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YOLO融合MogaNet中的ChannelAggregationFFN模块

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YOLOv11v10v8使用教程： YOLOv11入门到入土使用教程

YOLOv11改进汇总贴：YOLOv11及自研模型更新汇总

《MogaNet: Multi-order Gated Aggregation Network》

一、模块介绍

论文链接：https://arxiv.org/abs/2211.03295

代码链接：https://github.com/AIFengheshu/Plug-play-modules

论文速览：

通过将内核尽可能全局化，现代 ConvNet 在计算机视觉任务中显示出巨大的潜力。然而，深度神经网络（DNN）中多阶博弈论交互的最新进展揭示了现代ConvNet的表示瓶颈，即随着核大小的增加，表达互没有得到有效编码。为了应对这一挑战，我们提出了一个新的现代 ConvNet 系列，称为 MogaNet，用于在纯基于 ConvNet 的模型中进行判别视觉表示学习，并具有良好的复杂性-性能权衡。MogaNet将概念上简单而有效的卷积和门控聚合封装到一个紧凑的模块中，其中有效地收集了判别特征并自适应地进行情境化。与 ImageNet 上最先进的 ViT 和 ConvNet 以及各种下游视觉基准测试（包括 COCO 对象检测、ADE20K 语义分割、2D 和 3D 人体姿态估计和视频预测）相比，MogaNet 表现出强大的可扩展性、令人印象深刻的参数效率和具有竞争力的性能。

总结：。

⭐⭐本文二创模块仅更新于付费群中，往期免费教程可看下方链接⭐⭐

YOLOv11及自研模型更新汇总（含免费教程）文章浏览阅读366次，点赞3次，收藏4次。群文件2024/11/08日更新。,群文件2024/11/08日更新。_yolo11部署自己的数据集https://xy2668825911.blog.csdn.net/article/details/143633356https://xy2668825911.blog.csdn.net/article/details/143633356

二、二创融合模块

2.1 相关代码

# MogaNet: Multi-order Gated Aggregation Network
# https://arxiv.org/pdf/2211.03295
# https://blog.csdn.net/StopAndGoyyy?spm=1011.2124.3001.5343
# https://github.com/AIFengheshu/Plug-play-modules
def build_act_layer(act_type):#Build activation layerif act_type is None:return nn.Identity()assert act_type in ['GELU', 'ReLU', 'SiLU']if act_type == 'SiLU':return nn.SiLU()elif act_type == 'ReLU':return nn.ReLU()else:return nn.GELU()class ElementScale(nn.Module):#A learnable element-wise scaler.def __init__(self, embed_dims, init_value=0., requires_grad=True):super(ElementScale, self).__init__()self.scale = nn.Parameter(init_value * torch.ones((1, embed_dims, 1, 1)),requires_grad=requires_grad)def forward(self, x):return x * self.scaleclass ChannelAggregationFFN(nn.Module):"""An implementation of FFN with Channel Aggregation.Args:embed_dims (int): The feature dimension. Same as`MultiheadAttention`.feedforward_channels (int): The hidden dimension of FFNs.kernel_size (int): The depth-wise conv kernel size as thedepth-wise convolution. Defaults to 3.act_type (str): The type of activation. Defaults to 'GELU'.ffn_drop (float, optional): Probability of an element to bezeroed in FFN. Default 0.0."""def __init__(self,embed_dims,kernel_size=3,act_type='GELU',ffn_drop=0.):super(ChannelAggregationFFN, self).__init__()self.embed_dims = embed_dimsself.feedforward_channels = int(embed_dims * 4)self.fc1 = nn.Conv2d(in_channels=embed_dims,out_channels=self.feedforward_channels,kernel_size=1)self.dwconv = nn.Conv2d(in_channels=self.feedforward_channels,out_channels=self.feedforward_channels,kernel_size=kernel_size,stride=1,padding=kernel_size // 2,bias=True,groups=self.feedforward_channels)self.act = build_act_layer(act_type)self.fc2 = nn.Conv2d(in_channels=self.feedforward_channels,out_channels=embed_dims,kernel_size=1)self.drop = nn.Dropout(ffn_drop)self.decompose = nn.Conv2d(in_channels=self.feedforward_channels,  # C -> 1out_channels=1, kernel_size=1,)self.sigma = ElementScale(self.feedforward_channels, init_value=1e-5, requires_grad=True)self.decompose_act = build_act_layer(act_type)def feat_decompose(self, x):# x_d: [B, C, H, W] -> [B, 1, H, W]x = x + self.sigma(x - self.decompose_act(self.decompose(x)))return xdef forward(self, x):# proj 1x = self.fc1(x)x = self.dwconv(x)x = self.act(x)x = self.drop(x)# proj 2x = self.feat_decompose(x)x = self.fc2(x)x = self.drop(x)return x

