本文主要是介绍YOLOv5改进系列(28)——添加DSConv注意力卷积(ICCV 2023|用于管状结构分割的动态蛇形卷积),希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!
【YOLOv5改进系列】前期回顾:
YOLOv5改进系列(0)——重要性能指标与训练结果评价及分析
YOLOv5改进系列(1)——添加SE注意力机制
YOLOv5改进系列(2)——添加CBAM注意力机制
YOLOv5改进系列(3)——添加CA注意力机制
YOLOv5改进系列(4)——添加ECA注意力机制
YOLOv5改进系列(5)——替换主干网络之 MobileNetV3
YOLOv5改进系列(6)——替换主干网络之 ShuffleNetV2
YOLOv5改进系列(7)——添加SimAM注意力机制
YOLOv5改进系列(8)——添加SOCA注意力机制
YOLOv5改进系列(9)——替换主干网络之EfficientNetv2
YOLOv5改进系列(10)——替换主干网络之GhostNet
YOLOv5改进系列(11)——添加损失函数之EIoU、AlphaIoU、SIoU、WIoU
YOLOv5改进系列(12)——更换Neck之BiFPN
YOLOv5改进系列(13)——更换激活函数之SiLU,ReLU,ELU,Hardswish,Mish,Softplus,AconC系列等YOLOv5改进系列(14)——更换NMS(非极大抑制)之 DIoU-NMS、CIoU-NMS、EIoU-NMS、GIoU-NMS 、SIoU-NMS、Soft-NMS
YOLOv5改进系列(15)——增加小目标检测层
YOLOv5改进系列(16)——添加EMA注意力机制(ICASSP2023|实测涨点)
YOLOv5改进系列(17)——更换IoU之MPDIoU(ELSEVIER 2023|超越WIoU、EIoU等|实测涨点)
YOLOv5改进系列(18)——更换Neck之AFPN(全新渐进特征金字塔|超越PAFPN|实测涨点)
YOLOv5改进系列(19)——替换主干网络之Swin TransformerV1(参数量更小的ViT模型)
YOLOv5改进系列(21)——替换主干网络之RepViT(清华 ICCV 2023|最新开源移动端ViT)
YOLOv5改进系列(22)——替换主干网络之MobileViTv1(一种轻量级的、通用的移动设备 ViT)
YOLOv5改进系列(23)——替换主干网络之MobileViTv2(移动视觉 Transformer 的高效可分离自注意力机制)
YOLOv5改进系列(24)——替换主干网络之MobileViTv3(移动端轻量化网络的进一步升级)
YOLOv5改进系列(25)——添加LSKNet注意力机制(大选择性卷积核的领域首次探索)
YOLOv5改进系列(26)——添加RFAConv注意力卷积(感受野注意力卷积运算)
目录
🚀 一、DSConv介绍
1.1 DSConv简介
1.2 动态蛇形卷积
1.3 多视角特征融合策略
1.4 连续性拓扑约束损失
🚀二、具体添加方法
2.1 添加顺序
2.2 具体添加步骤
第①步:在common.py中添加DCConv模块
第②步:修改yolo.py文件
第③步:创建自定义的yaml文件
第④步:验证是否加入成功
🌟本人YOLOv5系列导航
🚀 一、DSConv介绍
学习资料:
- 论文题目:《Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation》
- 论文地址:https://arxiv.org/abs/2307.08388
- 源码地址:https://github.com/YaoleiQi/DSCNet
1.1 DSConv简介
背景
管状结构(例如血管、道路)是临床、自然界等各领域场景中十分重要的一种结构,其精确分割可以保证下游任务的准确性与效率。但管状结构的精确提取仍然面临着众多挑战:
- 细长且脆弱的局部结构。如下图所示,细长的结构仅占整个图像的一小部分,像素的组成有限。此外,这些结构容易受到复杂背景的干扰,因此模型很难精确分辨目标的细微变化,从而导致分割出现破碎与断裂。
- 复杂且多变的全局形态。如下图所示,我们可以看出细长管状结构复杂多变的形态,即使在同一张图像中也是如此。位于不同区域的目标的形态变化取决于分支的数量、分叉的位置,路径长度以及其在图像中的位置。因此当数据表现出未曾见过的形态特征时,模型倾向于过拟合到已见过的特征,无法识别未见过的特征形态,从而导致泛化性较弱。
本文主要工作
本文关注到管状结构细长连续的特点,并利用这一信息在神经网络以下三个阶段同时增强感知:特征提取、特征融合和损失约束。分别设计了动态蛇形卷积(Dynamic Snake Convolution),多视角特征融合策略与连续性拓扑约束损失。我们同时给出了基于 2D 和 3D 的方法设计,通过实验证明了本文所提出的 DSCNet 在管状结构分割任务上提供了更好的精度和连续性。
1.2 动态蛇形卷积
目的:
- 希望卷积核一方面能够自由地贴合结构学习特征
- 另一方面能够在约束条件下不偏离目标结构太远
可变形卷积:
- 操控单个卷积核形变的所有偏置(offset),是在网络中一次性全部学到的
- 对于这一个偏置只有一个范围的约束,即感受野范围(extend)
- 控制所有的卷积发生形变,是依赖于整个网络最终的损失约束回传,这个变化过程是相当自由的。
1.3 多视角特征融合策略
目的:
- 管状结构的走向与视角不是单一的,因此在设计中融合多视角特征也是必然的选择。
挑战:
- 融合更多的特征会导致更大的网络负载以及出现冗余。
方法:
- 在特征融合的训练过程中加入了分组与dropout的策略,一定程度上缓解了网络内内存的压力并避免模型陷入过拟合。
1.4 连续性拓扑约束损失
目的:
- 构建数据的拓扑结构,并提取复杂管状结构中的高维关系,也就是持续同源性(Persistence Homology, PH)。
启发:
- 假设 PO 的上端存在着一个异常的离散点(横坐标表示出现的时间,纵坐标表示消失的时间),这表明存在一个构件直到最后才与其他构件获得连接从而消失。
方法:
- 本文中采用的是豪斯多夫距离(Hausdorff Distance, HD),HD 也是用于衡量点集相似度的一个重要算法,对离散点也非常敏感。
# -*- coding: utf-8 -*-import torchfrom torch import nn
from torch.nn.functional import max_pool3dclass crossentry(nn.Module):def __init__(self):super().__init__()def forward(self, y_true, y_pred):smooth = 1e-6return -torch.mean(y_true * torch.log(y_pred + smooth))class cross_loss(nn.Module):def __init__(self):super().__init__()def forward(self, y_true, y_pred):smooth = 1e-6return -torch.mean(y_true * torch.log(y_pred + smooth) +(1 - y_true) * torch.log(1 - y_pred + smooth))'''
