🚀一、RepViT介绍 

• 论文题目：《RepViT: Revisiting Mobile CNN From ViT Perspective》
• 论文地址：https://arxiv.org/pdf/2307.09283.pdf
• 源码地址：代码地址

1.1 简介

近年来，与轻量级卷积神经网络(cnn)相比，轻量级视觉变压器(ViTs)在资源受限的移动设备上表现出了更高的性能和更低的延迟。这种改进通常归功于多头自注意模块，它使模型能够学习全局表示。然而，轻量级vit和轻量级cnn之间的架构差异还没有得到充分的研究。

本文重点探讨了在资源有限的移动设备上，通过重新审视轻量级卷积神经网络的设计，并整合轻量级 ViTs 的有效架构选择，来提升轻量级 CNNs 的性能，即RepViT。

大量的实验表明，RepViT优于现有的轻型ViT，并在各种视觉任务中表现出良好的延迟。在ImageNet上，RepViT在iPhone 12上以近1ms的延迟实现了超过80%的top-1精度，据我们所知，这是轻量级模型的第一次。

🚀二、具体添加方法 

第①步：在common.py中添加RepViT模块

将以下代码复制粘贴到common.py文件的末尾

class Conv2d_BN(torch.nn.Sequential):
    def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,
                 groups=1, bn_weight_init=1, resolution=-10000):
        super().__init__()
        self.add_module('c', torch.nn.Conv2d(
            a, b, ks, stride, pad, dilation, groups, bias=False))
        self.add_module('bn', torch.nn.BatchNorm2d(b))
        torch.nn.init.constant_(self.bn.weight, bn_weight_init)
        torch.nn.init.constant_(self.bn.bias, 0)
    @torch.no_grad()
    def fuse(self):
        c, bn = self._modules.values()
        w = bn.weight / (bn.running_var + bn.eps)**0.5
        w = c.weight * w[:, None, None, None]
        b = bn.bias - bn.running_mean * bn.weight / \
            (bn.running_var + bn.eps)**0.5
        m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size(
            0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups,
            device=c.weight.device)
        m.weight.data.copy_(w)
        m.bias.data.copy_(b)
        return m
class Residual(torch.nn.Module):
    def __init__(self, m, drop=0.):
        super().__init__()
        self.m = m
        self.drop = drop
    def forward(self, x):
        if self.training and self.drop > 0:
            return x + self.m(x) * torch.rand(x.size(0), 1, 1, 1,
                                              device=x.device).ge_(self.drop).div(1 - self.drop).detach()
        else:
            return x + self.m(x)
    @torch.no_grad()
    def fuse(self):
        if isinstance(self.m, Conv2d_BN):
            m = self.m.fuse()
            assert(m.groups == m.in_channels)
            identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)
            identity = torch.nn.functional.pad(identity, [1,1,1,1])
            m.weight += identity.to(m.weight.device)
            return m
        elif isinstance(self.m, torch.nn.Conv2d):
            m = self.m
            assert(m.groups != m.in_channels)
            identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)
            identity = torch.nn.functional.pad(identity, [1,1,1,1])
            m.weight += identity.to(m.weight.device)
            return m
        else:
            return self
class RepVGGDW(torch.nn.Module):
    def __init__(self, ed) -> None:
        super().__init__()
        self.conv = Conv2d_BN(ed, ed, 3, 1, 1, groups=ed)
        self.conv1 = Conv2d_BN(ed, ed, 1, 1, 0, groups=ed)
        self.dim = ed
    def forward(self, x):
        return self.conv(x) + self.conv1(x) + x
    @torch.no_grad()
    def fuse(self):
        conv = self.conv.fuse()
        conv1 = self.conv1.fuse()
        conv_w = conv.weight
        conv_b = conv.bias
        conv1_w = conv1.weight
        conv1_b = conv1.bias
        conv1_w = torch.nn.functional.pad(conv1_w, [1,1,1,1])
        identity = torch.nn.functional.pad(torch.ones(conv1_w.shape[0], conv1_w.shape[1], 1, 1, device=conv1_w.device), [1,1,1,1])
        final_conv_w = conv_w + conv1_w + identity
        final_conv_b = conv_b + conv1_b
        conv.weight.data.copy_(final_conv_w)
        conv.bias.data.copy_(final_conv_b)
        return conv
class RepViTBlock(nn.Module):
    def __init__(self,in1, inp, hidden_dim, oup, kernel_size=3, stride=2, use_se=0, use_hs=0):
        super(RepViTBlock, self).__init__()
        assert stride in [1, 2]
        self.identity = stride == 1 and inp == oup
        print(inp)
        print(hidden_dim)
        print(oup)
        assert(hidden_dim == 2 * inp)
        if stride == 2:
            self.token_mixer = nn.Sequential(
                Conv2d_BN(inp, inp, kernel_size, stride, (kernel_size - 1) // 2, groups=inp),
                SqueezeExcite(inp, 0.25) if use_se else nn.Identity(),
                Conv2d_BN(inp, oup, ks=1, stride=1, pad=0)
            )
            self.channel_mixer = Residual(nn.Sequential(
                    # pw
                    Conv2d_BN(oup, 2 * oup, 1, 1, 0),
                    nn.GELU() if use_hs else nn.GELU(),
                    # pw-linear
                    Conv2d_BN(2 * oup, oup, 1, 1, 0, bn_weight_init=0),
                ))
        else:
            assert(self.identity)
            self.token_mixer = nn.Sequential(
                RepVGGDW(inp),
                SqueezeExcite(inp, 0.25) if use_se else nn.Identity(),
            )
            self.channel_mixer = Residual(nn.Sequential(
                    # pw
                    Conv2d_BN(inp, hidden_dim, 1, 1, 0),
                    nn.GELU() if use_hs else nn.GELU(),
                    # pw-linear
                    Conv2d_BN(hidden_dim, oup, 1, 1, 0, bn_weight_init=0),
                ))
    def forward(self, x):
        return self.channel_mixer(self.token_mixer(x))

如下图所示：

第②步：修改yolo.py文件

首先找到yolo.py里面parse_model函数的这一行

加入 RepViTBlock这个模块

 第③步：创建自定义的yaml文件  

yaml文件完整代码：

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Parameters
nc: 1  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32
# CSPNet-v5
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 6, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, C3, [1024]],
   [-1, 1, SPPF, [1024, 5]],  # 9
  ]
# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 13
   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 14], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
   [-1, 1, RepViTBlock, [1024,1024,512]],  # 23 (P5/32-large)
   [[17, 20, 24], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

第④步：验证是否加入成功

运行yolo.py  

PS： 

这个也是，对我的数据集没涨点，而且我没觉得参数量小了。( ╯#-_-)╯┴—┴