从0开始复现unet

Unet是一种经典的卷积神经网络（CNN），特别适用于图像分割任务，如医学影像分析、遥感图像解析等。要从头开始复现Unet，你需要遵循以下步骤： 1. **导入必要的库**：首先安装Python的深度学习库如TensorFlow或PyTorch，并引入相关的模块。 ```python import torch from torch import nn import torchvision.models as models ``` 2. **定义基础网络结构**：Unet通常基于预训练的卷积模型（如VGG16、ResNet等）。你可以选择其中一个，然后去掉顶部的全连接层。 ```python def get_encoder(input_channels, base_model_name='vgg16'): encoder = getattr(models, base_model_name)(pretrained=True) for param in encoder.parameters(): param.requires_grad_(False) # 获取特征图列表 layers = list(encoder.children())[:-1] if base_model_name == 'vgg16' else list(encoder.children())[:-2] return nn.Sequential(*layers), list(layers)[-1].out_channels ``` 3. **设计编码器-解码器结构**：将预训练的前部分作为编码器，通过上采样（upsampling）和拼接操作连接到解码器部分，逐渐增加通道数并恢复分辨率。 ```python class UNetBlock(nn.Module): def __init__(self, in_channels, out_channels, upsample=True): super(UNetBlock, self).__init__() self.upsample = upsample and nn.Upsample(scale_factor=2) or None self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) self.norm = nn.BatchNorm2d(out_channels) self.activation = nn.ReLU() def forward(self, x1, x2=None): if x2 is not None: x = torch.cat([x2, self.upsample(x1)], dim=1) else: x = self.upsample(x1) x = self.conv(x) x = self.norm(x) x = self.activation(x) return x class UnetDecoder(nn.Module): def __init__(self, num_classes, in_channels, start_filters): super(UnetDecoder, self).__init__() self.decoder = nn.Sequential( UNetBlock(in_channels, start_filters * 2), UNetBlock(start_filters * 2, start_filters * 4), UNetBlock(start_filters * 4, start_filters * 8), nn.Conv2d(start_filters * 8, num_classes, kernel_size=1) ) def forward(self, x): return self.decoder(x) ``` 4. **构建完整的Unet模型**： ```python def build_unet(input_channels, num_classes): encoder_channels, last_layer_channels = get_encoder(input_channels) decoder = UnetDecoder(num_classes, last_layer_channels, start_filters=64) model = nn.Sequential() model.add_module('encoder', encoder_channels) model.add_module('decoder', decoder) return model ``` 5. **训练模型**：定义损失函数（如交叉熵损失）、优化器和训练循环。 ```python # ...省略训练过程... optimizer = torch.optim.Adam(model.parameters(), lr=0.001) criterion = nn.CrossEntropyLoss() # train loop for epoch in range(num_epochs): model.train() # 开启训练模式 # ...省略训练步骤... model.eval() # 开启验证模式 ```