Another Loss Function proposed by us in IEEE transactions on Image Precessing:
Paper: https://ieeexplore.ieee.org/abstract/document/9611074
Code: https://github.com/YaoleiQi/Examinee-Examiner-Network
'''class Dropoutput_Layer(nn.Module):def __init__(self):super().__init__()def forward(self, y_true, y_pred, alpha=0.4):smooth = 1e-6w = torch.abs(y_true - y_pred)w = torch.round(w + alpha)loss_ce = (-((torch.sum(w * y_true * torch.log(y_pred + smooth)) /torch.sum(w * y_true + smooth)) +(torch.sum(w * (1 - y_true) * torch.log(1 - y_pred + smooth)) /torch.sum(w * (1 - y_true) + smooth))) / 2)return loss_ce
🚀二、具体添加方法
2.1 添加顺序
(1)models/common.py --> 加入新增的网络结构
(2) models/yolo.py --> 设定网络结构的传参细节,将DSConv类名加入其中。(当新的自定义模块中存在输入输出维度时,要使用qw调整输出维度)
(3) models/yolov5*.yaml --> 新建一个文件夹,如yolov5s_DSConv.yaml,修改现有模型结构配置文件。(当引入新的层时,要修改后续的结构中的from参数)
(4) train.py --> 修改‘--cfg’默认参数,训练时指定模型结构配置文件
2.2 具体添加步骤
第①步:在common.py中添加DCConv模块
将下面的DSConv代码复制粘贴到common.py文件的末尾。
# by:迪菲赫尔曼
import warnings
import torch
from torch import nnwarnings.filterwarnings("ignore")"""
This code is mainly the deformation process of our DSConv
"""class DSConv(nn.Module):def __init__(self, in_ch, out_ch, kernel_size, extend_scope, morph,if_offset):"""动态蛇形卷积:param in_ch: 输入通道:param out_ch: 输出通道:param kernel_size: 卷积核的大小:param extend_scope: 扩展范围(默认为此方法的1):param morph: 卷积核的形态主要分为两种类型,沿x轴(0)和沿y轴(1)(详细信息请参阅论文):param if_offset: 是否需要变形,如果为False,则是标准卷积核"""super(DSConv, self).__init__()# use the <offset_conv> to learn the deformable offset# offset_conv: 学习可变形偏移的卷积层self.offset_conv = nn.Conv2d(in_ch, 2 * kernel_size, 3, padding=1)self.bn = nn.BatchNorm2d(2 * kernel_size)self.kernel_size = kernel_size# two types of the DSConv (along x-axis and y-axis)# dsc_conv_x 和 dsc_conv_y:两种动态蛇形卷积层,分别沿x轴和y轴。self.dsc_conv_x = nn.Conv2d(in_ch,out_ch,kernel_size=(kernel_size, 1),stride=(kernel_size, 1),padding=0,)self.dsc_conv_y = nn.Conv2d(in_ch,out_ch,kernel_size=(1, kernel_size),stride=(1, kernel_size),padding=0,)# gn:组归一化层self.gn = nn.GroupNorm(out_ch // 4, out_ch)self.relu = nn.ReLU(inplace=True)# extend_scope:扩展范围self.extend_scope = extend_scope# morph:卷积核形态的类型self.morph = morph# if_offset:指示是否需要变形的布尔值self.if_offset = if_offsetdef forward(self, f):offset = self.offset_conv(f)offset = self.bn(offset)# We need a range of deformation between -1 and 1 to mimic the snake's swingoffset = torch.tanh(offset)input_shape = f.shapedsc = DSC(input_shape, self.kernel_size, self.extend_scope, self.morph)deformed_feature = dsc.deform_conv(f, offset, self.if_offset)if self.morph == 0:x = self.dsc_conv_x(deformed_feature.type(f.dtype))x = self.gn(x)x = self.relu(x)return xelse:x = self.dsc_conv_y(deformed_feature.type(f.dtype))x = self.gn(x)x = self.relu(x)return x# Core code, for ease of understanding, we mark the dimensions of input and output next to the code
