代码生成，用torch写出yolov7添加的swin transformer检测头代码

以下是我对您的问题的回答：

对于您的问题，我可以回答。以下是用torch写出yolov7添加的swin transformer检测头代码的示例：

import torch
import torch.nn as nn
from models.common import Conv, DWConv

class YOLOv7(nn.Module):
    def __init__(self, num_classes=80, width_mult=0.5, hr=False):
        super(YOLOv7, self).__init__()
        self.num_classes = num_classes
        self.width_mult = width_mult
        self.hr = hr

        # Backbone
        self.backbone = nn.Sequential(
            Conv(3, int(64 * width_mult), 3, 2),
            DWConv(int(64 * width_mult), int(128 * width_mult), 3, 2),
            DWConv(int(128 * width_mult), int(256 * width_mult), 3, 2),
            DWConv(int(256 * width_mult), int(512 * width_mult), 3, 2),
            DWConv(int(512 * width_mult), int(1024 * width_mult), 3, 2),
            DWConv(int(1024 * width_mult), int(1024 * width_mult), 3, 2),
        )

        # Neck
        self.neck = nn.Sequential(
            Conv(int(1024 * width_mult), int(512 * width_mult), 1),
            nn.Upsample(scale_factor=2, mode='nearest'),
            Conv(int(1024 * width_mult), int(512 * width_mult), 1),
            Conv(int(512 * width_mult), int(1024 * width_mult), 3),
            Conv(int(1024 * width_mult), int(512 * width_mult), 1),
            Conv(int(512 * width_mult), int(1024 * width_mult), 3),
            Conv(int(1024 * width_mult), int(512 * width_mult), 1),
            Conv(int(512 * width_mult), int(1024 * width_mult), 3),
        )

        # Head
        self.head = nn.Sequential(
            Conv(int(1024 * width_mult), int(512 * width_mult), 1),
            Conv(int(512 * width_mult), int(1024 * width_mult), 3),
            Conv(int(1024 * width_mult), int(512 * width_mult), 1),
            Conv(int(512 * width_mult), int(1024 * width_mult), 3),
            Conv(int(1024 * width_mult), int(512 * width_mult), 1),
            nn.Conv2d(int(