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YOLOv5算法改进–通过yaml文件添加注意力机制【附代码】

• 人工智能 • 阅读 59

以往的算法改进中，网络结构一般直接是用代码写好的，个人感觉这样的方式其实更利于算法的改进，更简单明了。但也有很多算法，比如v5，YOLOR，YOLOV7等等，是用yaml文件进行模型的定义，然后通过pare_model()函数读取模型，如果你需要 在模型中添加若干个其他模块，增添自己的想法，通过修改yaml是不易的，但在实际中这又是不可避免的。因此本文章将以YOLOv5 6.0为例，在yaml中添加注意力机制来改进网络。

本文将在yolov5s结构中插入通道注意力机制SENet为例。分别从以下两个方面添加注意力机制：

1.不改变网络深度的改进(直接替换某些层)

2.改变原网络深度(增加层数)

目录

1.不改变网络深度的改进

1.1yaml修改

1.2yolo.py修改

2.改变原网络深度

在添加注意力之前，需要在models/common.py中添加SENet代码。代码如下:

class SE_Block(nn.Module):
    def __init__(self, c1, c2):
        super().__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)  # 平均池化
        self.fc = nn.Sequential(
            nn.Linear(c1, c2 // 16, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(c2 // 16, c2, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        # 添加注意力模块
        b, c, _, _ = x.size()  # 分别获取batch_size,channel
        y = self.avg_pool(x).view(b, c)  # y的shape为【batch_size, channels】
        y = self.fc(y).view(b, c, 1, 1)  # shape为【batch_size, channels, 1, 1】
        out = x * y.expand_as(x)  # shape 为【batch, channels,feature_w, feature_h】
        return out

1.不改变网络深度的改进

网络的修改需要修改两个位置： yaml文件、 models/yolo.py

1.1yaml修改

首先是打开models/yolov5s.yaml文件，我们在backbone中的SPPF之前增加SENet。增添位置如下，是将backbone中第4个C3模块替换为SE_Block，如下图。

【需要注意的是通道数要匹配，SENet并不改变通道数，由于原C3的输出通道数为1024*0.5=512，所以我们这里的写的是1024，这里的1024是传入到上面我们定义的Class SE_Block(nn.Moudel)中的c2参数，c1参数是由上一层的输出通道数控制的】。

YOLOv5算法改进--通过yaml文件添加注意力机制【附代码】 
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2  conv1(3,32,k=6,s=2,p=2)
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4  conv2(32,64,k=3,s=2,p=1)
   [-1, 3, C3, [128]],  # C3_1 有Bottleneck
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8  conv3(64,128,k=3,s=2,p=1)
   [-1, 6, C3, [256]], # C3_2 Bottleneck重复两次
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16  conv4(128,256,k=3,s=2,p=1)
   [-1, 9, C3, [512]], # C3_3 Bottleneck重复三次 输出256通道
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32   Conv5(256,512,k=3,s=2,p=1)
   #[-1, 3, C3, [1024]],  # C3_4 Bottleneck重复1次  输出512通道
   [-1, 1, SE_Block, [1024]],  # 增加通道注意力机制 输出为512通道
   [-1, 1, SPPF, [1024, 5]],  # 9  每个都是K为5的池化

  ]

1.2yolo.py修改

主要是在下面的代码中，在列表中添加SE_Block，这样可以获得我们要传入的参数。 这样就改完了。

if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
                 BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, SE_Block]:

改完以后可以随便训练几轮试试，或者打印一下模型，看看改的对不对。可以看到在我自己的数据集上训练是可以正常进行的，

 Epoch   gpu_mem       box       obj       cls    labels  img_size
     1/299      1.4G   0.05173   0.03137         0         5       640: 100%|████████████████████████████████████████████████████████████████████████| 180/180 [04:09

2.改变原网络深度

比如我要在第一个C3后面加一个SE。yaml的修改如下。接下来稍微麻烦一点了【需要你了解v5的每层结构】，由于我们在backbone中加入了一层，也就是相当于后面的网络与之前相比都往后移动了一层，那么在后面的Concat部分中融合的特征层的索引也会收到影响，因此我们需要的是修改Concat层的from参数。

会改变原来网络中的这些地方：

YOLOv5算法改进--通过yaml文件添加注意力机制【附代码】

YOLOv5算法改进--通过yaml文件添加注意力机制【附代码】 
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2  conv1(3,32,k=6,s=2,p=2)
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4  conv2(32,64,k=3,s=2,p=1)
   [-1, 3, C3, [128]],  # C3_1 有Bottleneck
   [-1, 1, SE_Block, [128]],  # 增加通道注意力机制 输出为512通道
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8  conv3(64,128,k=3,s=2,p=1)
   [-1, 6, C3, [256]], # C3_2 Bottleneck重复两次
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16  conv4(128,256,k=3,s=2,p=1)
   [-1, 9, C3, [512]], # C3_3 Bottleneck重复三次 输出256通道
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32   Conv5(256,512,k=3,s=2,p=1)
   [-1, 3, C3, [1024]],  # C3_4 Bottleneck重复1次  输出512通道
   [-1, 1, SPPF, [1024, 5]],  # 9  每个都是K为5的池化

