unet实战pytorch

### UNet 实战 PyTorch 操作教程 #### 数据集下载与准备 为了构建基于PyTorch的UNet模型来执行特定任务，比如道路裂纹检测，首先需要获取合适的数据集并做好预处理工作。这通常涉及从公开资源或者自有渠道获得标注好的图片数据，并将其划分为训练集、验证集以及测试集[^1]。 #### 构建UNet架构 UNet是一种经典的卷积神经网络结构，特别适用于医学影像分析中的像素级分类问题。该网络由收缩路径（Contracting Path）和扩展路径（Expansive Path）组成。前者负责捕捉上下文信息；后者则专注于精确定位特征。在PyTorch中实现时，可以利用`nn.Module`类来自定义每一层的操作，包括但不限于使用`Conv2d()`进行下采样操作，而上采样的部分可以通过调用`ConvTranspose2d()`完成反卷积过程[^3]。 ```python import torch.nn as nn 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__() bilinear = True factor = 2 if bilinear else 1 self.inc = DoubleConv(n_channels, 64) self.down1 = Down(64, 128) self.down2 = Down(128, 256) self.down3 = Down(256, 512 // factor) self.up1 = Up(512, 256 // factor, bilinear) self.up2 = Up(256, 128 // factor, bilinear) self.up3 = Up(128, 64, bilinear) 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) x = self.up1(x4, x3) x = self.up2(x, x2) x = self.up3(x, x1) logits = self.outc(x) return logits class Down(nn.Module): """Downscaling with maxpool then double conv""" def __init__(self, in_channels, out_channels): super().__init__() self.maxpool_conv = nn.Sequential( nn.MaxPool2d(2), DoubleConv(in_channels, out_channels) ) def forward(self, x): return self.maxpool_conv(x) class Up(nn.Module): """Upscaling then double conv""" def __init__(self, in_channels, out_channels, bilinear=True): super().__init__() # if bilinear, use the normal convolutions to reduce the number of channels if bilinear: self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) self.conv = DoubleConv(in_channels, out_channels, in_channels // 2) else: self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2) self.conv = DoubleConv(in_channels, out_channels) def forward(self, x1, x2): x1 = self.up(x1) diffY = x2.size()[2] - x1.size()[2] diffX = x2.size()[3] - x1.size()[3] x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2]) x = torch.cat([x2, x1], dim=1) return self.conv(x) class OutConv(nn.Module): def __init__(self, in_channels, out_channels): super(OutConv, self).__init__() self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) def forward(self, x): return self.conv(x) ``` 上述代码展示了如何创建一个基础版本的UNet模型。此模型包含了编码器（即向下采样阶段）、解码器（向上采样阶段），并通过跳跃连接将两者联系起来，从而保留更多细节信息以便于后续的任务处理。 #### 训练流程设置 当完成了模型的设计之后，下一步就是配置好损失函数、优化算法以及其他必要的超参数设定。对于二元分割任务而言，常用的损失函数有交叉熵损失(`CrossEntropyLoss`)或是Dice系数损失等。至于优化方法，则可以选择Adam或SGD这样的梯度下降变体来进行权重更新。 #### 测试评估环节 最后一步是对已经训练完毕后的模型性能进行全面评测。一般会采用交并比(IoU)，也称为Jaccard指数作为衡量标准之一。除此之外还可以考虑其他指标如精确率(Precision), 召回率(Recall)等等，具体取决于应用场景的需求。