yolov5添加transformer模型

要将Transformer模型添加到Yolov5中，需要进行以下步骤：

1. 在models文件夹中创建一个名为swintransformer.py的新文件，并将以下代码添加到该文件中：

import torch.nn as nn
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.vision_transformer import Mlp, PatchEmbed

class SwinTransformerBlock(nn.Module):
    def __init__(self, dim, num_heads, window_size, shift_size, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        self.qkv_bias = qkv_bias
        self.qk_scale = qk_scale
        self.drop = nn.Dropout(drop)
        self.attn_drop = nn.Dropout(attn_drop)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

        self.norm1 = norm_layer(dim)
        self.attn = nn.MultiheadAttention(dim, num_heads, qkv_bias=qkv_bias, dropout=attn_drop, kdim=dim, vdim=dim, batch_first=True)
        self.local_context = LocalContext(window_size, shift_size)
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

    def forward(self, x):
        H, W = x.shape[-2:]
        B, N, C = x.shape
        x = x.reshape(B, H // self.window_size, self.window_size, W // self.window_size, self.window_size, C)
        x = x.permute(0, 1, 3, 2, 4, 5).reshape(B, -1, self.window_size * self.window_size * C)

        x = self.norm1(x)
        qkv = self.attn(x, x, x)[0]
        qkv = self.drop(qkv)
        x = x