  ]

可以看到实际就是每个Concat也后面移动一层，因此yaml修改为一下。最终的Detect的from也需要修改。

head:
  [[-1, 1, Conv, [512, 1, 1]],  # conv1(512,256,1,1)
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 7], 1, Concat, [1]],  # cat backbone P4  将C3_3与SPPF出来后的上采样拼接 拼接后的通道为512
   [-1, 3, C3, [512, False]],  # 13  conv(256,256,k=1,s=1)  没有残差边

   [-1, 1, Conv, [256, 1, 1]], # conv2(256,128,1,1)
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 5], 1, Concat, [1]],  # cat backbone P3  与C3_2拼接，输出256通道
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small) conv3(128,128,1,1)

   [-1, 1, Conv, [256, 3, 2]],# conv4(128,128,3,2,1)
   [[-1, 15], 1, Concat, [1]],  # cat head P4  拼接后256通道
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)  conv5(256,256,1,1)

   [-1, 1, Conv, [512, 3, 2]],# conv6(256,256,3,2,1)
   [[-1, 11], 1, Concat, [1]],  # cat head P5  拼接后是512
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)

   [[18, 21, 24], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

同理，也需要在models/yolo.py中修改如下，增加SE_Block：

if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
                 BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, SE_Block]:

也可以训练一个epoch看看，或者打印一下网络。这样就改完啦。

Starting training for 300 epochs...

     Epoch   gpu_mem       box       obj       cls    labels  img_size
     0/299    0.419G   0.09617   0.03019         0        13       640:  12%|████████▉                                                                | 22/180 [00:15

Model(
  (model): Sequential(
    (0): Conv(
      (conv): Conv2d(3, 32, kernel_size=(6, 6), stride=(2, 2), padding=(2, 2), bias=False)
      (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (1): Conv(
      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (2): C3(
      (cv1): Conv(
        (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv2): Conv(
        (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv3): Conv(
        (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
          (cv2): Conv(
            (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
        )
      )
    )
    (3): SE_Block(
      (avg_pool): AdaptiveAvgPool2d(output_size=1)
      (fc): Sequential(
        (0): Linear(in_features=64, out_features=4, bias=False)
        (1): ReLU(inplace=True)
        (2): Linear(in_features=4, out_features=64, bias=False)
        (3): Sigmoid()
      )
    )
    (4): Conv(
      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (5): C3(
      (cv1): Conv(
        (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv2): Conv(
        (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv3): Conv(
        (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
          (cv2): Conv(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
        )
        (1): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
          (cv2): Conv(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
        )
      )
    )
    (6): Conv(
      (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (7): C3(
      (cv1): Conv(
        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv2): Conv(
        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv3): Conv(
        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
          (cv2): Conv(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
        )
        (1): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
          (cv2): Conv(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
        )
        (2): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
          (cv2): Conv(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
        )
      )
    )
    (8): Conv(
      (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (9): C3(
      (cv1): Conv(
        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv2): Conv(
        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv3): Conv(
        (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
          (cv2): Conv(
            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
        )
      )
    )
    (10): SPPF(
      (cv1): Conv(
        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv2): Conv(
        (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)
    )
    (11): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (12): Upsample(scale_factor=2.0, mode=nearest)
    (13): Concat()
    (14): C3(
      (cv1): Conv(
        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv2): Conv(
        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv3): Conv(
        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
          (cv2): Conv(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
        )
      )
    )
    (15): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (16): Upsample(scale_factor=2.0, mode=nearest)
    (17): Concat()
    (18): C3(
      (cv1): Conv(
        (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv2): Conv(
        (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv3): Conv(
        (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
          (cv2): Conv(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
        )
      )
    )
    (19): Conv(
      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (20): Concat()
    (21): C3(
      (cv1): Conv(
        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv2): Conv(
        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv3): Conv(
        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
          (cv2): Conv(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
        )
      )
    )
    (22): Conv(
      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
      (act): SiLU()
    )
    (23): Concat()
    (24): C3(
      (cv1): Conv(
        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv2): Conv(
        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (cv3): Conv(
        (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
        (act): SiLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
          (cv2): Conv(
            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
            (act): SiLU()
          )
        )
      )
    )
    (25): Detect(
      (m): ModuleList(
        (0): Conv2d(128, 255, kernel_size=(1, 1), stride=(1, 1))
        (1): Conv2d(256, 255, kernel_size=(1, 1), stride=(1, 1))
        (2): Conv2d(512, 255, kernel_size=(1, 1), stride=(1, 1))
      )
    )
  )
)

不论哪种网络的修改，都需要注意的一点是通道维度的对应，如果报错了，例如mat1和mat2无法相乘，或者shape匹配问题，都是你修改后的网络channels不匹配导致的，要细心检查，在增删网络的时候，要注意yaml中from列表参数的影响，因为你修改了一层，将会影响后面的后面层。

Original: https://blog.csdn.net/z240626191s/article/details/126970095
Author: 爱吃肉的鹏
Title: YOLOv5算法改进–通过yaml文件添加注意力机制【附代码】

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