编写一个语义分割模型代码
时间: 2023-03-04 09:49:32 浏览: 179
这里提供一个使用PyTorch实现的语义分割模型代码示例,基于U-Net网络结构。
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class DoubleConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(DoubleConv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, 3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.conv(x)
class UNet(nn.Module):
def __init__(self, in_channels, out_channels):
super(UNet, self).__init__()
self.dconv_down1 = DoubleConv(in_channels, 64)
self.dconv_down2 = DoubleConv(64, 128)
self.dconv_down3 = DoubleConv(128, 256)
self.dconv_down4 = DoubleConv(256, 512)
self.maxpool = nn.MaxPool2d(2)
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.dconv_up3 = DoubleConv(256 + 512, 256)
self.dconv_up2 = DoubleConv(128 + 256, 128)
self.dconv_up1 = DoubleConv(128 + 64, 64)
self.conv_last = nn.Conv2d(64, out_channels, 1)
def forward(self, x):
# Encoder
x1 = self.dconv_down1(x)
x2 = self.maxpool(x1)
x3 = self.dconv_down2(x2)
x4 = self.maxpool(x3)
x5 = self.dconv_down3(x4)
x6 = self.maxpool(x5)
x7 = self.dconv_down4(x6)
# Decoder
x = self.upsample(x7)
x = torch.cat([x, x5], dim=1)
x = self.dconv_up3(x)
x = self.upsample(x)
x = torch.cat([x, x3], dim=1)
x = self.dconv_up2(x)
x = self.upsample(x)
x = torch.cat([x, x1], dim=1)
x = self.dconv_up1(x)
out = self.conv_last(x)
return out
```
这是一个基本的U-Net网络结构实现,包括DoubleConv模块和UNet模块。在DoubleConv模块中,两个3x3卷积层和批归一化层以及ReLU激活层被串联在一起。在UNet模块中,上采样层使用双线性插值,对于每个解码器层,先进行上采样,然后将它与对应的编码器层级联,再进行DoubleConv操作。模型输出是与目标掩模具有相同空间大小的图像,表示每个像素属于哪个语义类别。