YOLO11改进 | 主干网络 | 简单而优雅且有效的VanillaNet 【华为诺亚方舟】

kay_5452024-10-26 20:37

秋招面试专栏推荐 ：深度学习算法工程师面试问题总结【百面算法工程师】------点击即可跳转

💡💡💡本专栏所有程序均经过测试，可成功执行💡💡💡

本文给大家带来的教程是将YOLO11的backbone替换为VanillaNet 结构来提取特征。文章在介绍主要的原理后，将手把手教学如何进行模块的代码添加和修改，并将修改后的完整代码放在文章的最后，方便大家一键运行，小白也可轻松上手实践。以帮助您更好地学习深度学习目标检测YOLO系列的挑战。

专栏地址：************YOLO11入门 + 改进涨点------点击即可跳转 欢迎订阅****************

目录

1.论文

[2. VanillaNet 代码实现](#2. VanillaNet 代码实现)

[2.1 将VanillaNet 添加到YOLO11中](#2.1 将VanillaNet 添加到YOLO11中)

[2.2 更改init.py文件](#2.2 更改init.py文件)

[2.3 添加yaml文件](#2.3 添加yaml文件)

[2.4 注册模块](#2.4 注册模块)

[2.5 替换函数](#2.5 替换函数)

[2.6 执行程序](#2.6 执行程序)

3.修改后的网络结构图

[4. 完整代码分享](#4. 完整代码分享)

[5. GFLOPs](#5. GFLOPs)

[6. 进阶](#6. 进阶)

7.总结

1.论文

​

论文地址： VanillaNet: the Power of Minimalism in Deep Learning------点击即可跳转

官方代码： 官方代码仓库------点击即可跳转

2. VanillaNet 代码实现

2.1 将VanillaNet 添加到YOLO11中

**关键步骤一：**将下面代码粘贴到在/ultralytics/ultralytics/nn/modules/block.py中

python 复制代码 
#Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.

#This program is free software; you can redistribute it and/or modify it under the terms of the MIT License.

#This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the MIT License for more details.

import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import weight_init, DropPath
import numpy as np

__all__ = ['vanillanet_5', 'vanillanet_6', 'vanillanet_7', 'vanillanet_8', 'vanillanet_9', 'vanillanet_10', 'vanillanet_11', 'vanillanet_12', 'vanillanet_13', 'vanillanet_13_x1_5', 'vanillanet_13_x1_5_ada_pool']

class activation(nn.ReLU):
    def __init__(self, dim, act_num=3, deploy=False):
        super(activation, self).__init__()
        self.deploy = deploy
        self.weight = torch.nn.Parameter(torch.randn(dim, 1, act_num*2 + 1, act_num*2 + 1))
        self.bias = None
        self.bn = nn.BatchNorm2d(dim, eps=1e-6)
        self.dim = dim
        self.act_num = act_num
        weight_init.trunc_normal_(self.weight, std=.02)

    def forward(self, x):
        if self.deploy:
            return torch.nn.functional.conv2d(
                super(activation, self).forward(x), 
                self.weight, self.bias, padding=(self.act_num*2 + 1)//2, groups=self.dim)
        else:
            return self.bn(torch.nn.functional.conv2d(
                super(activation, self).forward(x),
                self.weight, padding=self.act_num, groups=self.dim))

    def _fuse_bn_tensor(self, weight, bn):
        kernel = weight
        running_mean = bn.running_mean
        running_var = bn.running_var
        gamma = bn.weight
        beta = bn.bias
        eps = bn.eps
        std = (running_var + eps).sqrt()
        t = (gamma / std).reshape(-1, 1, 1, 1)
        return kernel * t, beta + (0 - running_mean) * gamma / std
    
    def switch_to_deploy(self):
        if not self.deploy:
            kernel, bias = self._fuse_bn_tensor(self.weight, self.bn)
            self.weight.data = kernel
            self.bias = torch.nn.Parameter(torch.zeros(self.dim))
            self.bias.data = bias
            self.__delattr__('bn')
            self.deploy = True


