PyTorch实现：增加ResUNet网络层数

本文将介绍如何将ResUNet网络的过滤器数量扩展为[64, 128, 256, 512, 1024]，并提供完整的PyTorch代码实现。

修改后的ResUNet网络

以下是修改后的ResUNet网络代码：

import torch
import torch.nn as nn

class ResidualConv(nn.Module):
    def __init__(self, input_dim, output_dim, stride, padding):
        super(ResidualConv, self).__init__()

        self.conv_block = nn.Sequential(
            nn.BatchNorm2d(input_dim),
            nn.ReLU(),
            nn.Conv2d(input_dim, output_dim, kernel_size=3, stride=stride, padding=padding),
            nn.BatchNorm2d(output_dim),
            nn.ReLU(),
            nn.Conv2d(output_dim, output_dim, kernel_size=3, padding=1),
        )
        self.conv_skip = nn.Sequential(
            nn.Conv2d(input_dim, output_dim, kernel_size=3, stride=stride, padding=padding),
            nn.BatchNorm2d(output_dim),
        )

    def forward(self, x):
        return self.conv_block(x) + self.conv_skip(x)


class Upsample(nn.Module):
    def __init__(self, input_dim, output_dim, kernel_size, stride):
        super(Upsample, self).__init__()
        self.upsample = nn.ConvTranspose2d(input_dim, output_dim, kernel_size=kernel_size, stride=stride)

    def forward(self, x):
        return self.upsample(x)


class ResUnet(nn.Module):
    def __init__(self, channel, filters=[64, 128, 256, 512, 1024]):
        super(ResUnet, self).__init__()

        self.input_layer = nn.Sequential(
            nn.Conv2d(channel, filters[0], kernel_size=3, padding=1),
            nn.BatchNorm2d(filters[0]),
            nn.ReLU(),
            nn.Conv2d(filters[0], filters[0], kernel_size=3, padding=1),
        )
        self.input_skip = nn.Sequential(
            nn.Conv2d(channel, filters[0], kernel_size=3, padding=1)
        )

        self.residual_conv_1 = ResidualConv(filters[0], filters[1], 2, 1)
        self.residual_conv_2 = ResidualConv(filters[1], filters[2], 2, 1)
        self.residual_conv_3 = ResidualConv(filters[2], filters[3], 2, 1)

        self.bridge = ResidualConv(filters[3], filters[4], 2, 1)

        self.upsample_1 = Upsample(filters[4], filters[4], 2, 2)
        self.up_residual_conv1 = ResidualConv(filters[4] + filters[3], filters[3], 1, 1)

        self.upsample_2 = Upsample(filters[3], filters[3], 2, 2)
        self.up_residual_conv2 = ResidualConv(filters[3] + filters[2], filters[2], 1, 1)

        self.upsample_3 = Upsample(filters[2], filters[2], 2, 2)
        self.up_residual_conv3 = ResidualConv(filters[2] + filters[1], filters[1], 1, 1)

        self.upsample_4 = Upsample(filters[1], filters[1], 2, 2)
        self.up_residual_conv4 = ResidualConv(filters[1] + filters[0], filters[0], 1, 1)

        self.output_layer = nn.Sequential(
            nn.Conv2d(filters[0], 1, 1, 1),
            nn.Sigmoid(),
        )

    def forward(self, x):
        # Encode
        x1 = self.input_layer(x) + self.input_skip(x)
        x2 = self.residual_conv_1(x1)
        x3 = self.residual_conv_2(x2)
        x4 = self.residual_conv_3(x3)
        # Bridge
        x5 = self.bridge(x4)
        # Decode
        x6 = self.upsample_1(x5)
        x7 = torch.cat([x6, x4], dim=1)
        x8 = self.up_residual_conv1(x7)

        x9 = self.upsample_2(x8)
        x10 = torch.cat([x9, x3], dim=1)
        x11 = self.up_residual_conv2(x10)

        x12 = self.upsample_3(x11)
        x13 = torch.cat([x12, x2], dim=1)
        x14 = self.up_residual_conv3(x13)

        x15 = self.upsample_4(x14)
        x16 = torch.cat([x15, x1], dim=1)
        x17 = self.up_residual_conv4(x16)

        output = self.output_layer(x17)

        return output

修改说明

相比于原始的ResUNet网络，主要进行了以下修改：

在__init__函数中，将filters参数的默认值修改为[64, 128, 256, 512, 1024]。
增加了一个名为residual_conv_3的ResidualConv层，用于处理filters[2]和filters[3]之间的特征图。
增加了一个名为upsample_4的Upsample层和一个名为up_residual_conv4的ResidualConv层，用于上采样和特征融合。
在forward函数中，增加了对新增层的调用，并调整了特征图的连接方式，以适应新的网络结构。

通过以上修改，我们成功将ResUNet网络的过滤器数量扩展为[64, 128, 256, 512, 1024]，从而增加了网络的深度和表达能力。