YOLOv8改进 | 2023主干篇 | RepViT从视觉变换器（ViT）的视角重新审视CNN

2023-12-19 06:43:50

一、本文介绍

本文给大家来的改进机制是RepViT，用其替换我们整个主干网络，其是今年最新推出的主干网络，其主要思想是将轻量级视觉变换器（ViT）的设计原则应用于传统的轻量级卷积神经网络(CNN)。我将其替换整个YOLOv8的Backbone，实现了大幅度涨点。我对修改后的网络(我用的最轻量的版本)，在一个包含1000张图片包含大中小的检测目标的数据集上(共有20+类别)，进行训练测试，发现所有的目标上均有一定程度的涨点效果，下面我会附上基础版本和修改版本的训练对比图。

推荐指数：?????

专栏回顾：YOLOv8改进系列专栏——本专栏持续复习各种顶会内容——科研必备????

训练结果对比图->???

一、本文介绍

二、RepViT基本原理

三、RepViT的核心代码?

四、手把手教你添加RepViT网络结构

修改一

修改二

修改三?

修改四

修改五?

修改六?

修改七

修改八

五、RepViT的yaml文件

六、成功运行记录?

七、本文总结

二、RepViT基本原理

官方论文地址：?官方论文地址点击即可跳转

官方代码地址：?官方代码地址点击即可跳转

RepViT: Revisiting Mobile CNN From ViT Perspective 这篇论文探讨了如何改进轻量级卷积神经网络（CNN）以提高其在移动设备上的性能和效率。作者们发现，虽然轻量级视觉变换器（ViT）因其能够学习全局表示而表现出色，但轻量级CNN和轻量级ViT之间的架构差异尚未得到充分研究。因此，他们通过整合轻量级ViT的高效架构设计，逐步改进标准轻量级CNN（特别是MobileNetV3），从而创造了一系列全新的纯CNN模型，称为RepViT。这些模型在各种视觉任务上表现出色，比现有的轻量级ViT更高效。

其主要的改进机制包括：

结构性重组：通过结构性重组（Structural Re-parameterization, SR），引入多分支拓扑结构，以提高训练时的性能。
扩展比率调整：调整卷积层中的扩展比率，以减少参数冗余和延迟，同时提高网络宽度以增强模型性能。
宏观设计优化：对网络的宏观架构进行优化，包括早期卷积层的设计、更深的下采样层、简化的分类器，以及整体阶段比例的调整。
微观设计调整：在微观架构层面进行优化，包括卷积核大小的选择和压缩激励（SE）层的最佳放置。

这些创新机制共同推动了轻量级CNN的性能和效率，使其更适合在移动设备上使用，下面的是官方论文中的结构图，我们对其进行简单的分析。

这张图片是论文中的图3，展示了RepViT架构的总览。RepViT有四个阶段，输入图像的分辨率依次为

每个阶段的通道维度用 Ci? 表示，批处理大小用 B 表示。?

Stem：用于预处理输入图像的模块。
Stage1-4：每个阶段由多个RepViTBlock组成，以及一个可选的RepViTSEBlock，包含深度可分离卷积（3x3DW），1x1卷积，压缩激励模块（SE）和前馈网络（FFN）。每个阶段通过下采样减少空间维度。
Pooling：全局平均池化层，用于减少特征图的空间维度。
FC：全连接层，用于最终的类别预测。

?总结：大家可以将RepViT看成是MobileNet系列的改进版本

三、RepViT的核心代码?