class VanillaBlock(nn.Module):
    def __init__(self, dim, dim_out, act_num=3, stride=2, deploy=False, ada_pool=None):
        super().__init__()
        self.act_learn = 1
        self.deploy = deploy
        if self.deploy:
            self.conv = nn.Conv2d(dim, dim_out, kernel_size=1)
        else:
            self.conv1 = nn.Sequential(
                nn.Conv2d(dim, dim, kernel_size=1),
                nn.BatchNorm2d(dim, eps=1e-6),
            )
            self.conv2 = nn.Sequential(
                nn.Conv2d(dim, dim_out, kernel_size=1),
                nn.BatchNorm2d(dim_out, eps=1e-6)
            )

        if not ada_pool:
            self.pool = nn.Identity() if stride == 1 else nn.MaxPool2d(stride)
        else:
            self.pool = nn.Identity() if stride == 1 else nn.AdaptiveMaxPool2d((ada_pool, ada_pool))

        self.act = activation(dim_out, act_num)
 
    def forward(self, x):
        if self.deploy:
            x = self.conv(x)
        else:
            x = self.conv1(x)
            x = torch.nn.functional.leaky_relu(x,self.act_learn)
            x = self.conv2(x)

        x = self.pool(x)
        x = self.act(x)
        return x

    def _fuse_bn_tensor(self, conv, bn):
        kernel = conv.weight
        bias = conv.bias
        running_mean = bn.running_mean
        running_var = bn.running_var
        gamma = bn.weight
        beta = bn.bias
        eps = bn.eps
        std = (running_var + eps).sqrt()
        t = (gamma / std).reshape(-1, 1, 1, 1)
        return kernel * t, beta + (bias - running_mean) * gamma / std
    
    def switch_to_deploy(self):
        if not self.deploy:
            kernel, bias = self._fuse_bn_tensor(self.conv1[0], self.conv1[1])
            self.conv1[0].weight.data = kernel
            self.conv1[0].bias.data = bias
            # kernel, bias = self.conv2[0].weight.data, self.conv2[0].bias.data
            kernel, bias = self._fuse_bn_tensor(self.conv2[0], self.conv2[1])
            self.conv = self.conv2[0]
            self.conv.weight.data = torch.matmul(kernel.transpose(1,3), self.conv1[0].weight.data.squeeze(3).squeeze(2)).transpose(1,3)
            self.conv.bias.data = bias + (self.conv1[0].bias.data.view(1,-1,1,1)*kernel).sum(3).sum(2).sum(1)
            self.__delattr__('conv1')
            self.__delattr__('conv2')
            self.act.switch_to_deploy()
            self.deploy = True
    

class VanillaNet(nn.Module):
    def __init__(self, in_chans=3, num_classes=1000, dims=[96, 192, 384, 768], 
                 drop_rate=0, act_num=3, strides=[2,2,2,1], deploy=False, ada_pool=None, **kwargs):
        super().__init__()
        self.deploy = deploy
        if self.deploy:
            self.stem = nn.Sequential(
                nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
                activation(dims[0], act_num)
            )
        else:
            self.stem1 = nn.Sequential(
                nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
                nn.BatchNorm2d(dims[0], eps=1e-6),
            )
            self.stem2 = nn.Sequential(
                nn.Conv2d(dims[0], dims[0], kernel_size=1, stride=1),
                nn.BatchNorm2d(dims[0], eps=1e-6),
                activation(dims[0], act_num)
            )

        self.act_learn = 1

        self.stages = nn.ModuleList()
        for i in range(len(strides)):
            if not ada_pool:
                stage = VanillaBlock(dim=dims[i], dim_out=dims[i+1], act_num=act_num, stride=strides[i], deploy=deploy)
            else:
                stage = VanillaBlock(dim=dims[i], dim_out=dims[i+1], act_num=act_num, stride=strides[i], deploy=deploy, ada_pool=ada_pool[i])
            self.stages.append(stage)
        self.depth = len(strides)

        self.apply(self._init_weights)
        self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]

    def _init_weights(self, m):
        if isinstance(m, (nn.Conv2d, nn.Linear)):
            weight_init.trunc_normal_(m.weight, std=.02)
            nn.init.constant_(m.bias, 0)