class DSC(object):def __init__(self, input_shape, kernel_size, extend_scope, morph):self.num_points = kernel_sizeself.width = input_shape[2]self.height = input_shape[3]self.morph = morphself.extend_scope = extend_scope # offset (-1 ~ 1) * extend_scope# define feature map shape"""B: Batch size C: Channel W: Width H: Height"""self.num_batch = input_shape[0]self.num_channels = input_shape[1]"""input: offset [B,2*K,W,H] K: Kernel size (2*K: 2D image, deformation contains <x_offset> and <y_offset>)output_x: [B,1,W,K*H] coordinate mapoutput_y: [B,1,K*W,H] coordinate map"""def _coordinate_map_3D(self, offset, if_offset):"""1.输入为偏移 (offset) 和是否需要偏移 (if_offset)。2.根据输入特征图的形状、卷积核大小、扩展范围以及形态类型,生成二维坐标映射。3.如果形态类型为0,表示沿x轴,生成y坐标映射;如果形态类型为1,表示沿y轴,生成x坐标映射。4.根据偏移和扩展范围调整坐标映射。5.返回生成的坐标映射。"""device = offset.device# offsety_offset, x_offset = torch.split(offset, self.num_points, dim=1)y_center = torch.arange(0, self.width).repeat([self.height])y_center = y_center.reshape(self.height, self.width)y_center = y_center.permute(1, 0)y_center = y_center.reshape([-1, self.width, self.height])y_center = y_center.repeat([self.num_points, 1, 1]).float()y_center = y_center.unsqueeze(0)x_center = torch.arange(0, self.height).repeat([self.width])x_center = x_center.reshape(self.width, self.height)x_center = x_center.permute(0, 1)x_center = x_center.reshape([-1, self.width, self.height])x_center = x_center.repeat([self.num_points, 1, 1]).float()x_center = x_center.unsqueeze(0)if self.morph == 0:"""Initialize the kernel and flatten the kernely: only need 0x: -num_points//2 ~ num_points//2 (Determined by the kernel size)!!! The related PPT will be submitted later, and the PPT will contain the whole changes of each step"""y = torch.linspace(0, 0, 1)x = torch.linspace(-int(self.num_points // 2),int(self.num_points // 2),int(self.num_points),)y, x = torch.meshgrid(y, x)y_spread = y.reshape(-1, 1)x_spread = x.reshape(-1, 1)y_grid = y_spread.repeat([1, self.width * self.height])y_grid = y_grid.reshape([self.num_points, self.width, self.height])y_grid = y_grid.unsqueeze(0) # [B*K*K, W,H]x_grid = x_spread.repeat([1, self.width * self.height])x_grid = x_grid.reshape([self.num_points, self.width, self.height])x_grid = x_grid.unsqueeze(0) # [B*K*K, W,H]y_new = y_center + y_gridx_new = x_center + x_gridy_new = y_new.repeat(self.num_batch, 1, 1, 1).to(device)x_new = x_new.repeat(self.num_batch, 1, 1, 1).to(device)y_offset_new = y_offset.detach().clone()if if_offset:y_offset = y_offset.permute(1, 0, 2, 3)y_offset_new = y_offset_new.permute(1, 0, 2, 3)center = int(self.num_points // 2)# The center position remains unchanged and the rest of the positions begin to swing# This part is quite simple. The main idea is that "offset is an iterative process"y_offset_new[center] = 0for index in range(1, center):y_offset_new[center + index] = (y_offset_new[center + index - 1] + y_offset[center + index])y_offset_new[center - index] = (y_offset_new[center - index + 1] + y_offset[center - index])y_offset_new = y_offset_new.permute(1, 0, 2, 3).to(device)y_new = y_new.add(y_offset_new.mul(self.extend_scope))y_new = y_new.reshape([self.num_batch, self.num_points, 1, self.width, self.height])y_new = y_new.permute(0, 3, 1, 4, 2)y_new = y_new.reshape([self.num_batch, self.num_points * self.width, 1 * self.height])x_new = x_new.reshape([self.num_batch, self.num_points, 1, self.width, self.height])x_new = x_new.permute(0, 3, 1, 4, 2)x_new = x_new.reshape([self.num_batch, self.num_points * self.width, 1 * self.height])return y_new, x_newelse:"""Initialize the kernel and flatten the kernely: -num_points//2 ~ num_points//2 (Determined by the kernel size)x: only need 0"""y = torch.linspace(-int(self.num_points // 2),int(self.num_points // 2),int(self.num_points),)x = torch.linspace(0, 0, 1)y, x = torch.meshgrid(y, x)y_spread = y.reshape(-1, 1)x_spread = x.reshape(-1, 1)y_grid = y_spread.repeat([1, self.width * self.height])y_grid = y_grid.reshape([self.num_points, self.width, self.height])y_grid = y_grid.unsqueeze(0)x_grid = x_spread.repeat([1, self.width * self.height])x_grid = x_grid.reshape([self.num_points, self.width, self.height])x_grid = x_grid.unsqueeze(0)y_new = y_center + y_gridx_new = x_center + x_gridy_new = y_new.repeat(self.num_batch, 1, 1, 1)x_new = x_new.repeat(self.num_batch, 1, 1, 1)y_new = y_new.to(device)x_new = x_new.to(device)x_offset_new = x_offset.detach().clone()if if_offset:x_offset = x_offset.permute(1, 0, 2, 3)x_offset_new = x_offset_new.permute(1, 0, 2, 3)center = int(self.num_points // 2)x_offset_new[center] = 0for index in range(1, center):x_offset_new[center + index] = (x_offset_new[center + index - 1] + x_offset[center + index])x_offset_new[center - index] = (x_offset_new[center - index + 1] + x_offset[center - index])x_offset_new = x_offset_new.permute(1, 0, 2, 3).to(device)x_new = x_new.add(x_offset_new.mul(self.extend_scope))y_new = y_new.reshape([self.num_batch, 1, self.num_points, self.width, self.height])y_new = y_new.permute(0, 3, 1, 4, 2)y_new = y_new.reshape([self.num_batch, 1 * self.width, self.num_points * self.height])x_new = x_new.reshape([self.num_batch, 1, self.num_points, self.width, self.height])x_new = x_new.permute(0, 3, 1, 4, 2)x_new = x_new.reshape([self.num_batch, 1 * self.width, self.num_points * self.height])return y_new, x_new"""input: input feature map [N,C,D,W,H];coordinate map [N,K*D,K*W,K*H] output: [N,1,K*D,K*W,K*H] deformed feature map"""def _bilinear_interpolate_3D(self, input_feature, y, x):"""1.输入为输入特征图 (input_feature)、y坐标映射 (y) 和x坐标映射 (x)。2.进行三维双线性插值,获取变形后的特征。3.返回插值得到的变形特征。"""