fix the GPU0 problem

components/DeConv_Depthwise.py

@@ -0,0 +1,32 @@
+#!/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:  Monday, 14th February 2022 4:54:28 pm
+# Modified By: Chen Xuanhong
+# Copyright (c) 2021 Shanghai Jiao Tong University
+#############################################################
+from tokenize import group
+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)
+        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)
+        h   = self.conv(h)
+        return h