多模态融合的高分遥感图像语义分割方法_se-unet

多模态融合的高分遥感图像语义分割方法(python)

论文地址：http://www.cnki.com.cn/Article/CJFDTotal-ZNZK202004012.htm

1、SE-UNet 网络模型

2、SE-UNet的具体设计方案

3、SE-UNet的pytorch复现

import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
import torch

class SEBlock(nn.Module):

    def __init__(self,ch_in):
        super(SEBlock, self).__init__()
        self.relu = nn.ReLU(inplace=False)
        self.global_pool = nn.AdaptiveAvgPool2d((1, 1))  # N * 32 * 1 * 1
        self.fc1 = nn.Linear(in_features = int(ch_in), out_features = int(ch_in//2))
        self.fc2 = nn.Linear(in_features = int(ch_in//2), out_features = int(ch_in))
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        # sequeeze
        out = self.global_pool(x)   
        out = out.view(out.size(0), -1)
        # Excitation
        out = self.fc1(out)
        out = self.relu(out)
        out = self.fc2(out)
        out = self.sigmoid(out)
        out = out.view(out.size(0), out.size(1), 1, 1)
        # Scale
        # out = out * x
        # out += x
        # out = self.relu(out)

        return out
        
class DoubleConv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(DoubleConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_ch, out_ch, 3, padding=1),
            nn.BatchNorm2d(out_ch), #添加了BN层
            nn.ReLU(inplace=True),
            nn.Conv2d(out_ch, out_ch, 3, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True)
        )

    def forward(self, input):
        return self.conv(input)

class Unet(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(Unet, self).__init__()
        self.pool = nn.MaxPool2d(2)
        self.conv1 = DoubleConv(in_ch, 64)
        self.pool1 = nn.MaxPool2d(2)
        self.conv2 = DoubleConv(64, 128)
        self.pool2 = nn.MaxPool2d(2)
        self.conv3 = DoubleConv(128, 256)
        self.pool3 = nn.MaxPool2d(2)
        self.conv4 = DoubleConv(256, 512)
        self.pool4 = nn.MaxPool2d(2)
        self.conv5 = DoubleConv(512, 1024)
        # 逆卷积，也可以使用上采样(保证k=stride,stride即上采样倍数)
        self.up6 = nn.ConvTranspose2d(1024, 512, 2, stride=2)
        self.conv6 = DoubleConv(1024, 512)
        self.up7 = nn.ConvTranspose2d(512, 256, 2, stride=2)
        self.conv7 = DoubleConv(512, 256)
        self.up8 = nn.ConvTranspose2d(256, 128, 2, stride=2)
        self.conv8 = DoubleConv(256, 128)
        self.up9 = nn.ConvTranspose2d(128, 64, 2, stride=2)
        self.conv9 = DoubleConv(128, 64)
        self.conv10 = nn.Conv2d(64, out_ch, 1)
        self.conv1_dilation = nn.Conv2d(2048, 256, 1, stride=1, padding=0, bias=False, dilation=1)  # dilation就是空洞率，即间隔
        self.conv2_dilation = nn.Conv2d(2048, 256, 2, stride=1, padding=2, bias=False, dilation=2)  # dilation就是空洞率，即间隔
        self.conv4_dilation = nn.Conv2d(2048, 256, 4, stride=1, padding=4, bias=False, dilation=4)  # dilation就是空洞率，即间隔
        self.global_pool = nn.AdaptiveAvgPool2d((1, 1)) 
        self.upsample = nn.Upsample(scale_factor=7, mode='bicubic', align_corners=True) 
        self.conv_c = nn.Conv2d(2816, 1024, 1, stride=1, padding=0, bias=False, dilation=1)  # dilation就是空洞率，即间隔
        self.upsample1 = nn.Upsample(scale_factor=2, mode='bicubic', align_corners=True) 

        self.R1 = nn.Sequential(
            nn.Conv2d(1, 64, 3, 1, 1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=False)
        ) # N * 16 * 16 * 16

        self.RP2 = nn.Sequential(
            nn.Conv2d(64, 128, 3, 1, 1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=False),
            nn.MaxPool2d(2, 2),
            nn.ReLU(inplace=False)
        ) # N * 16 * 16 * 16

        self.RP3 = nn.Sequential(
            nn.Conv2d(128, 256, 3, 1, 1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=False),
            nn.MaxPool2d(2, 2),
            nn.ReLU(inplace=False)
        )