    def change_act(self, m):
        for i in range(self.depth):
            self.stages[i].act_learn = m
        self.act_learn = m

    def forward(self, x):
        res = []
        if self.deploy:
            x = self.stem(x)
        else:
            x = self.stem1(x)
            x = torch.nn.functional.leaky_relu(x,self.act_learn)
            x = self.stem2(x)
        res.append(x)
        for i in range(self.depth):
            x = self.stages[i](x)
            res.append(x)
        return res

    def _fuse_bn_tensor(self, conv, bn):
        kernel = conv.weight
        bias = conv.bias
        running_mean = bn.running_mean
        running_var = bn.running_var
        gamma = bn.weight
        beta = bn.bias
        eps = bn.eps
        std = (running_var + eps).sqrt()
        t = (gamma / std).reshape(-1, 1, 1, 1)
        return kernel * t, beta + (bias - running_mean) * gamma / std
    
    def switch_to_deploy(self):
        if not self.deploy:
            self.stem2[2].switch_to_deploy()
            kernel, bias = self._fuse_bn_tensor(self.stem1[0], self.stem1[1])
            self.stem1[0].weight.data = kernel
            self.stem1[0].bias.data = bias
            kernel, bias = self._fuse_bn_tensor(self.stem2[0], self.stem2[1])
            self.stem1[0].weight.data = torch.einsum('oi,icjk->ocjk', kernel.squeeze(3).squeeze(2), self.stem1[0].weight.data)
            self.stem1[0].bias.data = bias + (self.stem1[0].bias.data.view(1,-1,1,1)*kernel).sum(3).sum(2).sum(1)
            self.stem = torch.nn.Sequential(*[self.stem1[0], self.stem2[2]])
            self.__delattr__('stem1')
            self.__delattr__('stem2')

            for i in range(self.depth):
                self.stages[i].switch_to_deploy()

            self.deploy = True

def update_weight(model_dict, weight_dict):
    idx, temp_dict = 0, {}
    for k, v in weight_dict.items():
        if k in model_dict.keys() and np.shape(model_dict[k]) == np.shape(v):
            temp_dict[k] = v
            idx += 1
    model_dict.update(temp_dict)
    print(f'loading weights... {idx}/{len(model_dict)} items')
    return model_dict

def vanillanet_5(pretrained='',in_22k=False, **kwargs):
    model = VanillaNet(dims=[128//2, 256//2, 512//2, 1024//2], strides=[2,2,2], **kwargs)
    if pretrained:
        weights = torch.load(pretrained)['model_ema']
        model.load_state_dict(update_weight(model.state_dict(), weights))
    return model

def vanillanet_6(pretrained='',in_22k=False, **kwargs):
    model = VanillaNet(dims=[128*4, 256*4, 512*4, 1024*4, 1024*4], strides=[2,2,2,1], **kwargs)
    if pretrained:
        weights = torch.load(pretrained)['model_ema']
        model.load_state_dict(update_weight(model.state_dict(), weights))
    return model

def vanillanet_7(pretrained='',in_22k=False, **kwargs):
    model = VanillaNet(dims=[128*4, 128*4, 256*4, 512*4, 1024*4, 1024*4], strides=[1,2,2,2,1], **kwargs)
    if pretrained:
        weights = torch.load(pretrained)['model_ema']
        model.load_state_dict(update_weight(model.state_dict(), weights))
    return model

def vanillanet_8(pretrained='', in_22k=False, **kwargs):
    model = VanillaNet(dims=[128*4, 128*4, 256*4, 512*4, 512*4, 1024*4, 1024*4], strides=[1,2,2,1,2,1], **kwargs)
    if pretrained:
        weights = torch.load(pretrained)['model_ema']
        model.load_state_dict(update_weight(model.state_dict(), weights))
    return model

def vanillanet_9(pretrained='', in_22k=False, **kwargs):
    model = VanillaNet(dims=[128*4, 128*4, 256*4, 512*4, 512*4, 512*4, 1024*4, 1024*4], strides=[1,2,2,1,1,2,1], **kwargs)
    if pretrained:
        weights = torch.load(pretrained)['model_ema']
        model.load_state_dict(update_weight(model.state_dict(), weights))
    return model

