ECA-Attention

README.md

@@ -12,6 +12,8 @@
     - [6. CBAM Attention Usage](#6-cbam-attention-usage)
 
     - [7. BAM Attention Usage](#7-bam-attention-usage)
+
+    - [8. ECA Attention Usage](#8-eca-attention-usage)
 
 - [MLP Series](#mlp-series)
 
@@ -41,6 +43,8 @@
 
 - Pytorch implementation of ["BAM: Bottleneck Attention Module---BMCV2018"](https://arxiv.org/pdf/1807.06514.pdf)
 
+- Pytorch implementation of ["ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks---CVPR2020"](https://arxiv.org/pdf/1910.03151.pdf)
+
 ***
 
 
@@ -173,7 +177,7 @@ print(output.shape)
 
 ### 7. BAM Attention Usage
 #### 7.1. Paper
-["BAM: Bottleneck Attention Module---BMCV2018"](https://arxiv.org/pdf/1807.06514.pdf)
+["BAM: Bottleneck Attention Module"](https://arxiv.org/pdf/1807.06514.pdf)
 
 #### 7.2. Overview
 ![](./img/BAM.png)
@@ -191,6 +195,24 @@ print(output.shape)
 ```
 
 
+### 8. ECA Attention Usage
+#### 8.1. Paper
+["ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks"](https://arxiv.org/pdf/1910.03151.pdf)
+
+#### 8.2. Overview
+![](./img/ECA.png)
+
+#### 8.3. Code
+```python
+from attention.ECAAttention import ECAAttention
+import torch
+
+input=torch.randn(50,512,7,7)
+eca = ECAAttention(kernel_size=3)
+output=eca(input)
+print(output.shape)
+
+```
 
 
 

attention/ECAAttention.py

@@ -0,0 +1,50 @@
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import init
+from collections import OrderedDict
+
+
+
+class ECAAttention(nn.Module):
+
+    def __init__(self, kernel_size=3):
+        super().__init__()
+        self.gap=nn.AdaptiveAvgPool2d(1)
+        self.conv=nn.Conv1d(1,1,kernel_size=kernel_size,padding=(kernel_size-1)//2)
+        self.sigmoid=nn.Sigmoid()
+
+    def init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                init.kaiming_normal_(m.weight, mode='fan_out')
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                init.constant_(m.weight, 1)
+                init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                init.normal_(m.weight, std=0.001)
+                if m.bias is not None:
+                    init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        y=self.gap(x) #bs,c,1,1
+        y=y.squeeze(-1).permute(0,2,1) #bs,1,c
+        y=self.conv(y) #bs,1,c
+        y=self.sigmoid(y) #bs,1,c
+        y=y.permute(0,2,1).unsqueeze(-1) #bs,c,1,1
+        return x*y.expand_as(x)
+
+
+
+
+
+
+if __name__ == '__main__':
+    input=torch.randn(50,512,7,7)
+    eca = ECAAttention(kernel_size=3)
+    output=eca(input)
+    print(output.shape)
+
+

main.py

@@ -1,8 +1,7 @@
-from mlp.resmlp import ResMLP
+from attention.ECAAttention import ECAAttention
 import torch
 
-
-input=torch.randn(50,3,14,14)
-resmlp=ResMLP(dim=128,image_size=14,patch_size=7,class_num=1000)
-out=resmlp(input)
-print(out.shape)
+input=torch.randn(50,512,7,7)
+eca = ECAAttention(kernel_size=3)
+output=eca(input)
+print(output.shape)