给我一个bisenetv2 pytorcfh代码用来图像分割的案例

以下是一个基于 PyTorch 的 BiSeNetv2 图像分割案例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class ConvBlock(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1, groups=1, bias=False):
        super(ConvBlock, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
    
    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x

class SpatialPath(nn.Module):
    def __init__(self):
        super(SpatialPath, self).__init__()
        self.conv1 = ConvBlock(3, 64, kernel_size=3, stride=2, padding=1)
        self.conv2 = ConvBlock(64, 128, kernel_size=3, stride=2, padding=1)
        self.conv3 = ConvBlock(128, 256, kernel_size=3, stride=2, padding=1)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        return x

class AttentionRefinement(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(AttentionRefinement, self).__init__()
        self.conv = ConvBlock(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
        self.sigmoid = nn.Sigmoid()
    
    def forward(self, x):
        y = F.adaptive_avg_pool2d(x, output_size=(1,1))
        y = self.conv(y)
        y = self.sigmoid(y)
        return x * y

class ContextPath(nn.Module):
    def __init__(self):
        super(ContextPath, self).__init__()
        self.conv1 = ConvBlock(3, 64, kernel_size=3, stride=2, padding=1)
        self.conv2 = ConvBlock(64, 64, kernel_size=3, stride=1, padding=1)
        self.conv3 = ConvBlock(64, 128, kernel_size=3, stride=2, padding=1)
        self.conv4 = ConvBlock(128, 128, kernel_size=3, stride=1, padding=1, dilation=2)
        self.conv5 = ConvBlock(128, 128, kernel_size=3, stride=1, padding=1, dilation=4)
        self.conv6 = ConvBlock(128, 128, kernel_size=3, stride=1, padding=1, dilation=8)
        self.conv7 = ConvBlock(128, 128, kernel_size=3, stride=1, padding=1, dilation=16)
        self.conv8 = ConvBlock(128, 256, kernel_size=1, stride=1, padding=0)
        self.arm = AttentionRefinementModule(256, 256)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.conv5(x)
        x = self.conv6(x)
        x = self.conv7(x)
        x = self.conv8(x)
        x = self.arm(x)
        return x

class AttentionRefinementModule(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(AttentionRefinementModule, self).__init__()
        self.conv = ConvBlock(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
        self.arm = AttentionRefinement(in_channels, out_channels)
    
    def forward(self, x):
        x = self.conv(x)
        x = self.arm(x)
        return x

class FeatureFusionModule(nn.Module):
    def __init__(self, in_channels, out_channels, reduction=1):
        super(FeatureFusionModule, self).__init__()
        self.conv1 = ConvBlock(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
        self.conv2 = ConvBlock(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.conv3 = ConvBlock(out_channels, out_channels, kernel_size=1, stride=1, padding=0)
        self.arm = AttentionRefinement(out_channels, out_channels//reduction)
    
    def forward(self, x1, x2):
        x = torch.cat([x1, x2], dim=1)
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.arm(x)
        x = x1 + x
        return x

class BiSeNetv2(nn.Module):
    def __init__(self, num_classes):
        super(BiSeNetv2, self).__init__()
        self.sp = SpatialPath()
        self.cp = ContextPath()
        self.ffm = FeatureFusionModule(256, 256, reduction=4)
        self.conv = ConvBlock(256, num_classes, kernel_size=1, stride=1, padding=0)
        self.softmax = nn.Softmax(dim=1)
    
    def forward(self, x):
        sp = self.sp(x)
        cp = self.cp(x)
        x = self.ffm(sp, cp)
        x = self.conv(x)
        x = self.softmax(x)
        return x

使用该模型进行图像分割需要进行以下步骤：

1. 定义模型

model = BiSeNetv2(num_classes)

2. 定义损失函数

criterion = nn.CrossEntropyLoss()

3. 定义优化器

optimizer = torch.optim.Adam(model.parameters(), lr=lr)

4. 训练模型

for epoch in range(num_epochs):
    for inputs, labels in dataloader:
        inputs, labels = inputs.cuda(), labels.cuda()
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

其中，`dataloader` 是用于加载训练数据的 PyTorch 数据加载器，`num_epochs` 是训练轮数，`lr` 是学习率。

5. 对测试集进行预测

model.eval()
with torch.no_grad():
    for inputs, labels in test_dataloader:
        inputs, labels = inputs.cuda(), labels.cuda()
        outputs = model(inputs)
        preds = torch.argmax(outputs, dim=1)

其中，`test_dataloader` 是用于加载测试数据的 PyTorch 数据加载器。`preds` 是预测的分割结果。