Yolov8-pose关键点检测：模型轻量化设计 | 模型压缩率从6842降低到1018，GFLOPs从9.6降低至2.2

class StemBlock(nn.Module):
    def __init__(self, c1, c2, k=3, s=2, p=None, g=1, d=1, act=True):
        super(StemBlock, self).__init__()
        self.stem_1 = Conv(c1, c2, k, s, p, g, d, act)
        self.stem_2a = Conv(c2, c2 // 2, 1, 1, 0)
        self.stem_2b = Conv(c2 // 2, c2, 3, 2, 1)
        self.stem_2p = nn.MaxPool2d(kernel_size=2,stride=2,ceil_mode=True)
        self.stem_3 = Conv(c2 * 2, c2, 1, 1, 0)

    def forward(self, x):
        stem_1_out  = self.stem_1(x)
        stem_2a_out = self.stem_2a(stem_1_out)
        stem_2b_out = self.stem_2b(stem_2a_out)
        stem_2p_out = self.stem_2p(stem_1_out)
        out = self.stem_3(torch.cat((stem_2b_out,stem_2p_out),1))
        return out

class conv_bn_relu_maxpool(nn.Module):
    def __init__(self, c1, c2):  # ch_in, ch_out
        super(conv_bn_relu_maxpool, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(c1, c2, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(c2),
            nn.SiLU(inplace=True),
        )
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)

    def forward(self, x):
        return self.maxpool(self.conv(x))

class DWConvblock(nn.Module):
    "Depthwise conv + Pointwise conv"
    def __init__(self, in_channels, out_channels, k, s):
        super(DWConvblock, self).__init__()
        self.p = k // 2
        self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=k, stride=s, padding=self.p, groups=in_channels, bias=False)
        self.bn1 = nn.BatchNorm2d(in_channels)
        self.act1 = nn.SiLU(inplace=True)
        self.conv2 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.act2 = nn.SiLU(inplace=True)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.act1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.act2(x)
        return x