用class写一个ASSP空洞卷积python代码

以下是使用Python编写的ASSP空洞卷积的类：

import torch
import torch.nn as nn

class ASSPConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, dilation_rates=[1, 6, 12, 18], stride=1, padding=0, bias=False):
        super(ASSPConv, self).__init__()

        self.conv_1 = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=bias, dilation=dilation_rates[0])
        self.conv_2 = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=bias, dilation=dilation_rates[1])
        self.conv_3 = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=bias, dilation=dilation_rates[2])
        self.conv_4 = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=bias, dilation=dilation_rates[3])

        self.bn = nn.BatchNorm2d(out_channels * 4)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        out_1 = self.conv_1(x)
        out_2 = self.conv_2(x)
        out_3 = self.conv_3(x)
        out_4 = self.conv_4(x)

        out = torch.cat([out_1, out_2, out_3, out_4], dim=1)

        out = self.bn(out)
        out = self.relu(out)

        return out

该类接受四个参数：

- in_channels: 输入特征图的通道数
- out_channels: 输出特征图的通道数
- kernel_size: 卷积核的大小
- dilation_rates: 空洞卷积的膨胀率列表，默认为[1, 6, 12, 18]
- stride: 卷积的步长，默认为1
- padding: 卷积的填充大小，默认为0
- bias: 是否使用偏置，默认为False

该类包含四个不同大小的空洞卷积层，每个卷积层的膨胀率不同。然后将四个卷积层的输出串联起来，再使用批归一化和ReLU激活函数。最后返回特征图作为输出。