res18-unet代码示例

Res18-Unet是一种结合了残差网络（ResNet）和U-Net结构的深度学习模型，在图像分割任务中特别流行，尤其在医学影像分析领域。这个模型通常用于处理高分辨率的输入数据，并生成相应的掩码或分割结果。

以下是使用PyTorch框架的一个简单的Res18-Unet代码片段示例：

import torch.nn as nn
from torchvision.models.resnet import ResNet

# 定义基本的Unet块
class UNetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(UNetBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, stride=stride)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)

    def forward(self, x):
        residual = x
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)
        return self.relu(x + residual)  # 残差连接

# 使用预训练的ResNet18作为基础模块
class Res18UNet(nn.Module):
    def __init__(self, num_classes):
        super(Res18UNet, self).__init__()
        resnet = ResNet(ResNet.BasicBlock, [2, 2, 2, 2])
        self.encoder = nn.Sequential(*list(resnet.children())[:4])  # 获取前四个卷积块
        self.decoder = self._make_decoder(resnet.fc.in_features, num_classes)

    def _make_decoder(self, in_channels, num_classes):
        decoder_channels = [in_channels] + [64, 128, 256, 512]
        layers = []
        for i in range(len(decoder_channels) - 1):
            layers.extend([
                UNetBlock(decoder_channels[i], decoder_channels[i+1]),
                nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
            ])
        layers.append(nn.Conv2d(decoder_channels[-1], num_classes, kernel_size=1))
        return nn.Sequential(*layers)

    def forward(self, x):
        enc_outs = self.encoder(x)
        dec_outs = self.decoder(enc_outs[-1])
        return dec_outs

# 初始化模型并创建实例
model = Res18UNet(num_classes=2 if you_want_binary_segmentation else your_num_classes)