涨点神器：基于Yolov8小目标遮挡物性能提升（SEAM、MultiSEAM）

class SEAM(nn.Module):
    def __init__(self, c1, c2, n, reduction=16):
        super(SEAM, self).__init__()
        if c1 != c2:
            c2 = c1
        self.DCovN = nn.Sequential(
            # nn.Conv2d(c1, c2, kernel_size=3, stride=1, padding=1, groups=c1),
            # nn.GELU(),
            # nn.BatchNorm2d(c2),
            *[nn.Sequential(
                Residual(nn.Sequential(
                    nn.Conv2d(in_channels=c2, out_channels=c2, kernel_size=3, stride=1, padding=1, groups=c2),
                    nn.GELU(),
                    nn.BatchNorm2d(c2)
                )),
                nn.Conv2d(in_channels=c2, out_channels=c2, kernel_size=1, stride=1, padding=0, groups=1),
                nn.GELU(),
                nn.BatchNorm2d(c2)
            ) for i in range(n)]
        )
        self.avg_pool = torch.nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(c2, c2 // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(c2 // reduction, c2, bias=False),
            nn.Sigmoid()
        )

        self._initialize_weights()
        # self.initialize_layer(self.avg_pool)
        self.initialize_layer(self.fc)


    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.DCovN(x)
        y = self.avg_pool(y).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        y = torch.exp(y)
        return x * y.expand_as(x)

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.xavier_uniform_(m.weight, gain=1)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def initialize_layer(self, layer):
        if isinstance(layer, (nn.Conv2d, nn.Linear)):
            torch.nn.init.normal_(layer.weight, mean=0., std=0.001)
            if layer.bias is not None:
                torch.nn.init.constant_(layer.bias, 0)

def DcovN(c1, c2, depth, kernel_size=3, patch_size=3):
    dcovn = nn.Sequential(
        nn.Conv2d(c1, c2, kernel_size=patch_size, stride=patch_size),
        nn.SiLU(),
        nn.BatchNorm2d(c2),
        *[nn.Sequential(
            Residual(nn.Sequential(
                nn.Conv2d(in_channels=c2, out_channels=c2, kernel_size=kernel_size, stride=1, padding=1, groups=c2),
                nn.SiLU(),
                nn.BatchNorm2d(c2)
            )),
            nn.Conv2d(in_channels=c2, out_channels=c2, kernel_size=1, stride=1, padding=0, groups=1),
            nn.SiLU(),
            nn.BatchNorm2d(c2)
        ) for i in range(depth)]
    )
    return dcovn

class MultiSEAM(nn.Module):
    def __init__(self, c1, c2, depth, kernel_size=3, patch_size=[3, 5, 7], reduction=16):
        super(MultiSEAM, self).__init__()
        if c1 != c2:
            c2 = c1
        self.DCovN0 = DcovN(c1, c2, depth, kernel_size=kernel_size, patch_size=patch_size[0])
        self.DCovN1 = DcovN(c1, c2, depth, kernel_size=kernel_size, patch_size=patch_size[1])
        self.DCovN2 = DcovN(c1, c2, depth, kernel_size=kernel_size, patch_size=patch_size[2])
        self.avg_pool = torch.nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(c2, c2 // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(c2 // reduction, c2, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y0 = self.DCovN0(x)
        y1 = self.DCovN1(x)
        y2 = self.DCovN2(x)
        y0 = self.avg_pool(y0).view(b, c)
        y1 = self.avg_pool(y1).view(b, c)
        y2 = self.avg_pool(y2).view(b, c)
        y4 = self.avg_pool(x).view(b, c)
        y = (y0 + y1 + y2 + y4) / 4
        y = self.fc(y).view(b, c, 1, 1)
        y = torch.exp(y)
        return x * y.expand_as(x)