Unet复现

### UNet 模型的复现 #### PyTorch 实现 为了在 PyTorch 中构建和训练 UNet 模型，可以按照以下方式定义网络结构： ```python import torch.nn as nn import torch class DoubleConv(nn.Module): """(convolution => [BN] => ReLU) * 2""" def __init__(self, in_channels, out_channels): super().__init__() self.double_conv = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True) ) def forward(self, x): return self.double_conv(x) class UNet(nn.Module): def __init__(self, n_channels, n_classes): super(UNet, self).__init__() self.inc = DoubleConv(n_channels, 64) self.down1 = Down(64, 128) self.down2 = Down(128, 256) self.down3 = Down(256, 512) self.down4 = Down(512, 1024 // factor) self.up1 = Up(1024, 512 // factor) self.up2 = Up(512, 256 // factor) self.up3 = Up(256, 128 // factor) self.up4 = Up(128, 64) self.outc = OutConv(64, n_classes) def forward(self, x): x1 = self.inc(x) x2 = self.down1(x1) x3 = self.down2(x2) x4 = self.down3(x3) x5 = self.down4(x4) x = self.up1(x5, x4) x = self.up2(x, x3) x = self.up3(x, x2) x = self.up4(x, x1) logits = self.outc(x) return logits ``` 此代码片段展示了如何创建一个基本版本的 UNet 架构，在医学影像分析等领域广泛应用。 对于 TensorFlow 的实现，则有另一种风格的方法[^1]: #### TensorFlow 实现 下面是一个简单的基于 Keras API 的 UNet 定义: ```python from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dropout, concatenate, UpSampling2D def unet(input_size=(256, 256, 1)): inputs = Input(input_size) conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs) pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool1) pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) conv3 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool2) pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) conv4 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool3) drop4 = Dropout(0.5)(conv4) pool4 = MaxPooling2D(pool_size=(2, 2))(drop4) conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same')(pool4) drop5 = Dropout(0.5)(conv5) up6 = Conv2D(512, (2, 2), activation='relu', padding='same')(UpSampling2D(size=(2, 2))(drop5)) merge6 = concatenate([drop4,up6], axis=3) conv6 = Conv2D(512, (3, 3), activation='relu', padding='same')(merge6) up7 = Conv2D(256, (2, 2), activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv6)) merge7 = concatenate([conv3,up7], axis=3) conv7 = Conv2D(256, (3, 3), activation='relu', padding='same')(merge7) up8 = Conv2D(128, (2, 2), activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv7)) merge8 = concatenate([conv2,up8], axis=3) conv8 = Conv2D(128, (3, 3), activation='relu', padding='same')(merge8) up9 = Conv2D(64, (2, 2), activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv8)) merge9 = concatenate([conv1,up9], axis=3) conv9 = Conv2D(64, (3, 3), activation='relu', padding='same')(merge9) conv9 = Conv2D(2, (3, 3), activation='relu', padding='same')(conv9) conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9) model = Model(inputs=[inputs], outputs=[conv10]) return model ``` 这段代码同样实现了经典的 U 形编码解码器架构，并利用了跳跃连接来保留空间信息。