GDFN模块（restormer）

为了对特征进行变换，常规的前馈神经网络独立地在每个像素位置进行相同的操作。它使用两个1x1卷积层，一个用来扩展特征通道（通常4倍），第二个用来将特征通道减少到原来的输入维度。在隐藏层中加入非线性。

GDFN做了两个基本的改进：

门机制
深度可分离卷积

架构如下图：

门机制通过经过线性变换的两个平行通道的逐元素点积实现，其中一个通道用GELU激活，可以参考Gaussian Error Linear Units (GELUs)

深度可分离卷积用来编码空间上邻域像素的信息，有助于学习局部图像结构。

Given an input tensor $\mathbf{X} \in \mathbb{R}^{\hat{H} \times \hat{W} \times \hat{C}}$ , GDFN is formulated as:
$\begin{aligned} \hat{\mathbf{X}} & =W_p^0 \text { Gating }(\mathbf{X})+\mathbf{X} \\ \operatorname{Gating}(\mathbf{X}) & =\phi\left(W_d^1 W_p^1(\operatorname{LN}(\mathbf{X}))\right) \odot W_d^2 W_p^2(\operatorname{LN}(\mathbf{X})) \end{aligned}$ where $\odot$ denotes element-wise multiplication, $\phi$ represents the GELU non-linearity, and LN is the layer normalization

实现代码

## Gated-Dconv Feed-Forward Network (GDFN)
class FeedForward(nn.Module):
    def __init__(self, dim, ffn_expansion_factor, bias):
        super(FeedForward, self).__init__()

        hidden_features = int(dim*ffn_expansion_factor)

        self.project_in = nn.Conv2d(dim, hidden_features*2, kernel_size=1, bias=bias)

        self.dwconv = nn.Conv2d(hidden_features*2, hidden_features*2,\
         kernel_size=3, stride=1, padding=1, groups=hidden_features*2, bias=bias)

        self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias)

    def forward(self, x):
        x = self.project_in(x)
        x1, x2 = self.dwconv(x).chunk(2, dim=1)
        x = F.gelu(x1) * x2
        x = self.project_out(x)
        return x