eca depth wise

components/DeConv_Depthwise_ECA.py

@@ -0,0 +1,48 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: DeConv copy.py
+# Created Date: Tuesday July 20th 2021
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Saturday, 19th February 2022 6:16:08 pm
+# Modified By: Chen Xuanhong
+# Copyright (c) 2021 Shanghai Jiao Tong University
+#############################################################
+from tokenize import group
+from torch import nn
+import math
+
+class DeConv(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2, padding="zero", up_mode = "bilinear"):
+        super().__init__()
+        if up_mode.lower() == "bilinear":
+            self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale)
+        elif up_mode.lower() == "nearest":
+            self.upsampling = nn.UpsamplingNearest2d(scale_factor=upsampl_scale)
+        b = 1
+        gamma = 2
+        k_size = int(abs(math.log(out_channels,2)+b)/gamma)
+        k_size = k_size if k_size % 2 else k_size+1
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.se = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False) 
+        self.sigmoid = nn.Sigmoid()
+        
+        padding_size = int((kernel_size -1)/2)
+        self.conv1x1 = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= 1)
+        self.conv    = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=padding_size, bias=False, groups=out_channels)
+            # nn.init.xavier_uniform_(self.conv.weight)
+    #     self.__weights_init__()
+
+    # def __weights_init__(self):
+    #     nn.init.xavier_uniform_(self.conv.weight)
+
+    def forward(self, input):
+        h   = self.conv1x1(input)
+        h   = self.upsampling(h)
+        y   = self.avg_pool(h)
+        y   = self.se(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
+        y   = self.sigmoid(y)
+        
+        h   = self.conv(h)
+        return h * y.expand_as(h)