swin-transform模块详细代码

以下是一个简单的示例，展示如何在PyTorch中实现Swin Transformer模块的代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class SwinTransformerBlock(nn.Module):
    def __init__(self, dim, num_heads, mlp_ratio=4):
        super(SwinTransformerBlock, self).__init__()
        
        self.norm1 = nn.LayerNorm(dim)
        self.attn = nn.MultiheadAttention(dim, num_heads)
        self.norm2 = nn.LayerNorm(dim)
        self.mlp = nn.Sequential(
            nn.Linear(dim, dim * mlp_ratio),
            nn.GELU(),
            nn.Linear(dim * mlp_ratio, dim)
        )
    
    def forward(self, x):
        residual = x
        x = self.norm1(x)
        x = x.permute(1, 0, 2)  # (seq_len, batch_size, dim)
        x, _ = self.attn(x, x, x) # self-attention
        x = x.permute(1, 0, 2)  # (batch_size, seq_len, dim)
        x += residual
        
        residual = x
        x = self.norm2(x)
        x = self.mlp(x)  # feed-forward network
        x += residual
        
        return x

class SwinTransformer(nn.Module):
    def __init__(self, num_layers, dim, num_heads, mlp_ratio=4):
        super(SwinTransformer, self).__init__()
        
        self.embedding = nn.Linear(128, dim)  # input embedding
        self.layers = nn.ModuleList([
            SwinTransformerBlock(dim, num_heads, mlp_ratio)
            for _ in range(num_layers)
        ])
        self.norm = nn.LayerNorm(dim)
    
    def forward(self, x):
        x = self.embedding(x)  # input embedding
        for layer in self.layers:
            x = layer(x)
        x = self.norm(x)
        return x

上述代码定义了一个SwinTransformerBlock类和一个SwinTransformer类。SwinTransformerBlock类表示Swin Transformer模块的基本构建块，包括多头自注意力机制和前馈网络。SwinTransformer类则由多个SwinTransformerBlock组成，用于构建整个Swin Transformer模块。

请注意，上述代码只是一个简化的示例，实际使用时可能需要根据具体需求进行适当的修改和调整。此外，代码中的输入维度、参数设置等也可能需要根据实际情况进行调整。

希望这个示例能对你理解如何实现Swin Transformer模块有所帮助。如果需要更详细的代码或更深入的解释，请参考相关论文和开源实现。