如何在PSPNet的SPP模块最末端加入条件随机场，请给出详细代码

PSPNet中的SPP模块最末端是全连接层，可以在全连接层后面加入条件随机场（CRF）层，以获得更好的语义分割结果。下面是一个示例代码，展示了如何在PSPNet的SPP模块最末端加入CRF：

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

class PSPNet(nn.Module):
    def __init__(self, n_classes=21, sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=1024):
        super(PSPNet, self).__init__()

        # some layers defined here

        # SPP module
        self.spp = SpatialPyramidPooling(sizes=sizes, in_channels=deep_features_size)

        # classifier
        self.fc1 = nn.Conv2d(int(deep_features_size * len(sizes)), psp_size, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(psp_size)
        self.fc2 = nn.Conv2d(psp_size, n_classes, kernel_size=1)

        # CRF layer
        self.crf = CRF(n_classes)

    def forward(self, x):
        # some forward operations defined here

        # SPP module
        x = self.spp(x)

        # classifier
        x = self.fc1(x)
        x = self.bn1(x)
        x = F.relu(x)
        x = F.dropout(x, p=0.5, training=self.training)
        x = self.fc2(x)
        
        # CRF layer
        x = self.crf(x)

        return x


class CRF(nn.Module):
    def __init__(self, num_classes):
        super(CRF, self).__init__()
        self.num_classes = num_classes
        self.crf = DenseCRF(21, 21, 5)

    def forward(self, x):
        # CRF inference
        x = self.crf(x)

        return x

在这个示例代码中，我们把CRF层定义为一个单独的nn.Module，并在PSPNet的forward()函数中调用它。CRF层使用了一个DenseCRF库，它需要安装并导入才能使用。