device = input_feature.devicey = y.reshape([-1]).float()x = x.reshape([-1]).float()zero = torch.zeros([]).int()max_y = self.width - 1max_x = self.height - 1# find 8 grid locationsy0 = torch.floor(y).int()y1 = y0 + 1x0 = torch.floor(x).int()x1 = x0 + 1# clip out coordinates exceeding feature map volumey0 = torch.clamp(y0, zero, max_y)y1 = torch.clamp(y1, zero, max_y)x0 = torch.clamp(x0, zero, max_x)x1 = torch.clamp(x1, zero, max_x)input_feature_flat = input_feature.flatten()input_feature_flat = input_feature_flat.reshape(self.num_batch, self.num_channels, self.width, self.height)input_feature_flat = input_feature_flat.permute(0, 2, 3, 1)input_feature_flat = input_feature_flat.reshape(-1, self.num_channels)dimension = self.height * self.widthbase = torch.arange(self.num_batch) * dimensionbase = base.reshape([-1, 1]).float()repeat = torch.ones([self.num_points * self.width * self.height]).unsqueeze(0)repeat = repeat.float()base = torch.matmul(base, repeat)base = base.reshape([-1])base = base.to(device)base_y0 = base + y0 * self.heightbase_y1 = base + y1 * self.height# top rectangle of the neighbourhood volumeindex_a0 = base_y0 - base + x0index_c0 = base_y0 - base + x1# bottom rectangle of the neighbourhood volumeindex_a1 = base_y1 - base + x0index_c1 = base_y1 - base + x1# get 8 grid valuesvalue_a0 = input_feature_flat[index_a0.type(torch.int64)].to(device)value_c0 = input_feature_flat[index_c0.type(torch.int64)].to(device)value_a1 = input_feature_flat[index_a1.type(torch.int64)].to(device)value_c1 = input_feature_flat[index_c1.type(torch.int64)].to(device)# find 8 grid locationsy0 = torch.floor(y).int()y1 = y0 + 1x0 = torch.floor(x).int()x1 = x0 + 1# clip out coordinates exceeding feature map volumey0 = torch.clamp(y0, zero, max_y + 1)y1 = torch.clamp(y1, zero, max_y + 1)x0 = torch.clamp(x0, zero, max_x + 1)x1 = torch.clamp(x1, zero, max_x + 1)x0_float = x0.float()x1_float = x1.float()y0_float = y0.float()y1_float = y1.float()vol_a0 = ((y1_float - y) * (x1_float - x)).unsqueeze(-1).to(device)vol_c0 = ((y1_float - y) * (x - x0_float)).unsqueeze(-1).to(device)vol_a1 = ((y - y0_float) * (x1_float - x)).unsqueeze(-1).to(device)vol_c1 = ((y - y0_float) * (x - x0_float)).unsqueeze(-1).to(device)outputs = (value_a0 * vol_a0 + value_c0 * vol_c0 + value_a1 * vol_a1 +value_c1 * vol_c1)if self.morph == 0:outputs = outputs.reshape([self.num_batch,self.num_points * self.width,1 * self.height,self.num_channels,])outputs = outputs.permute(0, 3, 1, 2)else:outputs = outputs.reshape([self.num_batch,1 * self.width,self.num_points * self.height,self.num_channels,])outputs = outputs.permute(0, 3, 1, 2)return outputsdef deform_conv(self, input, offset, if_offset):"""1.输入为原始特征图 (input)、偏移 (offset) 和是否需要偏移 (if_offset)。2.调用 _coordinate_map_3D 方法获取坐标映射。3.调用 _bilinear_interpolate_3D 方法进行双线性插值,得到变形后的特征。4.返回变形后的特征。"""y, x = self._coordinate_map_3D(offset, if_offset)deformed_feature = self._bilinear_interpolate_3D(input, y, x)return deformed_feature#---------------------------------YOLOv5 专用部分↓---------------------------------
