YOLO11改进 | 卷积模块 | ECCV2024 小波卷积

kay_5452025-01-03 10:20

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Conv替换为WTConv结构来融合特征。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. WTConv代码实现](#2. WTConv代码实现)

[2.1 将WTConv添加到YOLO11中](#2.1 将WTConv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 在task.py中进行注册](#2.4 在task.py中进行注册)

[2.5 执行程序](#2.5 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： Wavelet Convolutions for Large Receptive Fields

官方代码： 官方代码仓库点击------即可跳转

2. WTConv代码实现

2.1 将WTConv添加到YOLO11中

**关键步骤一：**在ultralytics\ultralytics\nn\modules下面新建文件夹models，在文件夹下新建WTConv.py，粘贴下面代码

python 复制代码 
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function

import pywt
import pywt.data


def create_wavelet_filter(wave, in_size, out_size, type=torch.float):
    w = pywt.Wavelet(wave)
    dec_hi = torch.tensor(w.dec_hi[::-1], dtype=type)
    dec_lo = torch.tensor(w.dec_lo[::-1], dtype=type)
    dec_filters = torch.stack([dec_lo.unsqueeze(0) * dec_lo.unsqueeze(1),
                               dec_lo.unsqueeze(0) * dec_hi.unsqueeze(1),
                               dec_hi.unsqueeze(0) * dec_lo.unsqueeze(1),
                               dec_hi.unsqueeze(0) * dec_hi.unsqueeze(1)], dim=0)

    dec_filters = dec_filters[:, None].repeat(in_size, 1, 1, 1)

    rec_hi = torch.tensor(w.rec_hi[::-1], dtype=type).flip(dims=[0])
    rec_lo = torch.tensor(w.rec_lo[::-1], dtype=type).flip(dims=[0])
    rec_filters = torch.stack([rec_lo.unsqueeze(0) * rec_lo.unsqueeze(1),
                               rec_lo.unsqueeze(0) * rec_hi.unsqueeze(1),
                               rec_hi.unsqueeze(0) * rec_lo.unsqueeze(1),
                               rec_hi.unsqueeze(0) * rec_hi.unsqueeze(1)], dim=0)

    rec_filters = rec_filters[:, None].repeat(out_size, 1, 1, 1)

    return dec_filters, rec_filters

def wavelet_transform(x, filters):
    b, c, h, w = x.shape
    pad = (filters.shape[2] // 2 - 1, filters.shape[3] // 2 - 1)
    x = F.conv2d(x, filters.to(x.dtype).to(x.device), stride=2, groups=c, padding=pad)
    x = x.reshape(b, c, 4, h // 2, w // 2)
    return x


def inverse_wavelet_transform(x, filters):
    b, c, _, h_half, w_half = x.shape
    pad = (filters.shape[2] // 2 - 1, filters.shape[3] // 2 - 1)
    x = x.reshape(b, c * 4, h_half, w_half)
    x = F.conv_transpose2d(x, filters.to(x.dtype).to(x.device), stride=2, groups=c, padding=pad)
    return x


# Define the WaveletTransform class
class WaveletTransform(Function):
    @staticmethod
    def forward(ctx, input, filters):
        ctx.filters = filters
        with torch.no_grad():
            x = wavelet_transform(input, filters)
        return x

    @staticmethod
    def backward(ctx, grad_output):
        grad = inverse_wavelet_transform(grad_output, ctx.filters)
        return grad, None

# Define the InverseWaveletTransform class
class InverseWaveletTransform(Function):
    @staticmethod
    def forward(ctx, input, filters):
        ctx.filters = filters
        with torch.no_grad():
            x = inverse_wavelet_transform(input, filters)
        return x

    @staticmethod
    def backward(ctx, grad_output):
        grad = wavelet_transform(grad_output, ctx.filters)
        return grad, None

# Initialize the WaveletTransform
def wavelet_transform_init(filters):
    def apply(input):
        return WaveletTransform.apply(input, filters)
    return apply

