parnet

README.md

@@ -112,6 +112,9 @@ Hello，大家好，我是小马🚀🚀🚀
     - [27. Coordinate Attention Usage](#27-Coordinate-Attention-Usage)
 
     - [28. MobileViT Attention Usage](#28-MobileViT-Attention-Usage)
+
+    - [29. ParNet Attention Usage](#29-ParNet-Attention-Usage)
+
 
 
 - [Backbone Series](#Backbone-series)
@@ -227,7 +230,9 @@ Hello，大家好，我是小马🚀🚀🚀
 
 - Pytorch implementation of [Coordinate Attention for Efficient Mobile Network Design ---CVPR 2021](https://arxiv.org/abs/2103.02907)
 
-- Pytorch implementation of [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer---ArXiv 2020.10.05](https://arxiv.org/abs/2103.02907)
+- Pytorch implementation of [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer---ArXiv 2021.10.05](https://arxiv.org/abs/2103.02907)
+
+- Pytorch implementation of [Non-deep Networks---ArXiv 2021.10.20](https://arxiv.org/abs/2110.07641)
 
 ***
 
@@ -948,7 +953,7 @@ print(output.shape)
 
 #### 28.1. Paper
 
-[MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer---ArXiv 2020.10.05](https://arxiv.org/abs/2103.02907)
+[MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer---ArXiv 2021.10.05](https://arxiv.org/abs/2103.02907)
 
 
 #### 28.2. Overview
@@ -974,6 +979,36 @@ if __name__ == '__main__':
 ***
 
 
+### 29. ParNet Attention Usage
+
+#### 29.1. Paper
+
+[Non-deep Networks---ArXiv 2021.10.20](https://arxiv.org/abs/2110.07641)
+
+
+#### 29.2. Overview
+
+![](./model/img/ParNet.png.png)
+
+#### 29.3. Usage Code
+
+```python
+from model.attention.ParNetAttention import *
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+if __name__ == '__main__':
+    input=torch.randn(50,512,7,7)
+    pna = ParNetAttention(channel=512)
+    output=pna(input)
+    print(output.shape) #50,512,7,7
+
+```
+
+***
+
+
 
 # Backbone Series
 

main.py

@@ -1,10 +1,10 @@
-from model.conv.CondConv import *
+from model.attention.ParNetAttention import *
 import torch
 from torch import nn
 from torch.nn import functional as F
 
 if __name__ == '__main__':
-    input=torch.randn(2,32,64,64)
-    m=CondConv(in_planes=32,out_planes=64,kernel_size=3,stride=1,padding=1,bias=False)
-    out=m(input)
-    print(out.shape)
+    input=torch.randn(50,512,7,7)
+    pna = ParNetAttention(channel=512)
+    output=pna(input)
+    print(output.shape) #50,512,7,7

model/attention/ParNetAttention.py

@@ -0,0 +1,44 @@
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import init
+
+
+
+class ParNetAttention(nn.Module):
+
+    def __init__(self, channel=512):
+        super().__init__()
+        self.sse = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(channel,channel,kernel_size=1),
+            nn.Sigmoid()
+        )
+
+        self.conv1x1=nn.Sequential(
+            nn.Conv2d(channel,channel,kernel_size=1),
+            nn.BatchNorm2d(channel)
+        )
+        self.conv3x3=nn.Sequential(
+            nn.Conv2d(channel,channel,kernel_size=3,padding=1),
+            nn.BatchNorm2d(channel)
+        )
+        self.silu=nn.SiLU()
+
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        x1=self.conv1x1(x)
+        x2=self.conv3x3(x)
+        x3=self.sse(x)*x
+        y=self.silu(x1+x2+x3)
+        return y
+
+
+if __name__ == '__main__':
+    input=torch.randn(50,512,7,7)
+    pna = ParNetAttention(channel=512)
+    output=pna(input)
+    print(output.shape)
+
+