YOLOv8添加注意力模块并测试和训练

YOLOv8添加注意力模块并测试和训练

参考bilibili视频

yolov8代码库中写好了注意力模块，但是yolov8的yaml文件中并没用使用它，如下图的通道注意力和空间注意力以及两者的结合CBAM，打开conv.py文件可以看到，其中包含了各种卷积块的定义，因此yolov8是把通道注意力和空间注意力以及两者的结合CBAM当作卷积块来处理：

2 逐层写入自定义的注意力模块

（1）ultralytics/nn/modules/conv.py中写入自定义的注意力模块：

（2）ultralytics/nn/modules/init.py中添加自定义的注意力模块名：


只有逐层添加模块名，才能封装成ultralytics.nn.modules的内部模块
（3）ultralytics/nn/tasks.py中添加自定义的注意力模块名，以便任务执行时调用自定义的注意力模块。

接着在ultralytics/nn/tasks.py–>parse_model函数中解析yaml文件时，判断是否有自定义的注意力模块：

由于CBAM可以看成只是给卷积块Conv加权重，并不会改变输入、输出通道数，因此可以仿照Conv块的处理，在下面判断的语句中它只会执行以下几句：

c1,c2为输入输出通道数，if 后面的语句是的作用是除了最后一层类别输出通道数，其它层的通道数都要是8的整数倍。args存放了c1,c2和args[1]之后的所有参数组成新的args，需要注意，args至少要两个元素，如果只有一个元素，agrs[1:]时会报错超出范围，因此模型的yaml文件中args位置，必须至少2个元素，如：

- [-1, 3, CBAM, [1024, 7]]	# 输入1024个通道数，kenel size=7

3 修改模型的yaml文件

在ultralytics/cfg/models/v8中复制一个yolov8-seg.yaml文件新建yaml文件命名为yolov8CBAM-seg.yaml：

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8-seg instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n-seg.yaml' will call yolov8-seg.yaml with scale 'n'# [depth, width, max_channels]n: [0.33, 0.25, 1024]s: [0.33, 0.50, 1024]m: [0.67, 0.75, 768]l: [1.00, 1.00, 512]x: [1.00, 1.25, 512]# 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]]     #-->2- [-1, 1, CBAM, [128, 7]] #CBAM 3- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8-->4- [-1, 6, C2f, [256, True]]- [-1, 1, CBAM, [256, 7]]   #CBAM 6- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16-->7- [-1, 6, C2f, [512, True]]- [-1, 1, CBAM, [512, 7]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32-->10- [-1, 3, C2f, [1024, True]]- [-1, 1, CBAM, [1024, 7]]- [-1, 1, SPPF, [1024, 5]] # 9-->13# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 8], 1, Concat, [1]] #[[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 3, C2f, [512]] # 12    -->16- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 5], 1, Concat, [1]] #[[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 3, C2f, [256]] # 15 (P3/8-small)--->19- [-1, 1, Conv, [256, 3, 2]]- [[-1, 16], 1, Concat, [1]]  #[[-1, 12], 1, Concat, [1]] # cat head P4- [-1, 3, C2f, [512]] # 18 (P4/16-medium)-->22- [-1, 1, Conv, [512, 3, 2]]- [[-1, 13], 1, Concat, [1]] #[[-1, 9], 1, Concat, [1]] # cat head P5- [-1, 3, C2f, [1024]] # 21 (P5/32-large)--->25#  - [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)- [[19, 22, 25], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)

这里在主干backbone中的c2f块后面添加了重复一次的CBAM共添加了四个。由于head层需要Concat backbone的相应层，因此，原来的层序号需要逐一修改，注释中 " -->x "表示新的序号，将原来的序号替换成新的即可。

4 测试是否修改成功

复制一份tests/test_python.py文件中的测试代码，新建文件命名为test_yolov8_CBAM_model.py，只保留下方代码:

