yolov8中的Bottleneck结构

yolov8中的Bottleneck结构是指在卷积神经网络中使用的一种残差块结构，它由三个卷积层组成，其中第一个卷积层的卷积核大小为1x1，第二个卷积层的卷积核大小为3x3，第三个卷积层的卷积核大小为1x1。这种结构可以有效地减少模型的参数数量和计算量，同时提高模型的准确性和泛化能力。

在yolov8中，Bottleneck结构被广泛应用于Darknet53网络中，用于提取图像特征。通过堆叠多个Bottleneck结构，可以构建出更深的卷积神经网络，从而提高模型的性能。

代码示例：

import torch.nn as nn

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

    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.shape[1] != out.shape[1]:
            identity = nn.Conv2d(identity.shape[1], out.shape[1] * self.expansion, kernel_size=1, stride=self.stride, bias=False)(identity)
            identity = nn.BatchNorm2d(out.shape[1] * self.expansion)(identity)

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

        return out