???????self.stride = stride ???def forward(self, x): ???????residual = 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: ???????????residual = self.downsample(x) ???????out += residual ???????out = self.relu(out) ???????return out
这里定义了最重要的残差模块，这个是基础版，由两个叠加的3x3卷积组成，与之相对应的bottleneck模块在下面定义
class Bottleneck(nn.Module): ???expansion = 4 ???def __init__(self, inplanes, planes, stride=1, downsample=None): ???????super(Bottleneck, self).__init__() ???????self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) ???????self.bn1 = nn.BatchNorm2d(planes) ???????self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, ??????????????????????????????padding=1, bias=False) ???????self.bn2 = nn.BatchNorm2d(planes) ???????self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) ???????self.bn3 = nn.BatchNorm2d(planes * self.expansion) ???????self.relu = nn.ReLU(inplace=True) ???????self.downsample = downsample ???????self.stride = stride ???def forward(self, x): ???????residual = 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) ???????if self.downsample is not None: ???????????residual = self.downsample(x) ???????out += residual ???????out = self.relu(out) ???????return out
与基础版的不同之处只在于这里是三个卷积，分别是1x1,3x3,1x1,分别用来压缩维度，卷积处理，恢复维度，这里我对inplane,plane,expansion的含义不甚明了，inplane是输入的通道数，plane是输出的通道数，expansion是什么，类似于wide resnet的宽度么？接着就是网络主体了。
class ResNet(nn.Module): ???def __init__(self, block, layers, num_classes=1000): ???????self.inplanes = 64 ???????super(ResNet, self).__init__() ???????self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, ??????????????????????????????bias=False) ???????self.bn1 = nn.BatchNorm2d(64) ???????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) ???????self.layer3 = self._make_layer(block, 256, layers[2], stride=2) ???????self.layer4 = self._make_layer(block, 512, layers[3], stride=2) ???????self.avgpool = nn.AvgPool2d(7, stride=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.init.constant_(m.weight, 1) ???????????????nn.init.constant_(m.bias, 0) ???def _make_layer(self, block, planes, blocks, stride=1): ???????downsample = None ???????if stride != 1 or self.inplanes != planes * block.expansion: ???????????downsample = nn.Sequential( ???????????????nn.Conv2d(self.inplanes, planes * block.expansion, ?????????????????????????kernel_size=1, stride=stride, bias=False), ???????????????nn.BatchNorm2d(planes * block.expansion), ???????????) ???????layers = [] ???????layers.append(block(self.inplanes, planes, stride, downsample)) ???????self.inplanes = planes * block.expansion ???????for i in range(1, blocks): ???????????layers.append(block(self.inplanes, planes)) ???????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 = x.view(x.size(0), -1) ???????x = self.fc(x) ???????return x
resnet共有五个阶段，其中第一阶段为一个7x7的卷积处理，stride为2，然后经过池化处理，此时特征图的尺寸已成为输入的1/4，接下来是四个阶段，也就是代码中的layer1,layer2,layer3,layer4。这里用make_layer函数产生四个layer，需要用户输入每个layer的block数目（即layers列表)以及采用的block类型（基础版还是bottleneck版）
接下来就是resnet18等几个模型的类定义
def resnet18(pretrained=False, **kwargs): ???"""Constructs a ResNet-18 model. ???Args: ???????pretrained (bool): If True, returns a model pre-trained on ImageNet ???""" ???model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) ???if pretrained: ???????model.load_state_dict(model_zoo.load_url(model_urls[‘resnet18‘])) ???return modeldef resnet34(pretrained=False, **kwargs): ???"""Constructs a ResNet-34 model. ???Args: ???????pretrained (bool): If True, returns a model pre-trained on ImageNet ???""" ???model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) ???if pretrained: ???????model.load_state_dict(model_zoo.load_url(model_urls[‘resnet34‘])) ???return modeldef resnet50(pretrained=False, **kwargs): ???"""Constructs a ResNet-50 model. ???Args: ???????pretrained (bool): If True, returns a model pre-trained on ImageNet ???""" ???model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) ???if pretrained: ???????model.load_state_dict(model_zoo.load_url(model_urls[‘resnet50‘])) ???return modeldef resnet101(pretrained=False, **kwargs): ???"""Constructs a ResNet-101 model. ???Args: ???????pretrained (bool): If True, returns a model pre-trained on ImageNet ???""" ???model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) ???if pretrained: ???????model.load_state_dict(model_zoo.load_url(model_urls[‘resnet101‘])) ???return modeldef resnet152(pretrained=False, **kwargs): ???"""Constructs a ResNet-152 model. ???Args: ???????pretrained (bool): If True, returns a model pre-trained on ImageNet ???""" ???model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) ???if pretrained: ???????model.load_state_dict(model_zoo.load_url(model_urls[‘resnet152‘])) ???return model
这里比较简单，就是调用上面ResNet对象，输入block类型和block数目，这里可以看到resnet18和resnet34用的是基础版block，因为此时网络还不深，不太需要考虑模型的效率，而当网络加深到52，101，152层时则有必要引入bottleneck结构，方便模型的存储和计算。另外是否加载预训练权重是可选的，具体就是调用model_zoo加载指定链接地址的序列化文件，反序列化为权重文件。
解读 pytorch对resnet的官方实现
原文地址：https://www.cnblogs.com/wzyuan/p/9880342.html