def vanillanet_10(pretrained='', in_22k=False, **kwargs):
    model = VanillaNet(
        dims=[128*4, 128*4, 256*4, 512*4, 512*4, 512*4, 512*4, 1024*4, 1024*4],
        strides=[1,2,2,1,1,1,2,1],
        **kwargs)
    if pretrained:
        weights = torch.load(pretrained)['model_ema']
        model.load_state_dict(update_weight(model.state_dict(), weights))
    return model

def vanillanet_11(pretrained='', in_22k=False, **kwargs):
    model = VanillaNet(
        dims=[128*4, 128*4, 256*4, 512*4, 512*4, 512*4, 512*4, 512*4, 1024*4, 1024*4],
        strides=[1,2,2,1,1,1,1,2,1],
        **kwargs)
    if pretrained:
        weights = torch.load(pretrained)['model_ema']
        model.load_state_dict(update_weight(model.state_dict(), weights))
    return model

def vanillanet_12(pretrained='', in_22k=False, **kwargs):
    model = VanillaNet(
        dims=[128*4, 128*4, 256*4, 512*4, 512*4, 512*4, 512*4, 512*4, 512*4, 1024*4, 1024*4],
        strides=[1,2,2,1,1,1,1,1,2,1],
        **kwargs)
    if pretrained:
        weights = torch.load(pretrained)['model_ema']
        model.load_state_dict(update_weight(model.state_dict(), weights))
    return model

def vanillanet_13(pretrained='', in_22k=False, **kwargs):
    model = VanillaNet(
        dims=[128*4, 128*4, 256*4, 512*4, 512*4, 512*4, 512*4, 512*4, 512*4, 512*4, 1024*4, 1024*4],
        strides=[1,2,2,1,1,1,1,1,1,2,1],
        **kwargs)
    if pretrained:
        weights = torch.load(pretrained)['model_ema']
        model.load_state_dict(update_weight(model.state_dict(), weights))
    return model

def vanillanet_13_x1_5(pretrained='', in_22k=False, **kwargs):
    model = VanillaNet(
        dims=[128*6, 128*6, 256*6, 512*6, 512*6, 512*6, 512*6, 512*6, 512*6, 512*6, 1024*6, 1024*6],
        strides=[1,2,2,1,1,1,1,1,1,2,1],
        **kwargs)
    if pretrained:
        weights = torch.load(pretrained)['model_ema']
        model.load_state_dict(update_weight(model.state_dict(), weights))
    return model

def vanillanet_13_x1_5_ada_pool(pretrained='', in_22k=False, **kwargs):
    model = VanillaNet(
        dims=[128*6, 128*6, 256*6, 512*6, 512*6, 512*6, 512*6, 512*6, 512*6, 512*6, 1024*6, 1024*6],
        strides=[1,2,2,1,1,1,1,1,1,2,1],
        ada_pool=[0,40,20,0,0,0,0,0,0,10,0],
        **kwargs)
    if pretrained:
        weights = torch.load(pretrained)['model_ema']
        model.load_state_dict(update_weight(model.state_dict(), weights))
    return model

2.2 更改init.py文件

**关键步骤二：**修改modules文件夹下的__init__.py文件，先导入函数

​

然后在下面的__all__中声明函数

2.3 添加yaml文件

**关键步骤三：**在/ultralytics/ultralytics/cfg/models/11下面新建文件yolo11_VanillaNet .yaml文件，粘贴下面的内容

  • 目标检测
python 复制代码 
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 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=yolo11n.yaml' will call yolo11.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
  s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
  m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
  l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
  x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs

# 0-P1/2
# 1-P2/4
# 2-P3/8
# 3-P4/16
# 4-P5/32
 
# YOLOv8.0n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, vanillanet_5, []]  # 4      
  - [-1, 1, SPPF, [1024, 5]]  # 5                   

# YOLO11n head
head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 3], 1, Concat, [1]] # cat backbone P4
  - [-1, 2, C3k2, [512, False]] # 13

