U-Net Pytorch实现

import torch.nn as nn
import torch
from torch import autograd#把常用的2个卷积操作简单封装下
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.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)def forward(self, x):c1 = self.conv1(x)p1 = self.pool1(c1)c2 = self.conv2(p1)p2 = self.pool2(c2)c3 = self.conv3(p2)p3 = self.pool3(c3)c4 = self.conv4(p3)p4 = self.pool4(c4)c5 = self.conv5(p4)up_6 = self.up6(c5)merge6 = torch.cat([up_6, c4], dim=1)c6 = self.conv6(merge6)up_7 = self.up7(c6)merge7 = torch.cat([up_7, c3], dim=1)c7 = self.conv7(merge7)up_8 = self.up8(c7)merge8 = torch.cat([up_8, c2], dim=1)c8 = self.conv8(merge8)up_9 = self.up9(c8)merge9 = torch.cat([up_9, c1], dim=1)c9 = self.conv9(merge9)c10 = self.conv10(c9)out = nn.Sigmoid()(c10)return out

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