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  • 整体思路以及计算方式
  • 时间复杂度
  • 训练以及loss
  • 代码
  • 简评
  1. FFN

S2-MLPv2 Improved Spatial-Shift MLP Architecture for Vision

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Last updated 2 years ago

论文地址：

整体思路以及计算方式

对S2-MLP的改进，使用了多种spatial-shift，然后split attention进行特征融合：

这里主要介绍split attention的计算方式：

  • 输入：[X1,X2,⋯ ,XK]∈Rn×K×c,Xk∈Rn×c\left[\mathbf{X}_{1}, \mathbf{X}_{2}, \cdots, \mathbf{X}_{K}\right]\in \mathbb R^{n\times K\times c} , \mathbf X_k\in \mathbb R^{n\times c}[X1​,X2​,⋯,XK​]∈Rn×K×c,Xk​∈Rn×c

  • 特征融合：a=∑k=1K1nXk∈Rc\mathbf a= \sum_{k=1}^K 1_n \mathbf X_k \in \mathbb R^ca=∑k=1K​1n​Xk​∈Rc

  • a1=σ(aW1)∈Rc1\mathbf a_1 = \sigma(\mathbf a \mathbf W_1) \in \mathbb R^{c_1}a1​=σ(aW1​)∈Rc1​

  • a2=a1W2∈RKc\mathbf a_2 = \mathbf a_1 \mathbf W_2 \in \mathbb R^{K c}a2​=a1​W2​∈RKc

  • a3=Softmax(reshape(a2),dim=1)∈R1×K×c\mathbf a_3 =\mathrm{Softmax}(\mathrm{reshape}(\mathbf a_2),\mathrm{dim}=1)\in \mathbb R^{1\times K\times c}a3​=Softmax(reshape(a2​),dim=1)∈R1×K×c

  • o1=[X1,X2,⋯ ,XK]⊙a3∈Rn×K×c\mathbf o_1=\left[\mathbf {X}_{1}, \mathbf {X}_{2}, \cdots, \mathbf {X}_{K}\right]\odot \mathbf a_3 \in \mathbb R^{n\times K \times c }o1​=[X1​,X2​,⋯,XK​]⊙a3​∈Rn×K×c

  • o2=Sum(o1,dim=1)∈Rn×c\mathbf o_2 = \mathrm{Sum}(\mathbf o_1, \mathrm{dim}=1)\in \mathbb R^{n\times c}o2​=Sum(o1​,dim=1)∈Rn×c

该模块主要融合了Xk\mathbf X_kXk​的特征，不知道是否可以代替Attention的效果？

时间复杂度

split attention模块的时间复杂度为O(nKc+cc1+Kc1c)O(nKc + cc_1 + Kc_1c )O(nKc+cc1​+Kc1​c)，其余部分任然为线性复杂度。

训练以及loss

不变。

代码

简评

spatial-shift + split attention可以大幅提升性能，可以研究下，然后在nlp中使用。

https://github.com/liuruiyang98/Jittor-MLP/blob/main/models_pytorch/s2_mlp_v2.py
https://arxiv.org/abs/2108.01072