我想讲resnet和Swin-Transformer结合起来 先用resnet和se注意力模块 在用swin transformer。我用的是pytorch。请给下代码

以下是基于PyTorch实现的ResNet和Swin Transformer的结合代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.resnet import conv1x1, conv3x3, BasicBlock, Bottleneck
from timm.models.vision_transformer import Mlp, PatchEmbed, Attention, Block

class ResNetSwinTransformer(nn.Module):
    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
                 embed_dim=96, depth=12, num_heads=8, mlp_ratio=4., qkv_bias=True,
                 qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1):
        super().__init__()

        self.num_classes = num_classes
        self.drop_rate = drop_rate
        self.attn_drop_rate = attn_drop_rate
        self.drop_path_rate = drop_path_rate

        # ResNet stem
        self.stem = nn.Sequential(
            conv3x3(in_chans, 64, stride=2),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            conv3x3(64, 64),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            conv3x3(64, 128)
        )
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        # Swin Transformer
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=128, embed_dim=embed_dim)
        self.pos_embed = nn.Parameter(torch.zeros(1, self.patch_embed.num_patches, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)

        self.blocks = nn.ModuleList([
            SwinTransformerBlock(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
                qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate,
                drop_path=DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
            )
            for i in range(depth)
        ])

        self.norm = nn.LayerNorm(embed_dim)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(embed_dim, num_classes)

    def forward_features(self, x):
        # ResNet stem
        x = self.stem(x)
        x = self.maxpool(x)

        # Swin Transformer
        x = self.patch_embed(x)
        x = x + self.pos_embed
        x = self.pos_drop(x)

        for block in self.blocks:
            x = block(x)

        x = self.norm(x)

        return x

    def forward(self, x):
        x = self.forward_features(x)
        x = self.avgpool(x).flatten(1)
        x = self.fc(x)
        return x


class SwinTransformerBlock(nn.Module):
    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop=0., attn_drop=0., drop_path=None):
        super().__init__()
        self.norm1 = nn.LayerNorm(dim)
        self.attn = Attention(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
            attn_drop=attn_drop, proj_drop=drop
        )
        self.drop_path = drop_path
        self.norm2 = nn.LayerNorm(dim)
        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=nn.GELU,
                       drop=drop)

    def forward(self, x):
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x

其中，`ResNetSwinTransformer`类实现了ResNet和Swin Transformer的结合，包括ResNet的stem和Swin Transformer的patch embedding、position embedding、Transformer block和layer normalization。

`SwinTransformerBlock`类实现了Swin Transformer中的Transformer block，包括multi-head attention、feedforward network和layer normalization。其中，通过DropPath实现了stochastic depth的效果。

你可以根据自己的需求修改一些超参数或者调整网络结构，例如修改`num_classes`、`depth`、`embed_dim`、`num_heads`、`mlp_ratio`、`drop_rate`、`attn_drop_rate`、`drop_path_rate`等。