3dtransformer代码
时间: 2023-08-18 07:05:57 浏览: 60
以下是一个用于实现3D Vision Transformer(3DViT)的伪代码示例:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class PatchEmbedding(nn.Module):
def __init__(self, in_channels, embed_dim, patch_size):
super(PatchEmbedding, self).__init__()
self.conv = nn.Conv3d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x):
x = self.conv(x)
x = x.flatten(2).transpose(1, 2)
return x
class TransformerEncoder(nn.Module):
def __init__(self, embed_dim, num_heads, hidden_dim, dropout):
super(TransformerEncoder, self).__init__()
self.self_attention = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout)
self.layer_norm1 = nn.LayerNorm(embed_dim)
self.feed_forward = nn.Sequential(
nn.Linear(embed_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, embed_dim)
)
self.layer_norm2 = nn.LayerNorm(embed_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
residual = x
x = self.layer_norm1(x)
x, _ = self.self_attention(x, x, x)
x = self.dropout(x)
x += residual
residual = x
x = self.layer_norm2(x)
x = self.feed_forward(x)
x = self.dropout(x)
x += residual
return x
class VisionTransformer3D(nn.Module):
def __init__(self, in_channels, patch_size,
num_layers, embed_dim, num_heads,
hidden_dim, num_classes, dropout):
super(VisionTransformer3D, self).__init__()
self.patch_embedding = PatchEmbedding(in_channels, embed_dim, patch_size)
self.transformer_encoder = nn.ModuleList([
TransformerEncoder(embed_dim, num_heads, hidden_dim, dropout)
for _ in range(num_layers)
])
self.layer_norm = nn.LayerNorm(embed_dim)
self.fc = nn.Linear(embed_dim, num_classes)
def forward(self, x):
x = self.patch_embedding(x)
for transformer in self.transformer_encoder:
x = transformer(x)
x = self.layer_norm(x[:, 0]) # 取第一个位置的特征
x = self.fc(x)
return F.log_softmax(x, dim=-1)
```
这只是一个简化的示例代码,用于说明3D Vision Transformer的基本结构。实际使用时,可能需要根据具体任务和数据进行适当的修改和调整。