将deeplabv3plus以xception网络为核心的pytorch代码

以下是基于PyTorch的DeepLabv3+模型，使用Xception网络作为backbone的示例代码：

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

class ASPP(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ASPP, self).__init__()
        # ASPP module with rates of 6, 12, 18 and 24
        self.conv1x1_1 = nn.Conv2d(in_channels, out_channels, 1)
        self.conv3x3_1 = nn.Conv2d(in_channels, out_channels, 3, padding=6, dilation=6)
        self.conv3x3_2 = nn.Conv2d(in_channels, out_channels, 3, padding=12, dilation=12)
        self.conv3x3_3 = nn.Conv2d(in_channels, out_channels, 3, padding=18, dilation=18)
        self.pool = nn.AdaptiveAvgPool2d(1)
        self.conv1x1_2 = nn.Conv2d(in_channels, out_channels, 1)
        self.conv1x1_3 = nn.Conv2d(out_channels * 5, out_channels, 1)
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU()

    def forward(self, x):
        x1 = self.conv1x1_1(x)
        x2 = self.conv3x3_1(x)
        x3 = self.conv3x3_2(x)
        x4 = self.conv3x3_3(x)
        x5 = self.pool(x)
        x5 = self.conv1x1_2(x5)
        x5 = F.interpolate(x5, size=x4.size()[2:], mode='bilinear', align_corners=True)
        x = torch.cat([x1, x2, x3, x4, x5], dim=1)
        x = self.conv1x1_3(x)
        x = self.bn(x)
        x = self.relu(x)
        return x

class DeepLabv3plus(nn.Module):
    def __init__(self, num_classes=21):
        super(DeepLabv3plus, self).__init__()
        # Xception backbone
        self.conv1 = nn.Conv2d(3, 32, 3, stride=2, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padding=1)
        self.block1 = nn.Sequential(
            nn.Conv2d(64, 128, 1, stride=2, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Conv2d(128, 128, 3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Conv2d(128, 128, 1, stride=1, bias=False),
            nn.BatchNorm2d(128),
        )
        self.block2 = nn.Sequential(
            nn.ReLU(),
            nn.Conv2d(128, 256, 1, stride=2, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Conv2d(256, 256, 3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Conv2d(256, 256, 1, stride=1, bias=False),
            nn.BatchNorm2d(256),
        )
        self.block3 = nn.Sequential(
            nn.ReLU(),
            nn.Conv2d(256, 728, 1, stride=2, bias=False),
            nn.BatchNorm2d(728),
            nn.ReLU(),
            nn.Conv2d(728, 728, 3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(728),
            nn.ReLU(),
            nn.Conv2d(728, 728, 1, stride=1, bias=False),
            nn.BatchNorm2d(728),
        )
        self.block4 = nn.Sequential(
            nn.ReLU(),
            nn.Conv2d(728, 728, 3, stride=1, padding=1, bias=False, dilation=2),
            nn.BatchNorm2d(728),
            nn.ReLU(),
            nn.Conv2d(728, 728, 3, stride=1, padding=1, bias=False, dilation=2),
            nn.BatchNorm2d(728),
            nn.ReLU(),
            nn.Conv2d(728, 728, 3, stride=1, padding=1, bias=False, dilation=2),
            nn.BatchNorm2d(728),
        )
        self.block5 = nn.Sequential(
            nn.ReLU(),
            nn.Conv2d(728, 728, 3, stride=1, padding=2, bias=False, dilation=4),
            nn.BatchNorm2d(728),
            nn.ReLU(),
            nn.Conv2d(728, 728, 3, stride=1, padding=2, bias=False, dilation=4),
            nn.BatchNorm2d(728),
            nn.ReLU(),
            nn.Conv2d(728, 728, 3, stride=1, padding=2, bias=False, dilation=4),
            nn.BatchNorm2d(728),
        )
        self.block6 = nn.Sequential(
            nn.ReLU(),
            nn.Conv2d(728, 728, 3, stride=1, padding=2, bias=False, dilation=4),
            nn.BatchNorm2d(728),
            nn.ReLU(),
            nn.Conv2d(728, 728, 3, stride=1, padding=2, bias=False, dilation=4),
            nn.BatchNorm2d(728),
            nn.ReLU(),
            nn.Conv2d(728, 728, 3, stride=1, padding=2, bias=False, dilation=4),
            nn.BatchNorm2d(728),
        )
        self.block7 = nn.Sequential(
            nn.ReLU(),
            nn.Conv2d(728, 728, 3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(728),
            nn.ReLU(),
            nn.Conv2d(728, 1024, 1, stride=1, bias=False),
            nn.BatchNorm2d(1024),
        )
        self.aspp = ASPP(1024, 256)
        self.conv1x1 = nn.Conv2d(1280, 256, 1)
        self.bn1 = nn.BatchNorm2d(256)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(0.5)
        self.conv3x3 = nn.Conv2d(256, 256, 3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(256)
        self.conv3x3_2 = nn.Conv2d(256, num_classes, 1)

    def forward(self, x):
        h = self.conv1(x)
        h = self.conv2(h)
        h = self.block1(h) + h
        h = self.block2(h) + h
        h = self.block3(h) + h
        h = self.block4(h) + h
        h = self.block5(h) + h
        h = self.block6(h) + h
        h = self.block7(h)
        h = self.aspp(h)
        h = self.conv1x1(h)
        h = self.bn1(h)
        h = self.relu(h)
        h = self.dropout(h)
        h = self.conv3x3(h)
        h = self.bn2(h)
        h = self.relu(h)
        h = self.conv3x3_2(h)
        h = F.interpolate(h, size=x.shape[2:], mode='bilinear', align_corners=True)
        return h

其中，ASPP模块实现了带有不同空洞率的卷积，以捕捉不同尺度的像素信息。DeepLabv3plus模型中使用了7个Xception模块和ASPP模块。最后使用1x1卷积层和双线性插值进行输出。