用pytorch写一个Deeplab v3+网络，把其中的DCNN模块替换为金字塔卷积模块

好的，我可以提供一个基于 PyTorch 的 DeepLab v3 网络的代码框架，并将其中的 DCNN 模块替换为金字塔卷积模块。

首先，需要定义金字塔卷积模块。下面是一个示例代码：

import torch
import torch.nn as nn

class PyramidConv(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(PyramidConv, self).__init__()
        
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        
    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = torch.relu(x)
        
        x = self.conv2(x)
        x = self.bn2(x)
        x = torch.relu(x)
        
        return x

然后，在 DeepLab v3 网络中使用金字塔卷积模块：

import torch
import torch.nn as nn

class DeepLabV3(nn.Module):
    def __init__(self, num_classes=21):
        super(DeepLabV3, self).__init__()
        
        self.backbone = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            
            PyramidConv(128, 256, stride=2),
            PyramidConv(256, 512),
            PyramidConv(512, 1024),
        )
        
        self.fc = nn.Sequential(
            nn.Conv2d(1024, num_classes, kernel_size=1),
            nn.Upsample(scale_factor=8, mode='bilinear', align_corners=True)
        )
        
    def forward(self