# Ultralytics YOLO 🚀, AGPL-3.0 licenseimport contextlib
import urllib
from copy import copy
from pathlib import Pathimport cv2
import numpy as np
import pytest
import torch
import yaml
from PIL import Imagefrom tests import CFG, IS_TMP_WRITEABLE, MODEL, SOURCE, TMP
from ultralytics import RTDETR, YOLO
from ultralytics.cfg import MODELS, TASK2DATA, TASKS
from ultralytics.data.build import load_inference_source
from ultralytics.utils import (ASSETS,DEFAULT_CFG,DEFAULT_CFG_PATH,LOGGER,ONLINE,ROOT,WEIGHTS_DIR,WINDOWS,checks,
)
from ultralytics.utils.downloads import download
from ultralytics.utils.torch_utils import TORCH_1_9CFG = 'ultralytics/cfg/models/v8/yolov8l-CBAMseg.yaml'	#使用l模型加一个l字母
SOURCE = ASSETS / "bus.jpg"
def test_model_forward():"""Test the forward pass of the YOLO model."""model = YOLO(CFG)model(source=SOURCE, imgsz=[512,512], augment=True)  # also test no source and augment

先在ultralytics/nn/tasks.py的parse_model函数中增加一行代码用于查看模型结构：

print(f"{i:>3}{str(f):>20}{n_:>3}{m.np:10.0f}  {t:<45}{str(args):<30}")

运行test_yolov8_CBAM_model.py的结果如下：

============================= test session starts ==============================
collected 1 item                                                               test_yolov8_CBAM_model.py::test_model_forward PASSED                     [100%]  0                  -1  1      1856  ultralytics.nn.modules.conv.Conv             [3, 64, 3, 2]                 1                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               2                  -1  3    279808  ultralytics.nn.modules.block.C2f             [128, 128, 3, True]           3                  -1  1     16610  ultralytics.nn.modules.conv.CBAM             [128, 7]                      4                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              5                  -1  6   2101248  ultralytics.nn.modules.block.C2f             [256, 256, 6, True]           6                  -1  1     65890  ultralytics.nn.modules.conv.CBAM             [256, 7]                      7                  -1  1   1180672  ultralytics.nn.modules.conv.Conv             [256, 512, 3, 2]              8                  -1  6   8396800  ultralytics.nn.modules.block.C2f             [512, 512, 6, True]           9                  -1  1    262754  ultralytics.nn.modules.conv.CBAM             [512, 7]                      10                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              11                  -1  3   4461568  ultralytics.nn.modules.block.C2f             [512, 512, 3, True]           12                  -1  1    262754  ultralytics.nn.modules.conv.CBAM             [512, 7]                      13                  -1  1    656896  ultralytics.nn.modules.block.SPPF            [512, 512, 5]                 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          15             [-1, 8]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           16                  -1  3   4723712  ultralytics.nn.modules.block.C2f             [1024, 512, 3]                17                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          18             [-1, 5]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           19                  -1  3   1247744  ultralytics.nn.modules.block.C2f             [768, 256, 3]                 20                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              21            [-1, 16]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           22                  -1  3   4592640  ultralytics.nn.modules.block.C2f             [768, 512, 3]                 23                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              24            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           25                  -1  3   4723712  ultralytics.nn.modules.block.C2f             [1024, 512, 3]                26        [19, 22, 25]  1   7950688  ultralytics.nn.modules.head.Segment          [80, 32, 256, [256, 512, 512]]image 1/1 /XXXXXXXXXXXXXXXXX/ultralyticsv8_2-main/ultralytics/assets/bus.jpg: 640x480 (no detections), 116.5ms
Speed: 2.7ms preprocess, 116.5ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 480)======================== 1 passed, 4 warnings in 7.04s =========================进程已结束,退出代码0

至此，注意力模块添加完成。

5 训练

如上图，这里使用x超大模型，只需yolov8-CBAMseg.yaml中加一个x变成yolov8x-CBAMseg.yaml，优化器为上一篇博客yolov8更改的Lion优化器。可以看到arguments参数按照x模型发生了调整，模型开始训练。