爆改YOLOv8 |YOLOv8融合SEAM注意力机制

1，本文介绍

SEAM（Spatially Enhanced Attention Module）是一个注意力网络模块，旨在解决面部遮挡导致的响应损失问题。通过使用深度可分离卷积和残差连接的组合，SEAM模块增强未遮挡面部的响应。深度可分离卷积在每个通道上独立操作，减少了参数量，但忽略了通道间的关系。为了解决这一问题，SEAM将不同深度卷积的输出通过1x1卷积结合，再通过两层全连接网络融合每个通道的信息，以提升通道间的联系，从而弥补遮挡造成的损失。

本文将讲解如何将SEAM融合进yolov8

话不多说，上代码！

2，将SEAM融合进yolov8

2.1 步骤一

找到如下的目录'ultralytics/nn/modules'，然后在这个目录下创建一个seam.py文件，文件名字可以根据你自己的习惯起，然后将seam的核心代码复制进去

import torch
import torch.nn as nn__all__ = ['SEAM', 'MultiSEAM']class Residual(nn.Module):def __init__(self, fn):super(Residual, self).__init__()self.fn = fndef forward(self, x):return self.fn(x) + xclass SEAM(nn.Module):def __init__(self, c1,  n=1, reduction=16):super(SEAM, self).__init__()c2 = c1self.DCovN = nn.Sequential(# nn.Conv2d(c1, c2, kernel_size=3, stride=1, padding=1, groups=c1),# nn.GELU(),# nn.BatchNorm2d(c2),*[nn.Sequential(Residual(nn.Sequential(nn.Conv2d(in_channels=c2, out_channels=c2, kernel_size=3, stride=1, padding=1, groups=c2),nn.GELU(),nn.BatchNorm2d(c2))),nn.Conv2d(in_channels=c2, out_channels=c2, kernel_size=1, stride=1, padding=0, groups=1),nn.GELU(),nn.BatchNorm2d(c2)) for i in range(n)])self.avg_pool = torch.nn.AdaptiveAvgPool2d(1)self.fc = nn.Sequential(nn.Linear(c2, c2 // reduction, bias=False),nn.ReLU(inplace=True),nn.Linear(c2 // reduction, c2, bias=False),nn.Sigmoid())self._initialize_weights()# self.initialize_layer(self.avg_pool)self.initialize_layer(self.fc)def forward(self, x):b, c, _, _ = x.size()y = self.DCovN(x)y = self.avg_pool(y).view(b, c)y = self.fc(y).view(b, c, 1, 1)y = torch.exp(y)return x * y.expand_as(x)def _initialize_weights(self):for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.xavier_uniform_(m.weight, gain=1)elif isinstance(m, nn.BatchNorm2d):nn.init.constant_(m.weight, 1)nn.init.constant_(m.bias, 0)def initialize_layer(self, layer):if isinstance(layer, (nn.Conv2d, nn.Linear)):torch.nn.init.normal_(layer.weight, mean=0., std=0.001)if layer.bias is not None:torch.nn.init.constant_(layer.bias, 0)def DcovN(c1, c2, depth, kernel_size=3, patch_size=3):dcovn = nn.Sequential(nn.Conv2d(c1, c2, kernel_size=patch_size, stride=patch_size),nn.SiLU(),nn.BatchNorm2d(c2),*[nn.Sequential(Residual(nn.Sequential(nn.Conv2d(in_channels=c2, out_channels=c2, kernel_size=kernel_size, stride=1, padding=1, groups=c2),nn.SiLU(),nn.BatchNorm2d(c2))),nn.Conv2d(in_channels=c2, out_channels=c2, kernel_size=1, stride=1, padding=0, groups=1),nn.SiLU(),nn.BatchNorm2d(c2)) for i in range(depth)])return dcovnclass MultiSEAM(nn.Module):def __init__(self, c1,  depth=1, kernel_size=3, patch_size=[3, 5, 7], reduction=16):super(MultiSEAM, self).__init__()c2 = c1self.DCovN0 = DcovN(c1, c2, depth, kernel_size=kernel_size, patch_size=patch_size[0])self.DCovN1 = DcovN(c1, c2, depth, kernel_size=kernel_size, patch_size=patch_size[1])self.DCovN2 = DcovN(c1, c2, depth, kernel_size=kernel_size, patch_size=patch_size[2])self.avg_pool = torch.nn.AdaptiveAvgPool2d(1)self.fc = nn.Sequential(nn.Linear(c2, c2 // reduction, bias=False),nn.ReLU(inplace=True),nn.Linear(c2 // reduction, c2, bias=False),nn.Sigmoid())def forward(self, x):b, c, _, _ = x.size()y0 = self.DCovN0(x)y1 = self.DCovN1(x)y2 = self.DCovN2(x)y0 = self.avg_pool(y0).view(b, c)y1 = self.avg_pool(y1).view(b, c)y2 = self.avg_pool(y2).view(b, c)y4 = self.avg_pool(x).view(b, c)y = (y0 + y1 + y2 + y4) / 4y = self.fc(y).view(b, c, 1, 1)y = torch.exp(y)return x * y.expand_as(x)

