yolov5添加注意力机制的最好效果的代码展示出来

在 YOLOv5 中添加注意力机制可以提高目标检测的精度和性能。以下是添加注意力机制的代码示例：

# 定义注意力模块
class CBAM(nn.Module):
    def __init__(self, planes, reduction_ratio=16, pool_types=['avg', 'max']):
        super(CBAM, self).__init__()
        self.mlp = nn.Sequential(
            OrderedDict([
                ('fc1', nn.Linear(planes, planes // reduction_ratio)),
                ('relu1', nn.ReLU()),
                ('fc2', nn.Linear(planes // reduction_ratio, planes)),
            ])
        )
        self.conv_block = nn.Sequential(
            nn.Conv2d(in_channels=planes, out_channels=planes, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(planes),
            nn.ReLU(),
            nn.Conv2d(in_channels=planes, out_channels=planes, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(planes)
        )
        self.pool_types = pool_types
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        module_input = x
        batch_size, channels, height, width = x.size()
        # Channel Attention Module
        avg_pool = self.avg_pool(x)
        max_pool = self.max_pool(x)

        if 'avg' in self.pool_types:
            avg_pool = self.mlp(avg_pool.view(batch_size, -1))
        if 'max' in self.pool_types:
            max_pool = self.mlp(max_pool.view(batch_size, -1))

        channel_att_sum = self.sigmoid(avg_pool + max_pool).unsqueeze(2).unsqueeze(3).expand_as(x)
        x = x * channel_att_sum

        # Spatial Attention Module
        x = module_input * self.sigmoid(self.conv_block(x))
        return x

# 定义YOLOv5中的注意力模块
class AttentionBlock(nn.Module):

    def __init__(self, in_channels):
        super(AttentionBlock, self).__init__()

        self.conv_block = nn.Sequential(
            nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(in_channels),
            nn.ReLU(),
            nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(in_channels),
            nn.ReLU()
        )

        self.ca = CBAM(in_channels)
        self.sa = CBAM(in_channels)

    def forward(self, x):
        out = self.conv_block(x)
        out = self.ca(out) * out
        out = self.sa(out) * out
        return out

然后，在 YOLOv5 中添加注意力机制的代码如下所示：

class YOLOv5(nn.Module):
    def __init__(self, num_classes=80, width_mult=1.0, backbone='cspdarknet53'):
        super(YOLOv5, self).__init__()
        ...
        self.backbone = nn.Sequential(*backbone_layers)
        self.neck = nn.Sequential(
            AttentionBlock(in_channels=1024),
            SPPBlock(in_channels=1024, out_channels=1024, pool_sizes=[5, 9, 13]),
            nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.1, inplace=True),
            nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, padding=1),
            nn.BatchNorm2d(1024),
            nn.LeakyReLU(0.1, inplace=True),
            AttentionBlock(in_channels=1024),
            nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.1, inplace=True),
            nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, padding=1),
            nn.BatchNorm2d(1024),
            nn.LeakyReLU(0.1, inplace=True),
            AttentionBlock(in_channels=1024)
        )
        self.head = nn.Sequential(
            ...
        )
        ...

在这里，我们添加了三个注意力块，以增强特征图中的重要特征。然后，将注意力模块添加到 YOLOv5 的 neck 部分中。