1、ASPP模型结构

空洞空间卷积池化金字塔（atrous spatial pyramid pooling (ASPP)）通过对于输入的特征以不同的采样率进行采样，即从不同尺度提取输入特征，然后将所获取的特征进行融合，得到最终的特征提取结果。

2、SENET结构

通道注意力机制（SENET）将尺度为HXWXC尺度大小的特征图通过全局平均池化进行压缩，只保留通道尺度上的大小C，即转换为1X1XC,之后再进行压缩，relu与还原，最后使用simoid进行激活，将各个通道的值转化为0~1范围内，相当于将各个通道的特征转换为权重值。
SENET代码如下：

import torch
import torch.nn as nn
import torch.nn.functional as F
# tensor=torch.ones(size=(2,1280,32,32))
# print(tensor)
class SE_Block(nn.Module):                         # Squeeze-and-Excitation block
    def __init__(self, in_planes):
        super(SE_Block, self).__init__()
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.conv1 = nn.Conv2d(in_planes, in_planes // 16, kernel_size=1)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(in_planes // 16, in_planes, kernel_size=1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.avgpool(x)
        x = self.conv1(x)
        x = self.relu(x)
        x = self.conv2(x)
        out = self.sigmoid(x)
        return out

（如果要直接使用下面的SE_ASPP改进代码，建议将这块代码新建py文件保存，然后在SE_ASPP所在python中导入SE_Block类）

3、改进ASPP:SE_ASPP结构

即把SENET产生的权重值与原本输入的各个特征进行相乘，作为输入特征。代码如下

class SE_ASPP(nn.Module):                       ##加入通道注意力机制
    def __init__(self, dim_in, dim_out, rate=1, bn_mom=0.1):
        super(SE_ASPP, self).__init__()
        self.branch1 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 1, 1, padding=0, dilation=rate, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )
        self.branch2 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 3, 1, padding=6 * rate, dilation=6 * rate, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )
        self.branch3 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 3, 1, padding=12 * rate, dilation=12 * rate, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )
        self.branch4 = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, 3, 1, padding=18 * rate, dilation=18 * rate, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )
        self.branch5_conv = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=True)
        self.branch5_bn = nn.BatchNorm2d(dim_out, momentum=bn_mom)
        self.branch5_relu = nn.ReLU(inplace=True)

        self.conv_cat = nn.Sequential(
            nn.Conv2d(dim_out * 5, dim_out, 1, 1, padding=0, bias=True),
            nn.BatchNorm2d(dim_out, momentum=bn_mom),
            nn.ReLU(inplace=True),
        )
        # print('dim_in:',dim_in)
        # print('dim_out:',dim_out)
        self.senet=SE_Block(in_planes=dim_out*5)

    def forward(self, x):
        [b, c, row, col] = x.size()
        conv1x1 = self.branch1(x)
        conv3x3_1 = self.branch2(x)
        conv3x3_2 = self.branch3(x)
        conv3x3_3 = self.branch4(x)
        global_feature = torch.mean(x, 2, True)
        global_feature = torch.mean(global_feature, 3, True)
        global_feature = self.branch5_conv(global_feature)
        global_feature = self.branch5_bn(global_feature)
        global_feature = self.branch5_relu(global_feature)
        global_feature = F.interpolate(global_feature, (row, col), None, 'bilinear', True)

        feature_cat = torch.cat([conv1x1, conv3x3_1, conv3x3_2, conv3x3_3, global_feature], dim=1)
        # print('feature:',feature_cat.shape)
        seaspp1=self.senet(feature_cat)             #加入通道注意力机制
        # print('seaspp1:',seaspp1.shape)
        se_feature_cat=seaspp1*feature_cat
        result = self.conv_cat(se_feature_cat)
        # print('result:',result.shape)
        return result

Reference

[1].Y. Sun, Y. Yang, G. Yao, F. Wei and M. Wong, “Autonomous Crack and Bughole Detection for Concrete Surface Image Based on Deep Learning,” in IEEE Access, vol. 9, pp. 85709-85720, 2021, doi: 10.1109/ACCESS.2021.3088292.
[2].J. Hu, L. Shen and G. Sun, “Squeeze-and-Excitation Networks,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132-7141, doi: 10.1109/CVPR.2018.00745.