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 2], 1, Concat, [1]] # cat backbone P3
  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 8], 1, Concat, [1]] # cat head P4
  - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 5], 1, Concat, [1]] # cat head P5
  - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)

  - [[11, 14, 17], 1, Detect, [nc]] # Detect(P3, P4, P5)
  • 语义分割
python 复制代码 
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 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=yolo11n.yaml' will call yolo11.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
  s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
  m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
  l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
  x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs

# 0-P1/2
# 1-P2/4
# 2-P3/8
# 3-P4/16
# 4-P5/32
 
# YOLOv8.0n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, vanillanet_5, []]  # 4      
  - [-1, 1, SPPF, [1024, 5]]  # 5                   

# YOLO11n head
head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 3], 1, Concat, [1]] # cat backbone P4
  - [-1, 2, C3k2, [512, False]] # 13

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 2], 1, Concat, [1]] # cat backbone P3
  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 8], 1, Concat, [1]] # cat head P4
  - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 5], 1, Concat, [1]] # cat head P5
  - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)

  - [[11, 14, 17], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)
  • 旋转目标检测
python 复制代码 
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 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=yolo11n.yaml' will call yolo11.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
  s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
  m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
  l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
  x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs

# 0-P1/2
# 1-P2/4
# 2-P3/8
# 3-P4/16
# 4-P5/32
 
# YOLOv8.0n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, vanillanet_5, []]  # 4      
  - [-1, 1, SPPF, [1024, 5]]  # 5                   

# YOLO11n head
head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 3], 1, Concat, [1]] # cat backbone P4
  - [-1, 2, C3k2, [512, False]] # 13

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 2], 1, Concat, [1]] # cat backbone P3
  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 8], 1, Concat, [1]] # cat head P4
  - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 5], 1, Concat, [1]] # cat head P5
  - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)

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

温馨提示：本文只是对yolo11基础上添加模块，如果要对yolo11n/l/m/x进行添加则只需要指定对应的depth_multiple 和 width_multiple

python 复制代码 
# YOLO11n
depth_multiple: 0.50  # model depth multiple
width_multiple: 0.25  # layer channel multiple
max_channel：1024
 
# YOLO11s
depth_multiple: 0.50  # model depth multiple
width_multiple: 0.50  # layer channel multiple
max_channel：1024
 
# YOLO11m
depth_multiple: 0.50  # model depth multiple
width_multiple: 1.00  # layer channel multiple
max_channel：512
 
# YOLO11l 
depth_multiple: 1.00  # model depth multiple
width_multiple: 1.00  # layer channel multiple
max_channel：512 
 
# YOLO11x
depth_multiple: 1.00  # model depth multiple
width_multiple: 1.50 # layer channel multiple
max_channel：512

2.4 注册模块

**关键步骤四：**在task.py的parse_model函数替换为下面的内容

先在task.py导入函数

然后在task.py文件下找到parse_model这个函数替换

python 复制代码 
def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
    """Parse a YOLO model.yaml dictionary into a PyTorch model."""
    import ast
 
    # Args
    max_channels = float("inf")
    nc, act, scales = (d.get(x) for x in ("nc", "activation", "scales"))
    depth, width, kpt_shape = (d.get(x, 1.0) for x in ("depth_multiple", "width_multiple", "kpt_shape"))
    if scales:
        scale = d.get("scale")
        if not scale:
            scale = tuple(scales.keys())[0]
            LOGGER.warning(f"WARNING ⚠️ no model scale passed. Assuming scale='{scale}'.")
        depth, width, max_channels = scales[scale]
 
    if act:
        Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
        if verbose:
            LOGGER.info(f"{colorstr('activation:')} {act}")  # print
 
    if verbose:
        LOGGER.info(f"\n{'':>3}{'from':>20}{'n':>3}{'params':>10}  {'module':<45}{'arguments':<30}")
    ch = [ch]
    is_backbone = False
    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]):  # from, number, module, args
        m = getattr(torch.nn, m[3:]) if "nn." in m else globals()[m]  # get module
        for j, a in enumerate(args):
            if isinstance(a, str):
                with contextlib.suppress(ValueError):
                    args[j] = locals()[a] if a in locals() else ast.literal_eval(a)
 
