YOLO模型缝合实战指南：ECA注意力模块的实现与集成
YOLO模型缝合实战指南：ECA注意力模块的实现与集成
1. 引言
在目标检测领域，YOLO (You Only Look Once) 系列模型因其出色的性能和实时性而广受欢迎。本文将详细介绍如何将ECA（Efficient Channel Attention）注意力模块缝合到YOLO模型中，以提升检测效果。
2. ECA模块简介
ECA（Efficient Channel Attention）是一种轻量级的通道注意力机制，它通过自适应地捕获通道间的依赖关系来提升模型性能。相比于SENet，ECA模块具有更少的参数量和更高的效率。
3. 实现步骤
3.1 创建ECA模块
首先，在ultralytics/nn/modules/目录下创建ECA.py文件：
import torch.nn as nn
import torch
class ECA(nn.Module):
"""Efficient Channel Attention Module.
Args:
c1 (int): Input channels
c2 (int): Output channels (usually same as c1)
k_size (int, optional): Kernel size for the 1D convolution. Defaults to 3.
"""
def __init__(self, c1, c2, k_size=3):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
y = self.avg_pool(x)
y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
y = self.sigmoid(y)
return x * y.expand_as(x)
3.2 更新模块导入
在ultralytics/nn/modules/__init__.py中添加ECA模块的导入：
from .transformer import (
AIFI,
MLP,
DeformableTransformerDecoder,
DeformableTransformerDecoderLayer,
LayerNorm2d,
MLPBlock,
MSDeformAttn,
TransformerBlock,
TransformerEncoderLayer,
TransformerLayer,
)
from .ECA import ECA as ECAModule
3.3 修改任务文件
在ultralytics/nn/tasks.py中进行以下修改：
- 添加ECA模块的导入：
from ultralytics.nn.modules import (
DPF,
CGAFusion,
DetailAttentionModule,
ECAModule, # 新增ECA模块
AIFI,
C1,
C2,
C2PSA,
)
- 在
parse_model函数中添加ECA模块支持：
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,
ECAModule, # 新增ECA模块支持
DetailAttentionModule,
}:
c1, c2 = ch[f], args[0]
if c2 != no: # if not output
c2 = make_divisible(c2 * gw, 8)
args = [c1, c2, *args[1:]]
if m in {C2fPSA, C2PSA}:
args.insert(2, n) # number of repeats
n = 1
- 修改
_predict_once函数以添加调试信息：
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
# 添加调试信息
print(f"\n当前层: {m.i}, 类型: {m.type}")
if isinstance(x, (list, tuple)):
print(f"输入形状: {[t.shape for t in x]}")
else:
print(f"输入形状: {x.shape}")
if profile:
self._profile_one_layer(m, x, dt)
x = m(x) # run
# 添加调试信息
if isinstance(x, (list, tuple)):
print(f"输出形状: {[t.shape for t in x]}")
else:
print(f"输出形状: {x.shape}")
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
4. 在YOLO中使用ECA
4.1 创建模型配置文件
在ultralytics/cfg/models/v9/目录下创建yolov9t-ECA.yaml文件：
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv9t object detection model. For Usage examples see https://docs.ultralytics.com/models/yolov9
# 917 layers, 2128720 parameters, 8.5 GFLOPs
#YOLOv9t-ECA summary: 925 layers, 2,807,131 parameters, 2,807,115 gradients, 14.3 GFLOPs
# Parameters
nc: 80 # number of classes
# GELAN backbone
backbone:
- [-1, 1, Conv, [16, 3, 2]] # 0-P1/2
- [-1, 1, Conv, [32, 3, 2]] # 1-P2/4
- [-1, 1, ELAN1, [32, 32, 16]] # 2
- [-1, 1, AConv, [64]] # 3-P3/8
- [-1, 1, RepNCSPELAN4, [64, 64, 32, 3]] # 4
- [-1, 1, AConv, [96]] # 5-P4/16
- [-1, 1, RepNCSPELAN4, [96, 96, 48, 3]] # 6
- [-1, 1, AConv, [128]] # 7-P5/32
- [-1, 1, RepNCSPELAN4, [128, 128, 64, 3]] # 8
- [-1, 1, SPPELAN, [128, 64]] # 9
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 1, RepNCSPELAN4, [96, 96, 48, 3]] # 12
- [-1, 1, ECAModule, [96]] # 添加ECA模块
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 1, RepNCSPELAN4, [64, 64, 32, 3]] # 15
- [-1, 1, ECAModule, [64]] # 添加ECA模块
- [-1, 1, AConv, [48]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 1, RepNCSPELAN4, [96, 96, 48, 3]] # 18 (P4/16-medium)
- [-1, 1, AConv, [64]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 1, RepNCSPELAN4, [128, 128, 64, 3]] # 21 (P5/32-large)
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
4.2 创建训练脚本
创建train.py文件：
from ultralytics import YOLO
def main():
# 加载轻量化模型配置
model = YOLO('yolov9t-ECA.yaml')
# 训练模型
model.train(
# 基础配置
data='D:/archive/data.yaml', # 改为你的数据路径
epochs=100,
imgsz=640,
batch=32,
device='0',
workers=8,
# 早停和学习率
patience=20,
lr0=0.01,
# 数据增强参数
mosaic=1.0,
mixup=0.5,
hsv_h=0.015,
hsv_s=0.7,
hsv_v=0.4,
degrees=45,
translate=0.1,
scale=0.5,
# 输出配置
project='runs/detect_test',
name='fruit_exp_test',
# 保存和可视化配置
save_period=10,
exist_ok=True,
plots=True,
)
if __name__ == '__main__':
main()
predict_once输出的调试信息
成功训练：
参考文献
- ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
- YOLOv8官方文档
- Ultralytics YOLO文档