用mobilevit替换yolov7主干网络

MobileViT是一种轻量级的视觉Transformer网络，适用于移动设备和嵌入式设备。它由移动设备优化的ViT（Vision Transformer）的修改版本组成，用于高效的图像分类和目标检测。MobileViT的主干网络包含一个基于MobileNetV3的特征提取器和一个Transformer编码器。MobileNetV3用于提取特征图，而Transformer编码器用于对特征图进行建模。MobileViT的主干网络比YOLOv7更轻量级，可以在移动设备上进行快速的目标检测。

以下是使用MobileViT替换YOLOv7主干网络的代码示例：

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

class MobileViT(nn.Module):
    def __init__(self, img_size=224, num_classes=1000, patch_size=16, embed_dim=768, depth=12,
                 num_heads=12, mlp_ratio=4., qkv_bias=True, drop_rate=0., attn_drop_rate=0.,
                 drop_path_rate=0., norm_layer=nn.LayerNorm):
        super(MobileViT, self).__init__()
        self.num_classes = num_classes
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=3, embed_dim=embed_dim)
        self.pos_drop = nn.Dropout(p=drop_rate)
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
        self.blocks = nn.ModuleList([
            Block(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
            for i in range(depth)])
        self.norm = norm_layer(embed_dim)
        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()

    def forward(self, x):
        x = self.patch_embed(x)
        x = self.pos_drop(x)
        for blk in self.blocks:
            x = blk(x)
        x = self.norm(x)
        x = x.mean(dim=1)
        x = self.head(x)
        return x

class Block(nn.Module):
    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm):
        super(Block, self).__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), out_features=dim,
                       act_layer=nn.ReLU, 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

在这里，`MobileViT`类定义了MobileViT的主干网络，包含一个Patch Embedding层、多个Transformer Block层和一个全连接层。每个Transformer Block层包含一个多头自注意力层、一个前馈网络层和一个残差连接。与YOLOv7不同，MobileViT的主干网络是基于Transformer而不是卷积神经网络的。另外，MobileViT更加轻量级，适用于移动设备和嵌入式设备。