下面的代码是整个RepViT的核心代码，其中有个版本，对应的GFLOPs也不相同，使用方式看章节四。

import torch.nn as nn
from timm.models.layers import SqueezeExcite
import torch

__all__ = ['repvit_m0_6','repvit_m0_9', 'repvit_m1_0', 'repvit_m1_1', 'repvit_m1_5', 'repvit_m2_3']

def _make_divisible(v, divisor, min_value=None):
    """
    This function is taken from the original tf repo.
    It ensures that all layers have a channel number that is divisible by 8
    It can be seen here:
    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
    :param v:
    :param divisor:
    :param min_value:
    :return:
    """
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


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(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(self):
        if isinstance(self.m, Conv2d_BN):
            m = self.m.fuse_self()
            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 = torch.nn.Conv2d(ed, ed, 1, 1, 0, groups=ed)
        self.dim = ed
        self.bn = torch.nn.BatchNorm2d(ed)

    def forward(self, x):
        return self.bn((self.conv(x) + self.conv1(x)) + x)

    @torch.no_grad()
    def fuse_self(self):
        conv = self.conv.fuse_self()
        conv1 = self.conv1

        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)

        bn = self.bn
        w = bn.weight / (bn.running_var + bn.eps) ** 0.5
        w = conv.weight * w[:, None, None, None]
        b = bn.bias + (conv.bias - bn.running_mean) * bn.weight / \
            (bn.running_var + bn.eps) ** 0.5
        conv.weight.data.copy_(w)
        conv.bias.data.copy_(b)
        return conv


class RepViTBlock(nn.Module):
    def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs):
        super(RepViTBlock, self).__init__()
        assert stride in [1, 2]

        self.identity = stride == 1 and inp == 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))


class RepViT(nn.Module):
    def __init__(self, cfgs):
        super(RepViT, self).__init__()
        # setting of inverted residual blocks
        self.cfgs = cfgs

        # building first layer
        input_channel = self.cfgs[0][2]
        patch_embed = torch.nn.Sequential(Conv2d_BN(3, input_channel // 2, 3, 2, 1), torch.nn.GELU(),
                                          Conv2d_BN(input_channel // 2, input_channel, 3, 2, 1))
        layers = [patch_embed]
        # building inverted residual blocks
        block = RepViTBlock
        for k, t, c, use_se, use_hs, s in self.cfgs:
            output_channel = _make_divisible(c, 8)
            exp_size = _make_divisible(input_channel * t, 8)
            layers.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs))
            input_channel = output_channel
        self.features = nn.ModuleList(layers)
        self.width_list = [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]

    def forward(self, x):
        # x = self.features(x
        results = [None, None, None, None]
        temp = None
        i = None
        for index, f in enumerate(self.features):
            x = f(x)
            if index == 0:
                temp = x.size(1)
                i = 0
            elif x.size(1) == temp:
                results[i] = x
            else:
                temp = x.size(1)
                i = i + 1
        return results



def repvit_m0_6():
    """
    Constructs a MobileNetV3-Large model
    """
    cfgs = [
        [3,   2,  40, 1, 0, 1],
        [3,   2,  40, 0, 0, 1],
        [3,   2,  80, 0, 0, 2],
        [3,   2,  80, 1, 0, 1],
        [3,   2,  80, 0, 0, 1],
        [3,   2,  160, 0, 1, 2],
        [3,   2, 160, 1, 1, 1],
        [3,   2, 160, 0, 1, 1],
        [3,   2, 160, 1, 1, 1],
        [3,   2, 160, 0, 1, 1],
        [3,   2, 160, 1, 1, 1],
        [3,   2, 160, 0, 1, 1],
        [3,   2, 160, 1, 1, 1],
        [3,   2, 160, 0, 1, 1],
        [3,   2, 160, 0, 1, 1],
        [3,   2, 320, 0, 1, 2],
        [3,   2, 320, 1, 1, 1],
    ]
    model = RepViT(cfgs)
    return model


