resnet50网络结构代码（resnet152网络结构）



（残差网络）是一种深度神经网络的结构，它通过残差块（Residual block）的堆叠来训练非常深的网络。下面是使用PyTorch

18的

示例：

 import torch.nn as nn import torch.nn.functional as F  class ResidualBlock(nn.Module): def __init__(self, in_channels, out_channels, stride=1): super(ResidualBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channels) self.shortcut = nn.Sequential() if stride != 1 or in_channels != out_channels: self.shortcut = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(out_channels) )  def forward(self, x): residual = x out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out += self.shortcut(residual) out = F.relu(out) return out   class ResNet (nn.Module): def __init__(self, block, num_blocks, num_classes=10): super( ResNet , self).__init__() self.in_channels = 64  self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(64) self.layer1 = self.make_layer(block, 64, num_blocks[0], stride=1) self.layer2 = self.make_layer(block, 128, num_blocks[1], stride=2) self.layer3 = self.make_layer(block, 256, num_blocks[2], stride=2) self.layer4 = self.make_layer(block, 512, num_blocks[3], stride=2) self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512, num_classes)  def make_layer(self, block, out_channels, num_blocks, stride): strides = [stride] + [1] * (num_blocks - 1) layers = [] for stride in strides: layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels return nn.Sequential(*layers)  def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = self.avg_pool(out) out = out.view(out.size(0), -1) out = self.fc(out) return out   def resnet 18(): return ResNet (ResidualBlock, [2, 2, 2, 2]) 

在上面的

中，我们首先定义了残差块`ResidualBlock`，然后定义了主要的

模型`

`。需要注意的是，`

`中包含了4个残差块的堆叠，每个残差块中又包含了2个卷积层，因此我们需要定义一个用于生成残差块的函数`make_layer`。最后，我们定义了一个`

18`函数，用于生成

18模型。

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