swin transformer block代码

以下是一个简单的Swin Transformer块的代码示例：

import torch.nn as nn
from einops.layers.torch import Rearrange

class SwinTransformerBlock(nn.Module):
    def __init__(self, embed_dim, num_heads, feedforward_dim, dropout_rate):
        super().__init__()
        self.norm1 = nn.LayerNorm(embed_dim)
        self.self_attention = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout_rate)
        self.dropout1 = nn.Dropout(dropout_rate)
        self.norm2 = nn.LayerNorm(embed_dim)
        self.feedforward = nn.Sequential(
            nn.Linear(embed_dim, feedforward_dim),
            nn.ReLU(),
            nn.Linear(feedforward_dim, embed_dim),
        )
        self.dropout2 = nn.Dropout(dropout_rate)
        
    def forward(self, x):
        residual = x
        x = self.norm1(x)
        x = self.self_attention(x, x, x)[0]
        x = self.dropout1(x)
        x += residual
        residual = x
        x = self.norm2(x)
        x = self.feedforward(x)
        x = self.dropout2(x)
        x += residual
        return x

这个Swin Transformer块包括一个多头自注意力层、一个Feedforward层和一个LayerNorm层。在这个块中，输入张量经过LayerNorm层进行归一化，然后通过自注意力层进行加权平均处理。注意力输出张量通过Dropout层进行随机失活，然后与输入张量进行残差连接并再次通过LayerNorm层进行归一化。然后，这个张量再经过一个Feedforward层进行非线性变换，输出再次通过Dropout层进行随机失活，然后与先前的残差连接进行最终的输出。