class DSConv_Bottleneck(nn.Module):# DSConv bottleneckdef __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansionsuper().__init__()c_ = int(c2 * e) # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c_, c2, 3, 1, g=g)self.add = shortcut and c1 == c2self.snc = DSConv(c2, c2, 3, 1, 1, True)def forward(self, x):return x + self.snc(self.cv2(self.cv1(x))) if self.add else self.snc(self.cv2(self.cv1(x)))class DSConv_C3(nn.Module):# DSConv Bottleneck with 3 convolutionsdef __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__()c_ = int(c2 * e) # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c1, c_, 1, 1)self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)self.m = nn.Sequential(*(DSConv_Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))def forward(self, x):return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))#---------------------------------YOLOv5 专用部分↑---------------------------------
第②步:修改yolo.py文件
再来修改yolo.py,在parse_model函数中找到 elif m is nn.BatchNorm2d:语句,在其后面加上下面代码:
elif m in (DSConv, DSConv_C3):c1, c2 = ch[f], args[0]if c2 != nc:c2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m is DSConv_C3:args.insert(2, n) # number of repeatsn = 1
如下图所示:
第③步:创建自定义的yaml文件
第1种,替换conv结构
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license# Parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.5 # 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# YOLOv5 v6.0 backbone
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]], # 13[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 4], 1, Concat, [1]], # cat backbone P3[-1, 3, DSConv, [256, 3,1,1,True]], # 17 (P3/8-small)[-1, 1, Conv, [256, 3, 2]],[[-1, 14], 1, Concat, [1]], # cat head P4[-1, 3, DSConv, [512, 3,1,1,True]], # 20 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]],[[-1, 10], 1, Concat, [1]], # cat head P5[-1, 3, DSConv, [1024, 3,1,1,True]], # 23 (P5/32-large)[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)]
这里要注意一个问题,替换时DSConv参数是需要做对应修改:
如下图栗子所示:
如果直接改模块名会出现缺参报错:
TypeError: __init__() missing 2 required positional arguments: 'morph' and 'if_offset'
第2种,替换C3模块
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license# Parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.5 # 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# YOLOv5 v6.0 backbone
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, DSConv_C3, [128]],[-1, 1, Conv, [256, 3, 2]], # 3-P3/8[-1, 6, DSConv_C3, [256]],[-1, 1, Conv, [512, 3, 2]], # 5-P4/16[-1, 9, DSConv_C3, [512]],[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32[-1, 3, DSConv_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]], # 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)[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)]
替换C3模块直接改模块名字就行。
代码参考:
改进YOLO系列 | YOLOv5/v7 引入 Dynamic Snake Convolution | 动态蛇形卷积_yolov7加入dynamic snake convolution-CSDN博客
第④步:验证是否加入成功
运行yolo.py
第1种
第2种
这样就OK啦!
🌟本人YOLOv5系列导航
🍀YOLOv5源码详解系列:
YOLOv5源码逐行超详细注释与解读(1)——项目目录结构解析
YOLOv5源码逐行超详细注释与解读(2)——推理部分detect.py
YOLOv5源码逐行超详细注释与解读(3)——训练部分train.py
YOLOv5源码逐行超详细注释与解读(4)——验证部分val(test).py
YOLOv5源码逐行超详细注释与解读(5)——配置文件yolov5s.yaml
YOLOv5源码逐行超详细注释与解读(6)——网络结构(1)yolo.py
YOLOv5源码逐行超详细注释与解读(7)——网络结构(2)common.py
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