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