sMLP

README.md

@@ -139,6 +139,7 @@ $ pip install fightingcv
 
     - [4. gMLP Usage](#4-gMLP-Usage)
 
+    - [5. sMLP Usage](#5-sMLP-Usage)
 
 - [Re-Parameter(ReP) Series](#Re-Parameter-series)
 
@@ -984,6 +985,9 @@ if __name__ == '__main__':
 
 - Pytorch implementation of ["Pay Attention to MLPs---arXiv 2021.05.17"](https://arxiv.org/abs/2105.08050)
 
+
+- Pytorch implementation of ["Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?---arXiv 2021.09.12"](https://arxiv.org/abs/2109.05422)
+
 ### 1. RepMLP Usage
 #### 1.1. Paper
 ["RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition"](https://arxiv.org/pdf/2105.01883v1.pdf)
@@ -1087,6 +1091,28 @@ output=gmlp(input)
 print(output.shape)
 ```
 
+***
+
+### 5. sMLP Usage
+#### 5.1. Paper
+["Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?"](https://arxiv.org/abs/2109.05422)
+
+#### 5.2. Overview
+![](./fightingcv/img/sMLP.jpg)
+
+#### 5.3. Code
+```python
+from fightingcv.mlp.sMLP_block import sMLPBlock
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+if __name__ == '__main__':
+    input=torch.randn(50,3,224,224)
+    smlp=sMLPBlock(h=224,w=224)
+    out=smlp(input)
+    print(out.shape)
+```
 
 
 # Re-Parameter Series

fightingcv/mlp/sMLP_block.py

@@ -0,0 +1,30 @@
+import torch
+from torch import nn
+
+
+
+
+
+
+
+class sMLPBlock(nn.Module):
+    def __init__(self,h=224,w=224,c=3):
+        super().__init__()
+        self.proj_h=nn.Linear(h,h)
+        self.proj_w=nn.Linear(w,w)
+        self.fuse=nn.Linear(3*c,c)
+
+    def forward(self,x):
+        x_h=self.proj_h(x.permute(0,1,3,2)).permute(0,1,3,2)
+        x_w=self.proj_w(x)
+        x_id=x
+        x_fuse=torch.cat([x_h,x_w,x_id],dim=1)
+        out=self.fuse(x_fuse.permute(0,2,3,1)).permute(0,3,1,2)
+        return out
+
+
+if __name__ == '__main__':
+    input=torch.randn(50,3,224,224)
+    smlp=sMLPBlock(h=224,w=224)
+    out=smlp(input)
+    print(out.shape)

main.py

@@ -1,23 +1,15 @@
-# from attention.gfnet import GFNet
-# import torch
-# from torch import nn
-# from torch.nn import functional as F
+from fightingcv.mlp.sMLP_block import sMLPBlock
+import torch
+from torch import nn
+from torch.nn import functional as F
 
-# x = torch.randn(1, 3, 224, 224)
-# gfnet = GFNet(embed_dim=384, img_size=224, patch_size=16, num_classes=1000)
-# out = gfnet(x)
-# print(out.shape)
+if __name__ == '__main__':
+    input=torch.randn(50,3,224,224)
+    smlp=sMLPBlock(h=224,w=224)
+    out=smlp(input)
+    print(out.shape)
 
 
-# from backbone_cnn.resnext import ResNeXt50,ResNeXt101,ResNeXt152
-# import torch
 
-# if __name__ == '__main__':
-#     input=torch.randn(50,3,224,224)
-#     resnext50=ResNeXt50(1000)
-#     # resnext101=ResNeXt101(1000)
-#     # resnext152=ResNeXt152(1000)
-#     out=resnext50(input)
-#     print(out.shape)