请写出使用基于pytorch的attention-Unet模型分割乳腺超声图像的代码

我可以提供一个参考，你可以参考以下代码：import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variableclass Attention_Unet(nn.Module):
def __init__(self, in_channels=1, out_channels=1, init_features=32):
super(Attention_Unet, self).__init__()
#Encoder
self.conv1 = nn.Sequential(nn.Conv2d(in_channels, init_features, 3, padding=1),
nn.BatchNorm2d(init_features),
nn.ReLU(inplace=True))
self.conv2 = nn.Sequential(nn.Conv2d(init_features, init_features, 3, padding=1),
nn.BatchNorm2d(init_features),
nn.ReLU(inplace=True))
self.maxpool = nn.MaxPool2d(2, 2)
self.conv3 = nn.Sequential(nn.Conv2d(init_features, init_features*2, 3, padding=1),
nn.BatchNorm2d(init_features*2),
nn.ReLU(inplace=True))
self.conv4 = nn.Sequential(nn.Conv2d(init_features*2, init_features*2, 3, padding=1),
nn.BatchNorm2d(init_features*2),
nn.ReLU(inplace=True))
self.conv5 = nn.Sequential(nn.Conv2d(init_features*2, init_features*4, 3, padding=1),
nn.BatchNorm2d(init_features*4),
nn.ReLU(inplace=True))
self.conv6 = nn.Sequential(nn.Conv2d(init_features*4, init_features*4, 3, padding=1),
nn.BatchNorm2d(init_features*4),
nn.ReLU(inplace=True))
self.conv7 = nn.Sequential(nn.Conv2d(init_features*4, init_features*8, 3, padding=1),
nn.BatchNorm2d(init_features*8),
nn.ReLU(inplace=True))
self.conv8 = nn.Sequential(nn.Conv2d(init_features*8, init_features*8, 3, padding=1),
nn.BatchNorm2d(init_features*8),
nn.ReLU(inplace=True)) #Decoder
self.upconv1 = nn.ConvTranspose2d(init_features*8, init_features*4, 2, stride=2)
self.conv9 = nn.Sequential(nn.Conv2d(init_features*12, init_features*4, 3, padding=1),
nn.BatchNorm2d(init_features*4),
nn.ReLU(inplace=True))
self.conv10 = nn.Sequential(nn.Conv2d(init_features*4, init_features*4, 3, padding=1),
nn.BatchNorm2d(init_features*4),
nn.ReLU(inplace=True))
self.upconv2 = nn.ConvTranspose2d(init_features*4, init_features*2, 2, stride=2)
self.conv11 = nn.Sequential(nn.Conv2d(init_features*6, init_features*2, 3, padding=1),
nn.BatchNorm2d(init_features*2),
nn.ReLU(inplace=True))
self.conv12 = nn.Sequential(nn.Conv2d(init_features*2, init_features*2, 3, padding=1),
nn.BatchNorm2d(init_features*2),
nn.ReLU(inplace=True))
self.upconv3 = nn.ConvTranspose2d(init_features*2, init_features, 2, stride=2)
self.conv13 = nn.Sequential(nn.Conv2d(init_features*3, init_features, 3, padding=1),
nn.BatchNorm2d(init_features),
nn.ReLU(inplace=True))
self.conv14 = nn.Sequential(nn.Conv2d(init_features, init_features, 3, padding=1),
nn.BatchNorm2d(init_features),
nn.ReLU(inplace=True))
self.conv15 = nn.Conv2d(init_features, out_channels, 1) def forward(self, x):
# Encoder
x1 = self.conv1(x)
x2 = self.conv2(x1)
x3 = self.maxpool(x2)
x4 = self.conv3(x3)
x5 = self.conv4(x4)
x6 = self.maxpool(x5)
x7 = self.conv5(x6)
x8 = self.conv6(x7)
x9 = self.maxpool(x8)
x10 = self.conv7(x9)
x11 = self.conv8(x10) # Decoder
x12 = self.upconv1(x11)
x12 = torch.cat((x12, x8), dim=1) # concat along channel axis
x13 = self.conv9(x12)
x14 = self.conv10(x13)
x15 = self.upconv2(x14)
x15 = torch.cat((x15, x5), dim=1)
x16 = self.conv11(x15)
x17 = self.conv12(x16)
x18 = self.upconv3(x17)
x18 = torch.cat((x18, x2), dim=1)
x19 = self.conv13(x18)
x20 = self.conv14(x19)
x21 = self.conv15(x20) return x21