init

components/DeConv.py

@@ -0,0 +1,20 @@
+import torch
+from torch import nn
+
+class DeConv(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2):
+        super().__init__()
+        self.upsampling = nn.UpsamplingNearest2d(scale_factor=upsampl_scale)
+        padding_size = int((kernel_size -1)/2)
+        # self.same_padding   = nn.ReflectionPad2d(padding_size)
+        self.conv           = nn.Conv2d(in_channels = in_channels ,padding=padding_size, out_channels = out_channels , kernel_size= kernel_size, bias= False)
+        self.__weights_init__()
+
+    def __weights_init__(self):
+        nn.init.xavier_uniform_(self.conv.weight)
+
+    def forward(self, input):
+        h   = self.upsampling(input)
+        # h   = self.same_padding(h)
+        h   = self.conv(h)
+        return h