Deeplabv3+中ASPP模块用1×3卷积和3×1卷积代替3×3卷积完整pytorch代码

以下是使用1x3和3x1卷积代替3x3卷积的Deeplabv3+中ASPP模块的完整PyTorch代码：

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

class ASPP(nn.Module):
    def __init__(self, in_channels, out_channels=256, rates=[1, 6, 12, 18]):
        super(ASPP, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.conv2 = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.conv3a = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.conv3b = nn.Conv2d(out_channels, out_channels, kernel_size=(1, 3), padding=(0, 1))
        self.conv3c = nn.Conv2d(out_channels, out_channels, kernel_size=(3, 1), padding=(1, 0))
        self.conv4a = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.conv4b = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=rates[0], padding=rates[0])
        self.conv5a = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.conv5b = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=rates[1], padding=rates[1])
        self.conv6a = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.conv6b = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=rates[2], padding=rates[2])
        self.conv7a = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.conv7b = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=rates[3], padding=rates[3])
        self.conv8 = nn.Conv2d(out_channels * 5, out_channels, kernel_size=1)

    def forward(self, x):
        x1 = self.conv1(x)
        x1 = F.relu(x1)

        x2 = F.avg_pool2d(x, kernel_size=x.size()[2:])
        x2 = self.conv2(x2)
        x2 = F.relu(x2)

        x3 = self.conv3a(x)
        x3 = self.conv3b(x3)
        x3 = self.conv3c(x3)
        x3 = F.relu(x3)

        x4 = self.conv4a(x)
        x4 = self.conv4b(x4)
        x4 = F.relu(x4)

        x5 = self.conv5a(x)
        x5 = self.conv5b(x5)
        x5 = F.relu(x5)

        x6 = self.conv6a(x)
        x6 = self.conv6b(x6)
        x6 = F.relu(x6)

        x7 = self.conv7a(x)
        x7 = self.conv7b(x7)
        x7 = F.relu(x7)

        out = torch.cat((x1, x2, x3, x4, x5, x6, x7), dim=1)
        out = self.conv8(out)
        out = F.relu(out)

        return out