# Initialize the InverseWaveletTransform
def inverse_wavelet_transform_init(filters):
    def apply(input):
        return InverseWaveletTransform.apply(input, filters)
    return apply

class WTConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=5, stride=1, bias=True, wt_levels=1, wt_type='db1'):
        super(WTConv2d, self).__init__()

        assert in_channels == out_channels

        self.in_channels = in_channels
        self.wt_levels = wt_levels
        self.stride = stride
        self.dilation = 1

        self.wt_filter, self.iwt_filter = create_wavelet_filter(wt_type, in_channels, in_channels, torch.float)
        self.wt_filter = nn.Parameter(self.wt_filter, requires_grad=False)
        self.iwt_filter = nn.Parameter(self.iwt_filter, requires_grad=False)
        
        self.wt_function = wavelet_transform_init(self.wt_filter)
        self.iwt_function = inverse_wavelet_transform_init(self.iwt_filter)

        self.base_conv = nn.Conv2d(in_channels, in_channels, kernel_size, padding='same', stride=1, dilation=1, groups=in_channels, bias=bias)
        self.base_scale = _ScaleModule([1,in_channels,1,1])

        self.wavelet_convs = nn.ModuleList(
            [nn.Conv2d(in_channels*4, in_channels*4, kernel_size, padding='same', stride=1, dilation=1, groups=in_channels*4, bias=False) for _ in range(self.wt_levels)]
        )
        self.wavelet_scale = nn.ModuleList(
            [_ScaleModule([1,in_channels*4,1,1], init_scale=0.1) for _ in range(self.wt_levels)]
        )

        if self.stride > 1:
            self.stride_filter = nn.Parameter(torch.ones(in_channels, 1, 1, 1), requires_grad=False)
            self.do_stride = lambda x_in: F.conv2d(x_in, self.stride_filter.to(x_in.dtype).to(x_in.device), bias=None, stride=self.stride, groups=in_channels)
        else:
            self.do_stride = None

    def forward(self, x):

        x_ll_in_levels = []
        x_h_in_levels = []
        shapes_in_levels = []

        curr_x_ll = x

        for i in range(self.wt_levels):
            curr_shape = curr_x_ll.shape
            shapes_in_levels.append(curr_shape)
            if (curr_shape[2] % 2 > 0) or (curr_shape[3] % 2 > 0):
                curr_pads = (0, curr_shape[3] % 2, 0, curr_shape[2] % 2)
                curr_x_ll = F.pad(curr_x_ll, curr_pads)

            curr_x = self.wt_function(curr_x_ll)
            curr_x_ll = curr_x[:,:,0,:,:]
            
            shape_x = curr_x.shape
            curr_x_tag = curr_x.reshape(shape_x[0], shape_x[1] * 4, shape_x[3], shape_x[4])
            curr_x_tag = self.wavelet_scale[i](self.wavelet_convs[i](curr_x_tag))
            curr_x_tag = curr_x_tag.reshape(shape_x)

            x_ll_in_levels.append(curr_x_tag[:,:,0,:,:])
            x_h_in_levels.append(curr_x_tag[:,:,1:4,:,:])

        next_x_ll = 0

        for i in range(self.wt_levels-1, -1, -1):
            curr_x_ll = x_ll_in_levels.pop()
            curr_x_h = x_h_in_levels.pop()
            curr_shape = shapes_in_levels.pop()

            curr_x_ll = curr_x_ll + next_x_ll

            curr_x = torch.cat([curr_x_ll.unsqueeze(2), curr_x_h], dim=2)
            next_x_ll = self.iwt_function(curr_x)

            next_x_ll = next_x_ll[:, :, :curr_shape[2], :curr_shape[3]]

        x_tag = next_x_ll
        assert len(x_ll_in_levels) == 0
        
        x = self.base_scale(self.base_conv(x))
        x = x + x_tag
        
        if self.do_stride is not None:
            x = self.do_stride(x)

        return x

class _ScaleModule(nn.Module):
    def __init__(self, dims, init_scale=1.0, init_bias=0):
        super(_ScaleModule, self).__init__()
        self.dims = dims
        self.weight = nn.Parameter(torch.ones(*dims) * init_scale)
        self.bias = None
    
    def forward(self, x):
        return torch.mul(self.weight, x)

2.2 更改init.py文件

**关键步骤二：**在文件ultralytics\ultralytics\nn\modules\models文件夹下新建__init__.py文件，先导入函数

是上面的这个WTConv2d 框错了

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_WTConv.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

# YOLO11n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
  - [-1, 2, C3k2, [256, False, 0.25]]
  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
  - [-1, 2, C3k2, [512, False, 0.25]]
  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
  - [-1, 2, C3k2, [512, True]]
  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
  - [-1, 2, C3k2, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]] # 9
  - [-1, 2, C2PSA, [1024]] # 10

# YOLO11n head
head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
  - [-1, 2, C3k2, [512, False]] # 13

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)

  - [-1, 1, WTConv2d, [256, 3, 2]]
  - [[-1, 13], 1, Concat, [1]] # cat head P4
  - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)