2.2 步骤二

首先找到如下的目录'ultralytics/nn/modules'，然后在这个目录下找到init文件,在init中添加如下代码.

from .seam import (MultiSEAM,SEAM
)

同时在init.py中的如下位置添加SEAM，MultiSEAM

2.3 步骤三

在task.py中导入SEAM

2.4 步骤四

在task.py中添加如下代码.

到此注册成功,复制后面的yaml文件直接运行即可

有两种yaml文件，可以自行选择

yaml文件一（seam)

# 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 GFLOPss: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPsm: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPsl: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOP# YOLOv8.0n 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, 3, C2f, [128, True]]- [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8- [-1, 6, C2f, [256, True]]- [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16- [-1, 6, C2f, [512, True]]- [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32- [-1, 3, C2f, [1024, True]]- [-1, 1, SPPF, [1024, 5]]  # 9# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 6], 1, Concat, [1]]  # cat backbone P4- [-1, 3, C2f, [512]]  # 12- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 4], 1, Concat, [1]]  # cat backbone P3- [-1, 3, C2f, [256]]  # 15 (P3/8-small)- [-1, 1, SEAM, []]  # 16- [-1, 1, Conv, [256, 3, 2]]- [[-1, 12], 1, Concat, [1]]  # cat head P4- [-1, 3, C2f, [512]]  # 19 (P4/16-medium)- [-1, 1, SEAM, []]  # 20- [-1, 1, Conv, [512, 3, 2]]- [[-1, 9], 1, Concat, [1]]  # cat head P5- [-1, 3, C2f, [1024]]  # 23 (P5/32-large)- [-1, 1, SEAM, []]  # 24- [[16, 20, 24], 1, Detect, [nc]]  # Detect(P3, P4, P5)

yaml文件二（multiseam)


# 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 GFLOPss: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPsm: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPsl: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOP# YOLOv8.0n 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, 3, C2f, [128, True]]- [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8- [-1, 6, C2f, [256, True]]- [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16- [-1, 6, C2f, [512, True]]- [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32- [-1, 3, C2f, [1024, True]]- [-1, 1, SPPF, [1024, 5]]  # 9# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 6], 1, Concat, [1]]  # cat backbone P4- [-1, 3, C2f, [512]]  # 12- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 4], 1, Concat, [1]]  # cat backbone P3- [-1, 3, C2f, [256]]  # 15 (P3/8-small)- [-1, 1, MultiSEAM, []]  # 16- [-1, 1, Conv, [256, 3, 2]]- [[-1, 12], 1, Concat, [1]]  # cat head P4- [-1, 3, C2f, [512]]  # 19 (P4/16-medium)- [-1, 1, MultiSEAM, []]  # 20- [-1, 1, Conv, [512, 3, 2]]- [[-1, 9], 1, Concat, [1]]  # cat head P5- [-1, 3, C2f, [1024]]  # 23 (P5/32-large)- [-1, 1, MultiSEAM, []]  # 24- [[16, 20, 24], 1, Detect, [nc]]  # Detect(P3, P4, P5)