video swin transformer 代码

以下是使用3D卷积实现的Video Swin Transformer的代码示例，供您参考：

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

class VideoSwinTransformerBlock(nn.Module):
    def __init__(self, in_channels, out_channels, num_heads, window_size, drop_rate=0.0):
        super().__init__()
        self.norm1 = nn.LayerNorm(in_channels)
        self.attn = nn.MultiheadAttention(in_channels, num_heads)
        self.norm2 = nn.LayerNorm(in_channels)
        self.mlp = nn.Sequential(
            nn.Linear(in_channels, out_channels),
            nn.GELU(),
            nn.Dropout(drop_rate),
            nn.Linear(out_channels, in_channels),
            nn.Dropout(drop_rate)
        )
        self.window_size = window_size

    def forward(self, x):
        # reshape input for 3D convolution
        b, t, c, h, w = x.size()
        x = x.view(b*t, c, h, w)
        
        # add padding to input for overlapping window
        p = self.window_size // 2
        x = F.pad(x, (p, p, p, p), mode='reflect')
        
        # apply 3D convolution with overlapping window
        x = self.conv(x)
        x = x.unfold(2, self.window_size, 1).unfold(3, self.window_size, 1)
        x = x.permute(0, 2, 3, 4, 1, 5, 6).contiguous()
        x = x.view(b*t*h*w, -1, c)
        
        # apply transformer block
        x = self.norm1(x)
        attn_output, _ = self.attn(x, x, x)
        x = x + attn_output
        x = self.norm2(x)
        x = self.mlp(x)
        x = x.view(b*t, h, w, -1, c).permute(0, 3, 4, 1, 2).contiguous()
        
        return x

class VideoSwinTransformer(nn.Module):
    def __init__(self, in_channels, out_channels, num_heads, window_sizes, num_layers, drop_rate=0.0):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv3d(in_channels, out_channels, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)),
            nn.BatchNorm3d(out_channels),
            nn.ReLU(inplace=True)
        )
        self.blocks = nn.ModuleList([
            VideoSwinTransformerBlock(out_channels, out_channels, num_heads, window_size, drop_rate=drop_rate)
            for window_size in window_sizes
        ])
        self.norm = nn.LayerNorm(out_channels)
        self.pool = nn.AdaptiveAvgPool3d((1, 1, 1))
        self.fc = nn.Linear(out_channels, 1000)

    def forward(self, x):
        x = self.conv(x)
        for block in self.blocks:
            x = block(x)
        x = self.norm(x)
        x = self.pool(x)
        x = x.flatten(1)
        x = self.fc(x)
        return x

其中，`VideoSwinTransformerBlock`表示视频Swin Transformer的一个基本块，包含了一个注意力机制和一个多层感知机，同时使用了3D卷积来处理视频数据。`VideoSwinTransformer`则表示整个视频Swin Transformer模型，包含了多个基本块和全局平均池化层和全连接层。您可以根据自己的需求修改其中的参数和模型结构。