Attention U-Net网络

Attention U-Net网络pytorch构建

from torch import nn
from torch.nn import functional as F
import torch
from torchvision import models
import torchvisionclass conv_block(nn.Module):def __init__(self,ch_in,ch_out):super(conv_block,self).__init__()self.conv = nn.Sequential(nn.Conv2d(ch_in, ch_out, kernel_size=3,stride=1,padding=1,bias=True),nn.BatchNorm2d(ch_out),nn.ReLU(inplace=True),nn.Conv2d(ch_out, ch_out, kernel_size=3,stride=1,padding=1,bias=True),nn.BatchNorm2d(ch_out),nn.ReLU(inplace=True))def forward(self,x):x = self.conv(x)return xclass up_conv(nn.Module):def __init__(self,ch_in,ch_out):super(up_conv,self).__init__()self.up = nn.Sequential(nn.Upsample(scale_factor=2),nn.Conv2d(ch_in,ch_out,kernel_size=3,stride=1,padding=1,bias=True),nn.BatchNorm2d(ch_out),nn.ReLU(inplace=True))def forward(self,x):x = self.up(x)return xclass Attention_block(nn.Module):def __init__(self, F_g, F_l, F_int):super(Attention_block, self).__init__()self.W_g = nn.Sequential(nn.Conv2d(F_g, F_int, kernel_size=1, stride=1, padding=0, bias=True),nn.BatchNorm2d(F_int))self.W_x = nn.Sequential(nn.Conv2d(F_l, F_int, kernel_size=1, stride=1, padding=0, bias=True),nn.BatchNorm2d(F_int))self.psi = nn.Sequential(nn.Conv2d(F_int, 1, kernel_size=1, stride=1, padding=0, bias=True),nn.BatchNorm2d(1),nn.Sigmoid())self.relu = nn.ReLU(inplace=True)def forward(self, g, x):g1 = self.W_g(g)x1 = self.W_x(x)psi = self.relu(g1 + x1)psi = self.psi(psi)return x * psiclass AttU_Net(nn.Module):def __init__(self, img_ch=3, output_ch=1):super(AttU_Net, self).__init__()self.Maxpool = nn.MaxPool2d(kernel_size=2, stride=2)self.Conv1 = conv_block(ch_in=img_ch, ch_out=64)self.Conv2 = conv_block(ch_in=64, ch_out=128)self.Conv3 = conv_block(ch_in=128, ch_out=256)self.Conv4 = conv_block(ch_in=256, ch_out=512)self.Conv5 = conv_block(ch_in=512, ch_out=1024)self.Up5 = up_conv(ch_in=1024, ch_out=512)self.Att5 = Attention_block(F_g=512, F_l=512, F_int=256)self.Up_conv5 = conv_block(ch_in=1024, ch_out=512)self.Up4 = up_conv(ch_in=512, ch_out=256)self.Att4 = Attention_block(F_g=256, F_l=256, F_int=128)self.Up_conv4 = conv_block(ch_in=512, ch_out=256)self.Up3 = up_conv(ch_in=256, ch_out=128)self.Att3 = Attention_block(F_g=128, F_l=128, F_int=64)self.Up_conv3 = conv_block(ch_in=256, ch_out=128)self.Up2 = up_conv(ch_in=128, ch_out=64)self.Att2 = Attention_block(F_g=64, F_l=64, F_int=32)self.Up_conv2 = conv_block(ch_in=128, ch_out=64)self.Conv_1x1 = nn.Conv2d(64, output_ch, kernel_size=1, stride=1, padding=0)self.sigmoid = nn.Sigmoid()def forward(self, x):# encoding pathx1 = self.Conv1(x)x2 = self.Maxpool(x1)x2 = self.Conv2(x2)x3 = self.Maxpool(x2)x3 = self.Conv3(x3)x4 = self.Maxpool(x3)x4 = self.Conv4(x4)x5 = self.Maxpool(x4)x5 = self.Conv5(x5)d5 = self.Up5(x5)x4 = self.Att5(g=d5, x=x4)d5 = torch.cat((x4, d5), dim=1)d5 = self.Up_conv5(d5)d4 = self.Up4(d5)x3 = self.Att4(g=d4, x=x3)d4 = torch.cat((x3, d4), dim=1)d4 = self.Up_conv4(d4)d3 = self.Up3(d4)x2 = self.Att3(g=d3, x=x2)d3 = torch.cat((x2, d3), dim=1)d3 = self.Up_conv3(d3)d2 = self.Up2(d3)x1 = self.Att2(g=d2, x=x1)d2 = torch.cat((x1, d2), dim=1)d2 = self.Up_conv2(d2)d1 = self.Conv_1x1(d2)d1 = self.sigmoid(d1)return d1

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