chenxuanhong
33 lines
1.3 KiB
Python
33 lines
1.3 KiB
Python
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: DeConv copy.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: Wednesday, 16th February 2022 1:42:49 am
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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 audioop import bias
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from tokenize import group
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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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self.conv1x1 = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= 1, bias = False)
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self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=padding_size, groups=in_channels)
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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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h = self.conv1x1(h)
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return h |