        n = n_ = max(round(n * depth), 1) if n > 1 else n  # depth gain
        if m in {
            Classify,
            Conv,
            ConvTranspose,
            GhostConv,
            Bottleneck,
            GhostBottleneck,
            SPP,
            SPPF,
            C2fPSA,
            C2PSA,
            DWConv,
            Focus,
            BottleneckCSP,
            C1,
            C2,
            C2f,
            C3k2,
            RepNCSPELAN4,
            ELAN1,
            ADown,
            AConv,
            SPPELAN,
            C2fAttn,
            C3,
            C3TR,
            C3Ghost,
            nn.ConvTranspose2d,
            DWConvTranspose2d,
            C3x,
            RepC3,
            PSA,
            SCDown,
            C2fCIB,
        }:
            c1, c2 = ch[f], args[0]
            if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
                c2 = make_divisible(min(c2, max_channels) * width, 8)
            if m is C2fAttn:
                args[1] = make_divisible(min(args[1], max_channels // 2) * width, 8)  # embed channels
                args[2] = int(
                    max(round(min(args[2], max_channels // 2 // 32)) * width, 1) if args[2] > 1 else args[2]
                )  # num heads
 
            args = [c1, c2, *args[1:]]
            if m in {
                BottleneckCSP,
                C1,
                C2,
                C2f,
                C3k2,
                C2fAttn,
                C3,
                C3TR,
                C3Ghost,
                C3x,
                RepC3,
                C2fPSA,
                C2fCIB,
                C2PSA,
            }:
                args.insert(2, n)  # number of repeats
                n = 1
            if m is C3k2 and scale in "mlx":  # for M/L/X sizes
                args[3] = True
        elif m is AIFI:
            args = [ch[f], *args]
        elif m in {HGStem, HGBlock}:
            c1, cm, c2 = ch[f], args[0], args[1]
            args = [c1, cm, c2, *args[2:]]
            if m is HGBlock:
                args.insert(4, n)  # number of repeats
                n = 1
        elif m in (vanillanet_5, vanillanet_6, vanillanet_7, vanillanet_8, vanillanet_9, vanillanet_10, vanillanet_11, vanillanet_12, vanillanet_13, vanillanet_13_x1_5, vanillanet_13_x1_5_ada_pool):
            m = m(*args)
            c2 = m.channel
        elif m is ResNetLayer:
            c2 = args[1] if args[3] else args[1] * 4
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum(ch[x] for x in f)
        elif m in {Detect, WorldDetect, Segment, Pose, OBB, ImagePoolingAttn, v10Detect}:
            args.append([ch[x] for x in f])
            if m is Segment:
                args[2] = make_divisible(min(args[2], max_channels) * width, 8)
        elif m is RTDETRDecoder:  # special case, channels arg must be passed in index 1
            args.insert(1, [ch[x] for x in f])
        elif m is CBLinear:
            c2 = args[0]
            c1 = ch[f]
            args = [c1, c2, *args[1:]]
        elif m is CBFuse:
            c2 = ch[f[-1]]
        else:
            c2 = ch[f]
 
        if isinstance(c2, list):
            is_backbone = True
            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, m_.np = i + 4 if is_backbone else i, f, t, m.np  # attach index, 'from' index, type, number params
        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 is_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)
            for _ in range(5 - len(ch)):
                ch.insert(0, 0)
        else:
            ch.append(c2)
    return nn.Sequential(*layers), sorted(save)

2.5 替换函数

**关键步骤五：**在task.py的BaseModel类下的_predict_once函数替换为下面的内容

python 复制代码 
    def _predict_once(self, x, profile=False, visualize=False, embed=None):
        """
        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.
            embed (list, optional): A list of feature vectors/embeddings to return.
        Returns:
            (torch.Tensor): The last output of the model.
        """
        y, dt, embeddings = [], [], []  # 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)
                for _ in range(5 - len(x)):
                    x.insert(0, None)
                for i_idx, i in enumerate(x):
                    if i_idx in self.save:
                        y.append(i)
                    else:
                        y.append(None)
                # for i in x:
                #     if i is not None:
                #         print(i.size())
                x = x[-1]
            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)
            if embed and m.i in embed:
                embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
                if m.i == max(embed):
                    return torch.unbind(torch.cat(embeddings, 1), dim=0)
        return x

