transunet unet
时间: 2025-01-23 17:07:54 浏览: 80
### TransUNet与U-Net架构对比
#### 架构特点
TransUNet引入了一种新的视角来改进传统的U-解码器结构并利用跳跃连接保留空间信息[^4]。然而,TransUNet进一步融合了Transformer机制,在保持原有U型结构的基础上增强了模型对于全局依赖性的捕捉能力[^2]。
具体来说:
- **U-Net**
- 编码路径逐步减少特征图尺寸的同时增加通道数;
- 解码路径则相反操作,并通过跳跃连接将高分辨率特征传递给对应的解码层;
- 主要依靠卷积运算处理局部区域内的像素关系;
- **TransUNet**
- 继承了U-Net的整体框架设计;
- 在瓶颈部分加入基于自注意力机制的多头变压器模块(Multi-head Self Attention, MSA),使得网络能够更好地理解远距离位置之间的关联性;
- 使用线性嵌入代替标准卷积作为输入投影方式,从而允许更灵活的感受野调整;
```python
import torch.nn as nn
class UNet(nn.Module):
def __init__(self):
super().__init__()
self.encoder = Encoder()
self.decoder = Decoder()
def forward(self, x):
enc_outs = []
for layer in self.encoder.layers:
x = layer(x)
enc_outs.append(x)
dec_in = reversed(enc_outs[:-1])
for skip_connection, layer in zip(dec_in, self.decoder.layers):
x = torch.cat([skip_connection, x], dim=1)
x = layer(x)
return x
from transformers import ViTModel
class TransformerBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.hidden_size)
self.self_attention = nn.MultiheadAttention(embed_dim=config.hidden_size,
num_heads=config.num_attention_heads)
def forward(self, hidden_states):
attention_output = self.self_attention(hidden_states, hidden_states, hidden_states)[0]
output = self.layer_norm(attention_output + hidden_states)
return output
class TransUNet(nn.Module):
def __init__(self, vit_config=None):
super().__init__()
self.unet = UNet()
if vit_config is not None:
self.transformer = ViTModel(vit_config).encoder
def forward(self, x):
unet_features = self.unet(x)
transformer_input = reshape_for_transformer(unet_features) # 自定义函数用于适配维度变换
transformed_features = self.transformer(transformer_input)[-1]
final_output = combine_outputs(unet_features, transformed_features) # 合并两种特征的方式需自行定义
return final_output
```
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