def repvit_m0_9():
    """
    Constructs a MobileNetV3-Large model
    """
    cfgs = [
        # k, t, c, SE, HS, s
        [3, 2, 48, 1, 0, 1],
        [3, 2, 48, 0, 0, 1],
        [3, 2, 48, 0, 0, 1],
        [3, 2, 96, 0, 0, 2],
        [3, 2, 96, 1, 0, 1],
        [3, 2, 96, 0, 0, 1],
        [3, 2, 96, 0, 0, 1],
        [3, 2, 192, 0, 1, 2],
        [3, 2, 192, 1, 1, 1],
        [3, 2, 192, 0, 1, 1],
        [3, 2, 192, 1, 1, 1],
        [3, 2, 192, 0, 1, 1],
        [3, 2, 192, 1, 1, 1],
        [3, 2, 192, 0, 1, 1],
        [3, 2, 192, 1, 1, 1],
        [3, 2, 192, 0, 1, 1],
        [3, 2, 192, 1, 1, 1],
        [3, 2, 192, 0, 1, 1],
        [3, 2, 192, 1, 1, 1],
        [3, 2, 192, 0, 1, 1],
        [3, 2, 192, 1, 1, 1],
        [3, 2, 192, 0, 1, 1],
        [3, 2, 192, 0, 1, 1],
        [3, 2, 384, 0, 1, 2],
        [3, 2, 384, 1, 1, 1],
        [3, 2, 384, 0, 1, 1]
    ]
    model = RepViT(cfgs)
    return model


def repvit_m1_0():
    """
    Constructs a MobileNetV3-Large model
    """
    cfgs = [
        # k, t, c, SE, HS, s
        [3, 2, 56, 1, 0, 1],
        [3, 2, 56, 0, 0, 1],
        [3, 2, 56, 0, 0, 1],
        [3, 2, 112, 0, 0, 2],
        [3, 2, 112, 1, 0, 1],
        [3, 2, 112, 0, 0, 1],
        [3, 2, 112, 0, 0, 1],
        [3, 2, 224, 0, 1, 2],
        [3, 2, 224, 1, 1, 1],
        [3, 2, 224, 0, 1, 1],
        [3, 2, 224, 1, 1, 1],
        [3, 2, 224, 0, 1, 1],
        [3, 2, 224, 1, 1, 1],
        [3, 2, 224, 0, 1, 1],
        [3, 2, 224, 1, 1, 1],
        [3, 2, 224, 0, 1, 1],
        [3, 2, 224, 1, 1, 1],
        [3, 2, 224, 0, 1, 1],
        [3, 2, 224, 1, 1, 1],
        [3, 2, 224, 0, 1, 1],
        [3, 2, 224, 1, 1, 1],
        [3, 2, 224, 0, 1, 1],
        [3, 2, 224, 0, 1, 1],
        [3, 2, 448, 0, 1, 2],
        [3, 2, 448, 1, 1, 1],
        [3, 2, 448, 0, 1, 1]
    ]
    model = RepViT(cfgs)
    return model


def repvit_m1_1():
    """
    Constructs a MobileNetV3-Large model
    """
    cfgs = [
        # k, t, c, SE, HS, s
        [3, 2, 64, 1, 0, 1],
        [3, 2, 64, 0, 0, 1],
        [3, 2, 64, 0, 0, 1],
        [3, 2, 128, 0, 0, 2],
        [3, 2, 128, 1, 0, 1],
        [3, 2, 128, 0, 0, 1],
        [3, 2, 128, 0, 0, 1],
        [3, 2, 256, 0, 1, 2],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 512, 0, 1, 2],
        [3, 2, 512, 1, 1, 1],
        [3, 2, 512, 0, 1, 1]
    ]
    model = RepViT(cfgs)
    return model


def repvit_m1_5():
    """
    Constructs a MobileNetV3-Large model
    """
    cfgs = [
        # k, t, c, SE, HS, s
        [3, 2, 64, 1, 0, 1],
        [3, 2, 64, 0, 0, 1],
        [3, 2, 64, 1, 0, 1],
        [3, 2, 64, 0, 0, 1],
        [3, 2, 64, 0, 0, 1],
        [3, 2, 128, 0, 0, 2],
        [3, 2, 128, 1, 0, 1],
        [3, 2, 128, 0, 0, 1],
        [3, 2, 128, 1, 0, 1],
        [3, 2, 128, 0, 0, 1],
        [3, 2, 128, 0, 0, 1],
        [3, 2, 256, 0, 1, 2],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 1, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 256, 0, 1, 1],
        [3, 2, 512, 0, 1, 2],
        [3, 2, 512, 1, 1, 1],
        [3, 2, 512, 0, 1, 1],
        [3, 2, 512, 1, 1, 1],
        [3, 2, 512, 0, 1, 1]
    ]
    model = RepViT(cfgs)
    return model