2.6 执行程序

关键步骤五: 在ultralytics文件中新建train.py，将model的参数路径设置为yolo11_VanillaNet .yaml的路径即可

python 复制代码 
from ultralytics import YOLO
import warnings
warnings.filterwarnings('ignore')
from pathlib import Path
 
if __name__ == '__main__':
 
 
    # 加载模型
    model = YOLO("ultralytics/cfg/11/yolo11.yaml")  # 你要选择的模型yaml文件地址
    # Use the model
    results = model.train(data=r"你的数据集的yaml文件地址",
                          epochs=100, batch=16, imgsz=640, workers=4, name=Path(model.cfg).stem)  # 训练模型

🚀运行程序，如果出现下面的内容则说明添加成功🚀

python 复制代码 
                   from  n    params  module                                       arguments
  0                  -1  1    318592  et  
  1                  -1  1    394240  ultralytics.nn.modules.block.SPPF            [512, 256, 5]
  2                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']
  3             [-1, 3]  1         0  ultralytics.nn.modules.conv.Concat           [1]
  4                  -1  1    127680  ultralytics.nn.modules.block.C3k2            [512, 128, 1, False]
  5                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']
  6             [-1, 2]  1         0  ultralytics.nn.modules.conv.Concat           [1]
  7                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1, False]
  8                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]
  9             [-1, 8]  1         0  ultralytics.nn.modules.conv.Concat           [1]
 10                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1, False]
 11                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]
 12             [-1, 5]  1         0  ultralytics.nn.modules.conv.Concat           [1]
 13                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1, True]
 14        [11, 14, 17]  1    464912  ultralytics.nn.modules.head.Detect           [80, [64, 128, 256]]
YOLO11_VanillaNet summary: 218 layers, 1,987,824 parameters, 1,987,808 gradients, 6.2 GFLOPs

3.修改后的网络结构图

4. 完整代码分享

++这个后期补充吧~，先按照步骤来即可++

5. GFLOPs

关于GFLOPs的计算方式可以查看：百面算法工程师 | 卷积基础知识------Convolution

未改进的YOLO11n GFLOPs

​

改进后的GFLOPs

6. 进阶

可以与其他的注意力机制或者损失函数等结合，进一步提升检测效果

7.总结

通过以上的改进方法，我们成功提升了模型的表现。这只是一个开始，未来还有更多优化和技术深挖的空间。在这里，我想隆重向大家推荐我的专栏------<专栏地址：YOLO11入门 + 改进涨点------点击即可跳转 欢迎订阅****>。这个专栏专注于前沿的深度学习技术，特别是目标检测领域的最新进展，不仅包含对YOLO11的深入解析和改进策略，还会定期更新来自各大顶会（如CVPR、NeurIPS等）的论文复现和实战分享。

为什么订阅我的专栏？ ------专栏地址：YOLO11入门 + 改进涨点------点击即可跳转 欢迎订阅****

  1. 前沿技术解读：专栏不仅限于YOLO系列的改进，还会涵盖各类主流与新兴网络的最新研究成果，帮助你紧跟技术潮流。

  2. 详尽的实践分享 ：所有内容实践性也极强。每次更新都会附带代码和具体的改进步骤，保证每位读者都能迅速上手。

  3. 问题互动与答疑 ：订阅我的专栏后，你将可以随时向我提问，获取及时的答疑。

  4. 实时更新，紧跟行业动态：不定期发布来自全球顶会的最新研究方向和复现实验报告，让你时刻走在技术前沿。

专栏适合人群：

  • 对目标检测、YOLO系列网络有深厚兴趣的同学

  • 希望在用YOLO算法写论文的同学

  • 对YOLO算法感兴趣的同学等

​

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