  - [-1, 1, WTConv2d, [512, 3, 2]]
  - [[-1, 10], 1, Concat, [1]] # cat head P5
  - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)

  - [[16, 19, 22], 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

# YOLO11n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
  - [-1, 2, C3k2, [256, False, 0.25]]
  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
  - [-1, 2, C3k2, [512, False, 0.25]]
  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
  - [-1, 2, C3k2, [512, True]]
  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
  - [-1, 2, C3k2, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]] # 9
  - [-1, 2, C2PSA, [1024]] # 10

# YOLO11n head
head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
  - [-1, 2, C3k2, [512, False]] # 13

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)

  - [-1, 1, WTConv2d, [256, 3, 2]]
  - [[-1, 13], 1, Concat, [1]] # cat head P4
  - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)

  - [-1, 1, WTConv2d, [512, 3, 2]]
  - [[-1, 10], 1, Concat, [1]] # cat head P5
  - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)

  - [[16, 19, 22], 1, Segment, [nc, 32, 256]] # Detect(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

# YOLO11n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
  - [-1, 2, C3k2, [256, False, 0.25]]
  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
  - [-1, 2, C3k2, [512, False, 0.25]]
  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
  - [-1, 2, C3k2, [512, True]]
  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
  - [-1, 2, C3k2, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]] # 9
  - [-1, 2, C2PSA, [1024]] # 10

# YOLO11n head
head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
  - [-1, 2, C3k2, [512, False]] # 13

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)

  - [-1, 1, WTConv2d, [256, 3, 2]]
  - [[-1, 13], 1, Concat, [1]] # cat head P4
  - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)

  - [-1, 1, WTConv2d, [512, 3, 2]]
  - [[-1, 10], 1, Concat, [1]] # cat head P5
  - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)

  - [[16, 19, 22], 1, OBB, [nc, 1]] # Detect(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 在task.py中进行注册

**关键步骤四：**在parse_model函数中进行注册，添加WTConv

先在task.py导入函数

然后在task.py文件下找到parse_model这个函数，如下图，添加WTConv

2.5 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_ WTConv**.yaml**的路径即可 【注意是在外边的Ultralytics下新建train.py】

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       464  ultralytics.nn.modules.conv.Conv                      [3, 16, 3, 2]
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv                      [16, 32, 3, 2]
  2                  -1  1      6640  ultralytics.nn.modules.block.C3k2                     [32, 64, 1, False, 0.25]      
  3                  -1  1     36992  ultralytics.nn.modules.conv.Conv                      [64, 64, 3, 2]
  4                  -1  1     26080  ultralytics.nn.modules.block.C3k2                     [64, 128, 1, False, 0.25]     
  5                  -1  1    147712  ultralytics.nn.modules.conv.Conv                      [128, 128, 3, 2]
  6                  -1  1     87040  ultralytics.nn.modules.block.C3k2                     [128, 128, 1, True]
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv                      [128, 256, 3, 2]
  8                  -1  1    346112  ultralytics.nn.modules.block.C3k2                     [256, 256, 1, True]
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF                     [256, 256, 5]
 10                  -1  1    249728  ultralytics.nn.modules.block.C2PSA                    [256, 256, 1]
 11                  -1  1         0  torch.nn.modules.upsampling.Upsample                  [None, 2, 'nearest']
 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat                    [1]
 13                  -1  1    111296  ultralytics.nn.modules.block.C3k2                     [384, 128, 1, False]
 14                  -1  1         0  torch.nn.modules.upsampling.Upsample                  [None, 2, 'nearest']
 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat                    [1]
 16                  -1  1     32096  ultralytics.nn.modules.block.C3k2                     [256, 64, 1, False]
 17                  -1  1      5376  ultralytics.nn.modules.models.WTConv.WTConv2d         [64, 64, 3, 2]
 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat                    [1]
 19                  -1  1     86720  ultralytics.nn.modules.block.C3k2                     [192, 128, 1, False]
 20                  -1  1     10752  ultralytics.nn.modules.models.WTConv.WTConv2d         [128, 128, 3, 2]
 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat                    [1]
 22                  -1  1    378880  ultralytics.nn.modules.block.C3k2                     [384, 256, 1, True]
 23        [16, 19, 22]  1    464912  ultralytics.nn.modules.head.Detect                    [80, [64, 128, 256]]
YOLO11_WTConv summary: 327 layers, 2,455,504 parameters, 2,449,152 gradients, 6.4 GFLOPs

3.修改后的网络结构图

4. 完整代码分享

++主页侧边++

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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