基于Python实现的结合U - Net与Transformer的神经网络用于视网膜血管分割的示例代码
以下是一个基于Python实现的结合U - Net与Transformer的神经网络用于视网膜血管分割的示例代码。我们将使用PyTorch深度学习框架。
1. 安装依赖库
确保你已经安装了以下库:
pip install torch torchvision numpy
2. 代码实现
import torch
import torch.nn as nn
import torch.nn.functional as F
# Transformer模块
class TransformerBlock(nn.Module):
def __init__(self, dim, num_heads, mlp_dim, dropout=0.1):
super().__init__()
self.norm1 = nn.LayerNorm(dim)
self.attn = nn.MultiheadAttention(dim, num_heads, dropout=dropout)
self.norm2 = nn.LayerNorm(dim)
self.mlp = nn.Sequential(
nn.Linear(dim, mlp_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(mlp_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x):
x1 = self.norm1(x)
attn_output, _ = self.attn(x1, x1, x1)
x = x + attn_output
x2 = self.norm2(x)
x = x + self.mlp(x2)
return x
# 编码器块
class EncoderBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
skip = x
x = self.pool(x)
return x, skip
# 解码器块
class DecoderBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
def forward(self, x, skip):
x = self.up(x)
diffY = skip.size()[2] - x.size()[2]
diffX = skip.size()[3] - x.size()[3]
x = F.pad(x, [diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2])
x = torch.cat([skip, x], dim=1)
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
return x
# 结合U - Net与Transformer的模型
class UNetTransformer(nn.Module):
def __init__(self, in_channels=3, out_channels=1, features=[64, 128, 256, 512]):
super().__init__()
self.encoders = nn.ModuleList(
[EncoderBlock(in_channels, features[0])] +
[EncoderBlock(features[i], features[i + 1]) for i in range(len(features) - 1)]
)
self.transformer = TransformerBlock(features[-1], num_heads=8, mlp_dim=2048)
self.decoders = nn.ModuleList(
[DecoderBlock(features[i], features[i - 1]) for i in range(len(features) - 1, 0, -1)]
)
self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)
def forward(self, x):
skips = []
for encoder in self.encoders:
x, skip = encoder(x)
skips.append(skip)
b, c, h, w = x.shape
x = x.flatten(2).transpose(1, 2)
x = self.transformer(x)
x = x.transpose(1, 2).view(b, c, h, w)
skips = skips[::-1]
for i, decoder in enumerate(self.decoders):
skip = skips[i]
x = decoder(x, skip)
x = self.final_conv(x)
return x
# 测试代码
if __name__ == "__main__":
model = UNetTransformer(in_channels=3, out_channels=1)
x = torch.randn(1, 3, 256, 256)
output = model(x)
print(output.shape)
3. 代码解释
- TransformerBlock:实现了一个标准的Transformer块,包含多头自注意力机制和前馈神经网络。
- EncoderBlock:U - Net的编码器块,包含两个卷积层和一个最大池化层。
- DecoderBlock:U - Net的解码器块,包含一个反卷积层和两个卷积层,用于上采样和特征融合。
- UNetTransformer:结合了U - Net和Transformer的模型。编码器部分使用U - Net的编码器块,中间使用Transformer块进行特征提取,解码器部分使用U - Net的解码器块。
4. 注意事项
- 此代码仅为示例,实际应用中可能需要根据具体数据集和任务进行调整,如调整模型参数、添加数据增强、优化训练过程等。
- 训练模型时,你需要准备视网膜血管分割的数据集,并使用合适的损失函数(如二元交叉熵损失)和优化器(如Adam)进行训练。