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