        self.RP4 = nn.Sequential(
            nn.Conv2d(256, 512, 3, 1, 1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=False),
            nn.MaxPool2d(2, 2),
            nn.ReLU(inplace=False)
        )
        self.RP5 = nn.Sequential(
            nn.Conv2d(512, 1024, 3, 1, 1, bias=False),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=False),
            nn.MaxPool2d(2, 2),
            nn.ReLU(inplace=False)
        )
        self.SE1 = SEBlock(64)
        self.SE2 = SEBlock(128)
        self.SE3 = SEBlock(256)
        self.SE4 = SEBlock(512)
        self.SE5 = SEBlock(1024)

    def forward(self, DSM, RGB):
        c1_DSM = self.R1(DSM)        # [2, 64, 512, 512]
        c1_SE_DSM = self.SE1(c1_DSM) # [2, 64,  1,  1]
        c1_RGB = self.conv1(RGB)     # [2, 64, 512, 512]
        c1_RGB = c1_SE_DSM * c1_RGB  # [2, 64, 512, 512]
        p1_RGB = self.pool1(c1_RGB)  # [2, 64, 256, 256]

        c2_DSM = self.RP2(c1_DSM)    # [2, 128, 256, 256]
        c2_SE_DSM = self.SE2(c2_DSM) # [2, 128,  1,  1]
        c2_RGB = self.conv2(p1_RGB)  # [2, 128, 256, 256]
        c2_RGB = c2_SE_DSM * c2_RGB  # [2, 128, 256, 256]
        p2_RGB = self.pool2(c2_RGB)  # [2, 128, 128, 128]

        c3_DSM = self.RP3(c2_DSM)    # [2, 256, 128, 128]
        c3_SE_DSM = self.SE3(c3_DSM) # [2, 256,  1,  1]
        c3_RGB = self.conv3(p2_RGB)  # [2, 256, 128, 128]
        c3_RGB = c3_SE_DSM * c3_RGB  # [2, 256, 128, 128]
        p3_RGB = self.pool3(c3_RGB)  # [2, 256, 64, 64]

        c4_DSM = self.RP4(c3_DSM)    # [2, 512, 64, 64]
        c4_SE_DSM = self.SE4(c4_DSM) # [2, 512,  1,  1]
        c4_RGB = self.conv4(p3_RGB)  # [2, 512, 64, 64]
        c4_RGB = c4_SE_DSM * c4_RGB  # [2, 512, 64, 64]
        p4_RGB = self.pool4(c4_RGB)  # [2, 512, 32, 32]

        c5_DSM = self.RP5(c4_DSM)    # [2, 1024, 32, 32]
        c5_SE_DSM = self.SE5(c5_DSM) # [2, 1024,  1,  1]
        c5_RGB = self.conv5(p4_RGB)  # [2, 1024, 32, 32]
        c5_RGB = c5_SE_DSM * c5_RGB  # [2, 1024, 32, 32]
         
        up_6 = self.up6(c5_RGB) # [2, 512, 64, 64]
        merge6 = torch.cat([up_6, c4_RGB], dim=1) # [2, 1024, 64, 64]
        c6 = self.conv6(merge6) # [2, 512, 64, 64]
        up_7 = self.up7(c6)     # [2, 256, 128, 128]

        merge7 = torch.cat([up_7, c3_RGB], dim=1) # [2, 512, 128, 128]
        c7 = self.conv7(merge7) # [2, 256, 128, 128]
        up_8 = self.up8(c7)     # [2, 128, 256, 256]

        merge8 = torch.cat([up_8, c2_RGB], dim=1) # [2, 256, 256, 256]
        c8 = self.conv8(merge8) # [2, 128, 256, 256]
        up_9 = self.up9(c8)     # [2, 64, 512, 512]

        merge9 = torch.cat([up_9, c1_RGB], dim=1) # [2, 128, 512, 512]
        c9 = self.conv9(merge9) # [2, 64, 512, 512]
        c10 = self.conv10(c9)   # [2, 3, 512, 512]
        out = nn.Sigmoid()(c10) # [2, 3, 512, 512]
        return out

if __name__ == "__main__":
    DSM = torch.randn(2, 1, 512, 512)
    RGB = torch.randn(2, 3, 512, 512)
    UNet = Unet(3,3)
    out_result = UNet(DSM,RGB)
    print(out_result)
    print(out_result.shape)
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