模块出处

[link] [code] [NIPS 22] SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

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模块名称

Multi-Scale Convolutional Attention (MSCA)

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模块作用

多尺度特征提取，更大感受野

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模块结构

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模块代码

import torch
import torch.nn as nn

class MSCA(nn.Module):
    def __init__(self, dim):
        super(MSCA, self).__init__()
        self.conv0 = nn.Conv2d(dim, dim, 5, padding=2, groups=dim)
        self.conv0_1 = nn.Conv2d(dim, dim, (1, 7), padding=(0, 3), groups=dim)
        self.conv0_2 = nn.Conv2d(dim, dim, (7, 1), padding=(3, 0), groups=dim)

        self.conv1_1 = nn.Conv2d(dim, dim, (1, 11), padding=(0, 5), groups=dim)
        self.conv1_2 = nn.Conv2d(dim, dim, (11, 1), padding=(5, 0), groups=dim)

        self.conv2_1 = nn.Conv2d(
            dim, dim, (1, 21), padding=(0, 10), groups=dim)
        self.conv2_2 = nn.Conv2d(
            dim, dim, (21, 1), padding=(10, 0), groups=dim)
        self.conv3 = nn.Conv2d(dim, dim, 1)

    def forward(self, x):
        u = x.clone()
        attn = self.conv0(x)

        attn_0 = self.conv0_1(attn)
        attn_0 = self.conv0_2(attn_0)

        attn_1 = self.conv1_1(attn)
        attn_1 = self.conv1_2(attn_1)

        attn_2 = self.conv2_1(attn)
        attn_2 = self.conv2_2(attn_2)
        attn = attn + attn_0 + attn_1 + attn_2

        attn = self.conv3(attn)

        return attn * u
    
if __name__ == '__main__':
    x = torch.randn([1, 512, 16, 16])
    msca = MSCA(512)
    out = msca(x)
    print(out.shape)  # 1, 512, 16, 16

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原文表述

如图2(a)所示，MSCA包含三个部分：一个深度卷积以汇总局部信息；一个多分支深度条带卷积以获取多尺度上下文；一个1×1卷积以建模不同通道之间的信息。该1×1卷积的输出将直接作为注意力以对MSCA的输出结果进行后处理加权。