RDU-Net代码实现图像分割
时间: 2023-09-24 14:10:47 浏览: 70
RDU-Net是一种用于图像分割的深度神经网络模型。以下是一个基于PyTorch实现的RDU-Net代码示例:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class RDU_Net(nn.Module):
def __init__(self, in_channels, out_channels):
super(RDU_Net, self).__init__()
# Encoding path
self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(128)
self.relu2 = nn.ReLU(inplace=True)
# Decoding path
self.upconv1 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
self.conv3 = nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1)
self.bn3 = nn.BatchNorm2d(64)
self.relu3 = nn.ReLU(inplace=True)
self.conv4 = nn.Conv2d(64, out_channels, kernel_size=3, stride=1, padding=1)
def forward(self, x):
# Encoding path
x1 = self.conv1(x)
x1 = self.bn1(x1)
x1 = self.relu1(x1)
x2 = self.conv2(x1)
x2 = self.bn2(x2)
x2 = self.relu2(x2)
# Decoding path
x2 = self.upconv1(x2)
x1 = self.conv3(torch.cat([x1, x2], dim=1))
x1 = self.bn3(x1)
x1 = self.relu3(x1)
x1 = self.conv4(x1)
return x1
```
在这个实现中,构建了一个具有64个卷积核的Conv2D层,并使用Batch Normalization和ReLU激活函数。然后构建了另一个具有128个卷积核的Conv2D层,并使用Batch Normalization和ReLU激活函数。在解码器路径中,使用反卷积层将特征图上采样为原始大小,并将其与编码器路径中相同层级的特征图连接起来。然后,使用Conv2D层和ReLU激活函数执行卷积操作,将结果输出为最后的分割结果。