语义分割 代码复现

### 关于语义分割的代码实现 #### 使用PyTorch实现语义分割 以下是基于PyTorch的一个简单语义分割模型实现，该模型使用了U-Net架构作为基础。U-Net是一种经典的用于医学图像分割的网络结构，其编码器-解码器的设计非常适合语义分割任务。 ```python import torch import torch.nn as nn import torchvision.transforms as transforms from torch.utils.data import DataLoader, Dataset 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.up1 = Up(256, 128) self.up2 = 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) x = self.up1(x3, x2) x = self.up2(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): super().__init__() self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) 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 = nn.functional.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) ``` 上述代码实现了U-Net的核心模块，并定义了一个完整的U-Net模型[^5]。 --- #### 使用TensorFlow/Keras实现语义分割 以下是一个简单的基于Keras的FCN（Fully Convolutional Network）实现： ```python import tensorflow as tf from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dropout, concatenate, UpSampling2D from tensorflow.keras.models import Model def fcn_model(input_shape=(256, 256, 3), num_classes=2): inputs = Input(shape=input_shape) # Encoder (downsampling path) c1 = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs) p1 = MaxPooling2D((2, 2))(c1) c2 = Conv2D(128, (3, 3), activation='relu', padding='same')(p1) p2 = MaxPooling2D((2, 2))(c2) # Bottleneck layer b = Conv2D(256, (3, 3), activation='relu', padding='same')(p2) # Decoder (upsampling path) u1 = UpSampling2D((2, 2))(b) d1 = Conv2D(128, (3, 3), activation='relu', padding='same')(u1) m1 = concatenate([d1, c2]) u2 = UpSampling2D((2, 2))(m1) d2 = Conv2D(64, (3, 3), activation='relu', padding='same')(u2) m2 = concatenate([d2, c1]) outputs = Conv2D(num_classes, (1, 1), activation='softmax')(m2) model = Model(inputs=[inputs], outputs=[outputs]) return model model = fcn_model() model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) ``` 此代码展示了如何通过卷积层、池化层和上采样层构建一个基本的全卷积网络来完成语义分割任务[^6]。 --- #### 数据集预处理的重要性 在实际应用中，数据集的质量直接影响模型性能。如果数据集过小或分布不均，则可能导致训练过程中出现偏差。为了缓解这一问题，可以通过扩增技术增加样本数量并调整数据分布。此外，Dropout机制可以在一定程度上防止过拟合，从而提升测试阶段的表现[^4]。 --- #### 轻量化网络的应用 针对资源受限场景下的语义分割任务，可以采用轻量化网络设计策略。例如，Inception系列、SqueezeNet等都提供了有效的参数压缩方法；而深度可分离卷积则进一步降低了计算复杂度。这些技术能够显著减少模型大小而不明显牺牲精度[^2]。 ---