def repvit_m2_3():
    """
    Constructs a MobileNetV3-Large model
    """
    cfgs = [
        # k, t, c, SE, HS, s
        [3, 2, 80, 1, 0, 1],
        [3, 2, 80, 0, 0, 1],
        [3, 2, 80, 1, 0, 1],
        [3, 2, 80, 0, 0, 1],
        [3, 2, 80, 1, 0, 1],
        [3, 2, 80, 0, 0, 1],
        [3, 2, 80, 0, 0, 1],
        [3, 2, 160, 0, 0, 2],
        [3, 2, 160, 1, 0, 1],
        [3, 2, 160, 0, 0, 1],
        [3, 2, 160, 1, 0, 1],
        [3, 2, 160, 0, 0, 1],
        [3, 2, 160, 1, 0, 1],
        [3, 2, 160, 0, 0, 1],
        [3, 2, 160, 0, 0, 1],
        [3, 2, 320, 0, 1, 2],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 320, 1, 1, 1],
        [3, 2, 320, 0, 1, 1],
        # [3,   2, 320, 1, 1, 1],
        # [3,   2, 320, 0, 1, 1],
        [3, 2, 320, 0, 1, 1],
        [3, 2, 640, 0, 1, 2],
        [3, 2, 640, 1, 1, 1],
        [3, 2, 640, 0, 1, 1],
        # [3,   2, 640, 1, 1, 1],
        # [3,   2, 640, 0, 1, 1]
    ]
    model = RepViT(cfgs)
    return model

四、手把手教你添加RepViT网络结构

这个主干的网络结构添加起来算是所有的改进机制里最麻烦的了，因为有一些网略结构可以用yaml文件搭建出来，有一些网络结构其中的一些细节根本没有办法用yaml文件去搭建，用yaml文件去搭建会损失一些细节部分(而且一个网络结构设计很多细节的结构修改方式都不一样，一个一个去修改大家难免会出错)，所以这里让网络直接返回整个网络，然后修改部分 yolo代码以后就都以这种形式添加了，以后我提出的网络模型基本上都会通过这种方式修改，我也会进行一些模型细节改进。创新出新的网络结构大家直接拿来用就可以的。下面开始添加教程->

(同时每一个后面都有代码，大家拿来复制粘贴替换即可，但是要看好了不要复制粘贴替换多了)

修改一

我们复制网络结构代码到“ultralytics/nn/modules”目录下创建一个py文件复制粘贴进去，我这里起的名字是RepViT。

修改二

找到如下的文件"ultralytics/nn/tasks.py" 在开始的部分导入我们的模型如下图。

from .modules.RepViT import repvit_m0_6, repvit_m0_9, repvit_m1_0, repvit_m1_1, repvit_m1_5, repvit_m2_3

修改三?

添加如下两行代码！！！

修改四

找到七百多行大概把具体看图片，按照图片来修改就行，添加红框内的部分，注意没有()只是函数名。

        elif m in {repvit_m0_6, repvit_m0_9, repvit_m1_0, repvit_m1_1, repvit_m1_5, repvit_m2_3}:
            m = m()
            c2 = m.width_list  # 返回通道列表
            backbone = True

修改五?

下面的两个红框内都是需要改动的。?

        if isinstance(c2, list):
            m_ = m
            m_.backbone = True
        else:
            m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
            t = str(m)[8:-2].replace('__main__.', '')  # module type


        m.np = sum(x.numel() for x in m_.parameters())  # number params
        m_.i, m_.f, m_.type = i + 4 if backbone else i, f, t  # attach index, 'from' index, type

修改六?

