eca depth wise

components/DeConv.py

@@ -0,0 +1,41 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: DeConv.py
+# Created Date: Tuesday July 20th 2021
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Saturday, 19th February 2022 5:35:38 pm
+# Modified By: Chen Xuanhong
+# Copyright (c) 2021 Shanghai Jiao Tong University
+#############################################################
+
+
+from torch import nn
+
+class DeConv(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2, padding="zero"):
+        super().__init__()
+        self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale)
+        padding_size = int((kernel_size -1)/2)
+        if padding.lower() == "reflect":
+            self.conv = nn.Sequential(
+                nn.ReflectionPad2d(padding_size),
+                nn.Conv2d(in_channels = in_channels,
+                            out_channels = out_channels, kernel_size= kernel_size, bias= False))
+            # for layer in self.conv:
+            #     if isinstance(layer,nn.Conv2d):
+            #         nn.init.xavier_uniform_(layer.weight)
+        elif padding.lower() == "zero":
+            self.conv = nn.Conv2d(in_channels = in_channels, padding = 1,
+                            out_channels = out_channels, kernel_size= kernel_size, bias= False)
+            # 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.upsampling(input)
+        h   = self.conv(h)
+        return h