resnet18的bottleneck

ResNet18的bottleneck是指在ResNet18网络中使用的一种瓶颈结构。这种结构通过使用1x1的卷积核来降低输入通道数，然后再使用3x3的卷积核进行卷积操作，最后再使用1x1的卷积核将通道数恢复到原来的大小。这种设计可以减少参数量，提高网络的计算效率。

以下是一个示例代码，演示了如何在ResNet18中使用bottleneck结构：

import torch
import torch.nn as nn

class Bottleneck(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels//4, kernel_size=1, stride=stride, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels//4)
        self.conv2 = nn.Conv2d(out_channels//4, out_channels//4, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels//4)
        self.conv3 = nn.Conv2d(out_channels//4, out_channels, kernel_size=1, stride=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        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)

        if self.stride != 1 or identity.size(1) != out.size(1):
            identity = self.conv3(identity)
            identity = self.bn3(identity)

        out += identity
        out = self.relu(out)

        return out

class ResNet18(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet18, self).__init__()
        self.in_channels = 64

        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(Bottleneck, 64, 2, stride=1)
        self.layer2 = self._make_layer(Bottleneck, 128, 2, stride=2)
        self.layer3 = self._make_layer(Bottleneck, 256, 2, stride=2)
        self.layer4 = self._make_layer(Bottleneck, 512, 2, stride=2)

        self.avgpool = 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):
        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

model = ResNet18()
print(model)

这段代码演示了如何使用PyTorch构建一个ResNet18网络，并在其中使用了bottleneck结构。你可以通过运行这段代码来查看网络的结构和参数数量。