YOLOv8独家原创改进：SPPF自研创新 | 可变形大核注意力（D-LKA Attention），大卷积核提升不同特征感受野的注意力机制

class DeformConv_experimental(nn.Module):

    def __init__(self, in_channels, groups, kernel_size=(3,3), padding=1, stride=1, dilation=1, bias=True):
        super(DeformConv_experimental, self).__init__()
        
        self.conv_channel_adjust = nn.Conv2d(in_channels=in_channels, out_channels=2 * kernel_size[0] * kernel_size[1], kernel_size=(1,1))

        self.offset_net = nn.Conv2d(in_channels=2 * kernel_size[0] * kernel_size[1],
                                    out_channels=2 * kernel_size[0] * kernel_size[1],
                                    kernel_size=3,
                                    padding=1,
                                    stride=1,
                                    groups=2 * kernel_size[0] * kernel_size[1],
                                    bias=True)

        self.deform_conv = torchvision.ops.DeformConv2d(in_channels=in_channels,
                                                        out_channels=in_channels,
                                                        kernel_size=kernel_size,
                                                        padding=padding,
                                                        groups=groups,
                                                        stride=stride,
                                                        dilation=dilation,
                                                        bias=False)

    def forward(self, x):
        x_chan = self.conv_channel_adjust(x)
        offsets = self.offset_net(x_chan)
        out = self.deform_conv(x, offsets)
        return out


class deformable_LKA(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.conv0 = DeformConv(dim, kernel_size=(5,5), padding=2, groups=dim)
        self.conv_spatial = DeformConv(dim, kernel_size=(7,7), stride=1, padding=9, groups=dim, dilation=3)
        self.conv1 = nn.Conv2d(dim, dim, 1)


    def forward(self, x):
        u = x.clone()        
        attn = self.conv0(x)
        attn = self.conv_spatial(attn)
        attn = self.conv1(attn)

        return u * attn