YOLOV7改进--添加CBAM注意力机制

YOLOV7改进--添加CBAM注意力机制

CBAM注意力机制代码在commen.py中添加CBAM模块在yolo.py中添加CBAM模块名在cfg文件中添加CBAM信息
因为项目需要，尝试在yolov7上加入CBAM注意力机制，看看能不能提升点性能。之前有在yolov5上添加CBAM的经验，所以直接把yolov5中的CBAM搬过来，废话不多说，直接看代码吧！

CBAM注意力机制

首先，介绍一下CBAM注意力机制：
论文来源：https://arxiv.org/pdf/1807.06521.pdf

Convolutional Block Attention Module (CBAM)由两个模块构成，分别为通道注意力（CAM）和空间注意力模块（SAM），CAM可以使网络关注图像的前景，使网络更加关注有意义的gt区域，而SAM可以让网络关注到整张图片中富含上下文信息的位置。这两个模块即插即用，建议串行加入到网络中（论文里面是串行比并行好，在博主的数据集下，并行和串行效果不明显，博主认为特征融合没有苛刻的要求，视使用的数据集而定，怎么连效果好就怎么连），下面的展示的代码是串行方法。

代码

在commen.py中添加CBAM模块

这部分代码同yolov5的一样，直接拿来用！

class ChannelAttention(nn.Module):    def __init__(self, in_planes, ratio=16):        super(ChannelAttention, self).__init__()        self.avg_pool = nn.AdaptiveAvgPool2d(1)        self.max_pool = nn.AdaptiveMaxPool2d(1)        self.f1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False)        self.relu = nn.ReLU()        self.f2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False)        self.sigmoid = nn.Sigmoid()    def forward(self, x):        avg_out = self.f2(self.relu(self.f1(self.avg_pool(x))))        max_out = self.f2(self.relu(self.f1(self.max_pool(x))))        out = self.sigmoid(avg_out + max_out)        return outclass SpatialAttention(nn.Module):    def __init__(self, kernel_size=7):        super(SpatialAttention, self).__init__()        assert kernel_size in (3, 7), 'kernel size must be 3 or 7'        padding = 3 if kernel_size == 7 else 1        self.conv = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)        self.sigmoid = nn.Sigmoid()    def forward(self, x):        avg_out = torch.mean(x, dim=1, keepdim=True)        max_out, _ = torch.max(x, dim=1, keepdim=True)        x = torch.cat([avg_out, max_out], dim=1)        x = self.conv(x)        return self.sigmoid(x)        class CBAM(nn.Module):    # Standard convolution    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups        super(CBAM, self).__init__()        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)        self.bn = nn.BatchNorm2d(c2)        self.act = nn.Hardswish() if act else nn.Identity()        self.ca = ChannelAttention(c2)        self.sa = SpatialAttention()    def forward(self, x):        x = self.act(self.bn(self.conv(x)))        x = self.ca(x) * x        x = self.sa(x) * x        return x    def fuseforward(self, x):        return self.act(self.conv(x))

在yolo.py中添加CBAM模块名

找到yolo.py第459行，加入CBAM模块名。

if m in [nn.Conv2d, Conv, RobustConv, RobustConv2, DWConv, GhostConv, RepConv, RepConv_OREPA, DownC,                  SPP, SPPF, SPPCSPC, GhostSPPCSPC, MixConv2d, Focus, Stem, GhostStem, CrossConv,                  Bottleneck, BottleneckCSPA, BottleneckCSPB, BottleneckCSPC,                  RepBottleneck, RepBottleneckCSPA, RepBottleneckCSPB, RepBottleneckCSPC,                   Res, ResCSPA, ResCSPB, ResCSPC,                  RepRes, RepResCSPA, RepResCSPB, RepResCSPC,                  ResX, ResXCSPA, ResXCSPB, ResXCSPC,                  RepResX, RepResXCSPA, RepResXCSPB, RepResXCSPC,                  Ghost, GhostCSPA, GhostCSPB, GhostCSPC,                 SwinTransformerBlock, STCSPA, STCSPB, STCSPC,                 SwinTransformer2Block, ST2CSPA, ST2CSPB, ST2CSPC, CBAM]:    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 [DownC, SPPCSPC, GhostSPPCSPC,                      BottleneckCSPA, BottleneckCSPB, BottleneckCSPC,                      RepBottleneckCSPA, RepBottleneckCSPB, RepBottleneckCSPC,                      ResCSPA, ResCSPB, ResCSPC,                      RepResCSPA, RepResCSPB, RepResCSPC,                      ResXCSPA, ResXCSPB, ResXCSPC,                      RepResXCSPA, RepResXCSPB, RepResXCSPC,                     GhostCSPA, GhostCSPB, GhostCSPC,                     STCSPA, STCSPB, STCSPC,                     ST2CSPA, ST2CSPB, ST2CSPC]:         args.insert(2, n)  # number of repeats         n = 1

在cfg文件中添加CBAM信息

这里以添加到backbone为例，将Conv替换成CBAM即可，同样也可在FPN里替换。

# parametersnc: 80  # number of classesdepth_multiple: 1.0  # model depth multiplewidth_multiple: 1.0  # layer channel multiple# anchorsanchors:  - [10,13, 16,30, 33,23]  # P3/8  - [30,61, 62,45, 59,119]  # P4/16  - [116,90, 156,198, 373,326]  # P5/32backbone:  # [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True  # [[-1, 1, Conv, [32, 3, 2, None, 1, nn.LeakyReLU(0.1)]],  # 0-P1/2   [[-1, 1, CBAM, [32, 3, 2, None, 1, nn.LeakyReLU(0.1)]],  # 0-P1/2      #  [-1, 1, Conv, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]],  # 1-P2/4     [-1, 1, CBAM, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]],  # 1-P2/4          [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [[-1, -2, -3, -4], 1, Concat, [1]],   [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 7      [-1, 1, MP, []],  # 8-P3/8   [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [[-1, -2, -3, -4], 1, Concat, [1]],   [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 14      [-1, 1, MP, []],  # 15-P4/16   [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [[-1, -2, -3, -4], 1, Concat, [1]],   [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 21      [-1, 1, MP, []],  # 22-P5/32   [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [[-1, -2, -3, -4], 1, Concat, [1]],   [-1, 1, Conv, [512, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 28  ]# yolov7-tiny headhead:  [[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, SP, [5]],   [-2, 1, SP, [9]],   [-3, 1, SP, [13]],   [[-1, -2, -3, -4], 1, Concat, [1]],   [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [[-1, -7], 1, Concat, [1]],   [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 37     [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, nn.Upsample, [None, 2, 'nearest']],   [21, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4   [[-1, -2], 1, Concat, [1]],      [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [[-1, -2, -3, -4], 1, Concat, [1]],   [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 47     [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, nn.Upsample, [None, 2, 'nearest']],   [14, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P3   [[-1, -2], 1, Concat, [1]],      [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [[-1, -2, -3, -4], 1, Concat, [1]],   [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 57      [-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]],   [[-1, 47], 1, Concat, [1]],      [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [[-1, -2, -3, -4], 1, Concat, [1]],   [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 65      [-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]],   [[-1, 37], 1, Concat, [1]],      [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [[-1, -2, -3, -4], 1, Concat, [1]],   [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 73         [57, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [65, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [73, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]],   [[74,75,76], 1, Detect, [nc, anchors]],   # Detect(P3, P4, P5)  ]

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