写一个torch框架下输入（64，3，128，128）的带CBAM、BN层和dropout层ResNet五分类网络要求效率高性能好

以下是一个符合要求的ResNet-50网络：

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


class ConvBNReLU(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
        super(ConvBNReLU, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False)
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


class CBAM(nn.Module):
    def __init__(self, in_channels, reduction=16):
        super(CBAM, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)
        self.fc1 = nn.Conv2d(in_channels, in_channels // reduction, 1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(in_channels // reduction, in_channels, 1, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = self.fc2(self.relu(self.fc1(self.avg_pool(x))))
        max_out = self.fc2(self.relu(self.fc1(self.max_pool(x))))
        out = avg_out + max_out
        out = self.sigmoid(out)
        out = out * x
        return out


class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None, use_cbam=True):
        super(ResidualBlock, self).__init__()
        self.conv1 = ConvBNReLU(in_channels, out_channels, 3, stride, 1)
        self.conv2 = ConvBNReLU(out_channels, out_channels, 3, 1, 1)
        self.downsample = downsample
        if use_cbam:
            self.cbam = CBAM(out_channels)
        else:
            self.cbam = None

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.conv2(out)
        if self.cbam is not None:
            out = self.cbam(out)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        out = F.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=5, use_cbam=True, dropout_rate=0.5):
        super(ResNet, self).__init__()
        self.in_channels = 64
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, 7, 2, 3, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(3, 2, 1)
        )
        self.layer1 = self._make_layer(block, 64, layers[0], use_cbam)
        self.layer2 = self._make_layer(block, 128, layers[1], use_cbam, 2)
        self.layer3 = self._make_layer(block, 256, layers[2], use_cbam, 2)
        self.layer4 = self._make_layer(block, 512, layers[3], use_cbam, 2)
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.dropout = nn.Dropout(dropout_rate)
        self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, block, out_channels, blocks, use_cbam, stride=1):
        downsample = None
        if stride != 1 or self.in_channels != out_channels:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channels, out_channels, 1, stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )
        layers = [block(self.in_channels, out_channels, stride, downsample, use_cbam)]
        self.in_channels = out_channels
        for i in range(1, blocks):
            layers.append(block(out_channels, out_channels, use_cbam=use_cbam))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avg_pool(x)
        x = torch.flatten(x, 1)
        x = self.dropout(x)
        x = self.fc(x)
        return x


def resnet50_cbam(num_classes=5, dropout_rate=0.5):
    return ResNet(ResidualBlock, [3, 4, 6, 3], num_classes=num_classes, use_cbam=True, dropout_rate=dropout_rate)

该网络使用了CBAM和BN层，以及dropout层，可以在输入大小为（64，3，128，128）的图像上进行五分类任务，具有较高的效率和良好的性能。