YOLOv8优化：注意力系列篇 | 线性上下文变换LCT，性能优于SE等注意力-灵析社区
神机妙算🚀🚀🚀本文改进：线性上下文变换LCT，引入到YOLOv8，多种实现方式
🚀🚀🚀LCT在不同检测领域中应用广泛
🚀🚀🚀YOLOv8改进专栏：http://t.csdnimg.cn/hGhVK
1.LCT介绍
AAAI 2020
摘要：
在本研究中，我们首先重新审视了SE块，然后基于全局上下文和注意力分布之间的关系进行了详细的实证研究，基于此提出了一个简单而有效的模块，称为线性上下文变换（LCT）块。我们将所有通道分成不同的组，并在每个通道组内对全局聚合的上下文特征进行归一化，减少了来自无关通道的干扰。通过对归一化的上下文特征进行线性变换，我们独立地为每个通道建模全局上下文。LCT块非常轻量级，易于插入不同的主干模型，同时增加的参数和计算负担可以忽略不计。大量实验证明，在ImageNet图像分类任务和COCO数据集上的目标检测/分割任务中，LCT块在不同主干模型上的性能优于SE块。此外，LCT在现有最先进的检测架构上都能带来一致的性能提升，例如在COCO基准测试中，无论基线模型的容量如何，APbbox提升1.5∼1.7%，APmask提升1.0%∼1.2%。我们希望我们的简单而有效的方法能为基于注意力的模型的未来研究提供一些启示。
LCT结构图：
实验：
分类任务很重，LCT优于SE
检测任务重，ap提升1.5∼1.7%
2.LCT加入YOLOv8
2.1加入ultralytics/nn/attention/attention.py
###################### LCT attention #### start ###############################
"""
PyTorch implementation of Linear Context Transform Block
As described in https://arxiv.org/pdf/1909.03834v2
"""
import torch
from torch import nn
class LCTattention(nn.Module):
def __init__(self, channels, groups, eps=1e-5):
super().__init__()
assert channels % groups == 0, "Number of channels should be evenly divisible by the number of groups"
self.groups = groups
self.channels = channels
self.eps = eps
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.w = nn.Parameter(torch.ones(channels))
self.b = nn.Parameter(torch.zeros(channels))
self.sigmoid = nn.Sigmoid()
def forward(self, x):
batch_size = x.shape[0]
y = self.avgpool(x).view(batch_size, self.groups, -1)
mean = y.mean(dim=-1, keepdim=True)
mean_x2 = (y ** 2).mean(dim=-1, keepdim=True)
var = mean_x2 - mean ** 2
y_norm = (y - mean) / torch.sqrt(var + self.eps)
y_norm = y_norm.reshape(batch_size, self.channels, 1, 1)
y_norm = self.w.reshape(1, -1, 1, 1) * y_norm + self.b.reshape(1, -1, 1, 1)
y_norm = self.sigmoid(y_norm)
return x * y_norm.expand_as(x)
###################### LCT attention #### END ###############################
2.2 修改tasks.py
首先LCTattention进行注册
from ultralytics.nn.attention.attention import *函数def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)进行修改
if m in (Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, RepC3,LCTattention):
c1, c2 = ch[f], args[0]2.3 yaml实现
2.3.1 yolov8_LCTattention.yaml
加入backbone SPPF后
# 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 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
# 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
- [-1, 1, LCTattention, [1024]] # 10
# YOLOv8.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2f, [512]] # 13
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f, [1024]] # 22 (P5/32-large)
- [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)2.3.2 yolov8_LCTattention2.yaml
neck里的连接Detect的3个C2f结合
# 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 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
# 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, LCTattention, [256]] # 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, LCTattention, [512]] # 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, LCTattention, [1024]] # 24
- [[16, 20, 24], 1, Detect, [nc]] # Detect(P3, P4, P5)2.3.3 yolov8_LCTattention3.yaml
放入neck的C2f后面
# Ultralytics YOLO 🚀, GPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 1 # 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 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
# 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, LCTattention, [512]] # 13
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
- [-1, 1, LCTattention, [256]] # 17 (P5/32-large)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 20 (P4/16-medium)
- [-1, 1, LCTattention, [512]] # 21 (P5/32-large)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f, [1024]] # 24 (P5/32-large)
- [-1, 1, LCTattention, [1024]] # 25 (P5/32-large)
- [[17, 21, 25], 1, Detect, [nc]] # Detect(P3, P4, P5)阅读量：1444
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