（内含即插即用模块代码） ICCV 2023 RepViT | RepViTBlock最新开源移动端网络架构

本文会直接展示我们能用到的RepViTBlock这一即插即用的模块代码，有兴趣的可以先去看一看论文内容，论文链接：https://arxiv.org/abs/2307.09283

下面我会直接放代码，以及我们的使用方法：

        m.bias.data.copy_(b)
        return m

class Residual(torch.nn.Module):
    def __init__(self, m, drop=0.):
        super().__init__()
        self.m = m
        self.drop = drop

    def forward(self, x):
        if self.training and self.drop > 0:
            return x + self.m(x) * torch.rand(x.size(0), 1, 1, 1,
                                              device=x.device).ge_(self.drop).div(1 - self.drop).detach()
        else:
            return x + self.m(x)

    @torch.no_grad()
    def fuse_self(self):
        if isinstance(self.m, Conv2d_BN):
            m = self.m.fuse_self()
            assert (m.groups == m.in_channels)
            identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)
            identity = torch.nn.functional.pad(identity, [1, 1, 1, 1])
            m.weight += identity.to(m.weight.device)
            return m
        elif isinstance(self.m, torch.nn.Conv2d):
            m = self.m
            assert (m.groups != m.in_channels)
            identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)
            identity = torch.nn.functional.pad(identity, [1, 1, 1, 1])
            m.weight += identity.to(m.weight.device)
            return m
        else:
            return self

class RepVGGDW(torch.nn.Module):
    def __init__(self, ed) -> None:
        super().__init__()
        self.conv = Conv2d_BN(ed, ed, 3, 1, 1, groups=ed)
        self.conv1 = Conv2d_BN(ed, ed, 1, 1, 0, groups=ed)
        self.dim = ed

    def forward(self, x):
        return self.conv(x) + self.conv1(x) + x

    @torch.no_grad()
    def fuse_self(self):
        conv = self.conv.fuse_self()
        conv1 = self.conv1.fuse_self()

        conv_w = conv.weight
        conv_b = conv.bias
        conv1_w = conv1.weight
        conv1_b = conv1.bias

        conv1_w = torch.nn.functional.pad(conv1_w, [1, 1, 1, 1])

        identity = torch.nn.functional.pad(torch.ones(conv1_w.shape[0], conv1_w.shape[1], 1, 1, device=conv1_w.device),
                                           [1, 1, 1, 1])

        final_conv_w = conv_w + conv1_w + identity
        final_conv_b = conv_b + conv1_b

        conv.weight.data.copy_(final_conv_w)
        conv.bias.data.copy_(final_conv_b)
        return conv

class RepViTBlock(nn.Module):
    def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se=False, use_hs=False):
        super(RepViTBlock, self).__init__()
        assert stride in [1, 2]

        self.identity = stride == 1 and inp == oup
        assert (hidden_dim == 2 * inp)

        if stride == 2:
            self.token_mixer = nn.Sequential(
                Conv2d_BN(inp, inp, kernel_size, stride, (kernel_size - 1) // 2, groups=inp),
                SqueezeExcite(inp, 0.25) if use_se else nn.Identity(),
                Conv2d_BN(inp, oup, ks=1, stride=1, pad=0)
            )
            self.channel_mixer = Residual(nn.Sequential(
                # pw
                Conv2d_BN(oup, 2 * oup, 1, 1, 0),
                nn.GELU() if use_hs else nn.GELU(),
                # pw-linear
                Conv2d_BN(2 * oup, oup, 1, 1, 0, bn_weight_init=0),
            ))
        else:
            assert (self.identity)
            self.token_mixer = nn.Sequential(
                RepVGGDW(inp),
                SqueezeExcite(inp, 0.25) if use_se else nn.Identity(),
            )
            self.channel_mixer = Residual(nn.Sequential(
                # pw
                Conv2d_BN(inp, hidden_dim, 1, 1, 0),
                nn.GELU() if use_hs else nn.GELU(),
                # pw-linear
                Conv2d_BN(hidden_dim, oup, 1, 1, 0, bn_weight_init=0),
            ))

    def forward(self, x):
        return self.channel_mixer(self.token_mixer(x))

可以看到RepViTBlock的参数较多，这里需要注意的就是中间层的通道数需要是输入通道的两倍，然后输出通道尽量和输入通道保持一致即可，并且最后的两个参数我都默认设置为FALSE,有需要的自行修改即可，下面放一下我用于测试输入输出的代码：

a = torch.ones(1,10,20,20)
b = RepViTBlock(10,20,10,3,1)
c = b(a)
print(c.size())

其输出特征尺寸未改变