每日Attention学习16——Multi-layer Multi-scale Dilated Convolution

模块出处

CBM 22 link code Do You Need Sharpened Details? Asking MMDC-Net: Multi-layer Multi-scale Dilated Convolution Network For Retinal Vessel Segmentation

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模块名称

Multi-layer Multi-scale Dilated Convolution (MMDC)

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模块作用

多尺度特征提取与融合

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模块结构

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模块思想

与传统的编码器-解码器结构相比，更好的分割模型应该使其编码器能够尽可能多地获取全局信息。然而，这通常受到小感受野应用的限制，因此传统编码器学习的图像特征包含的全局信息不足。多尺度膨胀卷积可以在一定程度上解决这个问题。但是，它仍然存在以下缺点：1）相应的图像大小通常是单一的，可能会错过不同尺度的全局信息，2）由于多层信息组合效率低下，可能会丢失更多的血管细节，特别是影响眼底图像中那些小血管的分割。为了解决这两个问题，我们提出了 MMDC 模块并将其插入 U-Net 模型的 skip 连接中。与 MSDC 或其他类似方法不同，我们的 MMDC 模块基于一种新颖的级联模式，将不同尺度组合在一起，并融合了多层特性，以更好地解决上述问题。因此，与其他工作不同，MMDC 通过提出的级联模式实现了相对更大的感受野，这有助于提取更多的全局信息并更好地完成视网膜血管分割。此外，所提出的 MMDC 模块是完全即插即用的。

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模块代码

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


class MMDC(nn.Module):
    def __init__(self, out_channels):
        super().__init__()
        self.conv3_1 =  nn.Conv2d(out_channels*2,out_channels*2, kernel_size=1)
        self.conv3_3_1 =  nn.Sequential(nn.Conv2d(out_channels*2,out_channels*2, kernel_size=3, padding=1, dilation=1),
                                        nn.ReLU(inplace=True))                   ## conv3_3_x indicates denotes the convolution of different dilation at the lower level
        self.conv3_3_3 =  nn.Sequential(nn.Conv2d(out_channels*2,out_channels*2, kernel_size=3, padding=3, dilation=3),
                                        nn.ReLU(inplace=True))
        self.conv3_3_5 =  nn.Sequential(nn.Conv2d(out_channels*2,out_channels*2, kernel_size=3, padding=5, dilation=5),
                                        nn.ReLU(inplace=True))
        self.conv33 =  nn.Sequential(nn.Conv2d(out_channels*2,out_channels, kernel_size=1),
                                        nn.ReLU(inplace=True))
        self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        self.conv1_1 = nn.Conv2d(out_channels // 2, out_channels //2, kernel_size=1)
        self.conv1_3_1 = nn.Sequential(
            nn.Conv2d(out_channels //2, out_channels //2, kernel_size=3, padding=1, dilation=1),
            nn.ReLU(inplace=True))
        self.conv1_3_3 = nn.Sequential(
            nn.Conv2d(out_channels //2, out_channels //2, kernel_size=3, padding=3, dilation=3),
            nn.ReLU(inplace=True))
        self.conv1_3_5 = nn.Sequential(
            nn.Conv2d(out_channels // 2, out_channels // 2, kernel_size=3, padding=5, dilation=5),
            nn.ReLU(inplace=True))
        self.max = nn.Sequential(nn.MaxPool2d(2),
                                 nn.Conv2d(out_channels // 2, out_channels, kernel_size=1),
                                 nn.BatchNorm2d(out_channels))
    
    def forward(self, x1,x2,x3):
        """ x1--> 2H * 2W * C/2,  x2--> H * W * C,  x3-->H/2 * W/2 * 2C"""
        x1_1 = self.conv1_1(x1)
        x1_2 = self.conv1_1(self.conv1_3_1(x1))
        x1_3 = self.conv1_1(self.conv1_3_3(self.conv1_3_1(x1)))
        x1_4 = self.conv1_1(self.conv1_3_5(self.conv1_3_3(self.conv1_3_1(x1))))
        x11 = self.max(x1_1 + x1_2 + x1_3 + x1_4)
        x3_1 = self.conv3_1(x3)
        x3_2 = self.conv3_1(self.conv3_3_1(x3))
        x3_3 = self.conv3_1(self.conv3_3_3(self.conv3_3_1(x3)))
        x3_4 = self.conv3_1(self.conv3_3_5(self.conv3_3_3(self.conv3_3_1(x3))))
        x33 = self.conv33(self.up(x3_1 + x3_2 + x3_3 +x3_4))
        return x11 + x2 + x33
    

if __name__ == '__main__':
    x1 = torch.randn([1, 64, 44, 44])
    x2 = torch.randn([1, 128, 22, 22])
    x3 = torch.randn([1, 256, 11, 11])
    mmdc = MMDC(out_channels=128)
    out = mmdc(x1, x2, x3)
    print(out.shape)  # 1, 128, 22, 22

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