unet++ pytorch代码
时间: 2023-05-16 19:07:45 浏览: 203
以下是一个简单的 UNet PyTorch 代码示例:
```
import torch
import torch.nn as nn
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),
nn.ReLU(inplace=True),
nn.Conv2d(out_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
def forward(self, x):
x = self.conv(x)
return x
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)
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
```
这是一个简单的 UNet 模型,用于图像分割任务。它由一系列双卷积层和池化层组成,然后通过一系列反卷积层进行上采样和合并。最后,输出通过 Sigmoid 函数进行激活,以产生二进制分割掩模。
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