2.2更改yaml文件 (以自研模型为例)

yam文件解读：YOLO系列 “.yaml“文件解读_yolo yaml文件-CSDN博客

打开更改ultralytics/cfg/models/11路径下的YOLOv11.yaml文件，替换原有模块。

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# ⭐⭐Powered by https://blog.csdn.net/StopAndGoyyy,  技术指导QQ:2668825911⭐⭐# 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: 377 layers, 2,249,525 parameters, 2,249,509 gradients, 8.7 GFLOPs/258 layers, 2,219,405 parameters, 0 gradients, 8.5 GFLOPss: [0.50, 0.50, 1024] # summary: 377 layers, 8,082,389 parameters, 8,082,373 gradients, 29.8 GFLOPs/258 layers, 7,972,885 parameters, 0 gradients, 29.2 GFLOPsm: [0.50, 1.00, 512] # summary:  377 layers, 20,370,221 parameters, 20,370,205 gradients, 103.0 GFLOPs/258 layers, 20,153,773 parameters, 0 gradients, 101.2 GFLOPsl: [1.00, 1.00, 512] # summary: 521 layers, 23,648,717 parameters, 23,648,701 gradients, 124.5 GFLOPs/330 layers, 23,226,989 parameters, 0 gradients, 121.2 GFLOPsx: [1.00, 1.50, 512] # summary: 521 layers, 53,125,237 parameters, 53,125,221 gradients, 278.9 GFLOPs/330 layers, 52,191,589 parameters, 0 gradients, 272.1 GFLOPs#  n: [0.33, 0.25, 1024]
#  s: [0.50, 0.50, 1024]
#  m: [0.67, 0.75, 768]
#  l: [1.00, 1.00, 512]
#  x: [1.00, 1.25, 512]
# 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, RCRep2A, [128, False, 0.25]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 4, RCRep2A, [256, False, 0.25]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 4, RCRep2A, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 2, RCRep2A, [1024, True]]- [-1, 1, ChannelAggregationFFN, []] # 9# YOLO11n head
head:- [[3, 5, 7], 1, align_3In, [256, 1]] # 10- [[4, 6, 9], 1, align_3In, [256, 1]] # 11- [[-1, -2], 1, Concat, [1]] #12  cat- [-1, 1, RepVGGBlocks, []] #13- [-1, 1, nn.Upsample, [None, 2, "nearest"]] #14- [[-1, 4], 1, Concat, [1]] #15 cat- [-1, 1, Conv, [256, 3]] # 16- [13, 1, Conv, [512, 3]] #17- [13, 1, Conv, [1024, 3, 2]] #18- [[16, 17, 18], 1, Detect, [nc]] # Detect(P3, P4, P5)# ⭐⭐Powered by https://blog.csdn.net/StopAndGoyyy,  技术指导QQ:2668825911⭐⭐

2.3 修改train.py文件

创建Train脚本用于训练。

from ultralytics.models import YOLO
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'if __name__ == '__main__':model = YOLO(model='ultralytics/cfg/models/xy_YOLO/xy_yolov1.yaml')# model = YOLO(model='ultralytics/cfg/models/11/yolo11l.yaml')model.train(data='./datasets/data.yaml', epochs=1, batch=1, device='0', imgsz=320, workers=1, cache=False,amp=True, mosaic=False, project='run/train', name='exp',)