如下的也需要修改，全部按照我的来。

代码如下把原先的代码替换了即可。?

        if verbose:
            LOGGER.info(f'{i:>3}{str(f):>20}{n_:>3}{m.np:10.0f}  {t:<45}{str(args):<30}')  # print

        save.extend(x % (i + 4 if backbone else i) for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        layers.append(m_)
        if i == 0:
            ch = []
        if isinstance(c2, list):
            ch.extend(c2)
            if len(c2) != 5:
                ch.insert(0, 0)
        else:
            ch.append(c2)

修改七

修改七和前面的都不太一样，需要修改前向传播中的一个部分，?已经离开了parse_model方法了。

可以在图片中开代码行数，没有离开task.py文件都是同一个文件。同时这个部分有好几个前向传播都很相似，大家不要看错了，是70多行左右的！！！，同时我后面提供了代码，大家直接复制粘贴即可，有时间我针对这里会出一个视频。

代码如下->

    def _predict_once(self, x, profile=False, visualize=False):
        """
        Perform a forward pass through the network.

        Args:
            x (torch.Tensor): The input tensor to the model.
            profile (bool):  Print the computation time of each layer if True, defaults to False.
            visualize (bool): Save the feature maps of the model if True, defaults to False.

        Returns:
            (torch.Tensor): The last output of the model.
        """
        y, dt = [], []  # outputs
        for m in self.model:
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
            if profile:
                self._profile_one_layer(m, x, dt)
            if hasattr(m, 'backbone'):
                x = m(x)
                if len(x) != 5: # 0 - 5
                    x.insert(0, None)
                for index, i in enumerate(x):
                    if index in self.save:
                        y.append(i)
                    else:
                        y.append(None)
                x = x[-1] # 最后一个输出传给下一层
            else:
                x = m(x)  # run
                y.append(x if m.i in self.save else None)  # save output
            if visualize:
                feature_visualization(x, m.type, m.i, save_dir=visualize)
        return x

到这里就完成了修改部分，但是这里面细节很多，大家千万要注意不要替换多余的代码，导致报错，也不要拉下任何一部，都会导致运行失败，而且报错很难排查！！！很难排查！！！?

修改八

这个Swin Transformer和其他的不太一样会导致计算的GFLOPs计算异常，所以需要额外修改一处，?我们找到如下文件'ultralytics/utils/torch_utils.py'按照如下的图片进行修改。

五、RepViT的yaml文件

复制如下yaml文件进行运行！！！?

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

# Parameters
nc: 80  # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPs
  s: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPs
  m: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPs
  l: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
  x: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs

# YOLOv8.0n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, repvit_m0_9, []]  # 4
  - [-1, 1, SPPF, [1024, 5]]  # 5

# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']] # 6
  - [[-1, 3], 1, Concat, [1]]  # 7 cat backbone P4
  - [-1, 3, C2f, [512]]  # 8

  - [-1, 1, nn.Upsample, [None, 2, 'nearest']] # 9
  - [[-1, 2], 1, Concat, [1]]  # 10 cat backbone P3
  - [-1, 3, C2f, [256]]  # 11 (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]] # 12
  - [[-1, 8], 1, Concat, [1]]  # 13 cat head P4
  - [-1, 3, C2f, [512]]  # 14 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]] # 15
  - [[-1, 5], 1, Concat, [1]]  # 16 cat head P5
  - [-1, 3, C2f, [1024]]  # 17 (P5/32-large)

  - [[11, 14, 17], 1, Detect, [nc]]  # Detect(P3, P4, P5)

六、成功运行记录?

下面是成功运行的截图，已经完成了有1个epochs的训练，图片太大截不全第2个epochs了。??

七、本文总结

到此本文的正式分享内容就结束了，在这里给大家推荐我的YOLOv8改进有效涨点专栏，本专栏目前为新开的平均质量分98分，后期我会根据各种最新的前沿顶会进行论文复现，也会对一些老的改进机制进行补充，目前本专栏免费阅读(暂时，大家尽早关注不迷路~)，如果大家觉得本文帮助到你了，订阅本专栏，关注后续更多的更新~

专栏回顾：YOLOv8改进系列专栏——本专栏持续复习各种顶会内容——科研必备

文章来源:https://blog.csdn.net/java1314777/article/details/135052550
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