chenxuanhong
49 lines
2.1 KiB
Python
49 lines
2.1 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: Saturday, 26th February 2022 4:07:55 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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from components.misc.Involution_BN import involution
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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", up_mode = "bilinear"):
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super().__init__()
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if up_mode.lower() == "bilinear":
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self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale)
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elif up_mode.lower() == "nearest":
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self.upsampling = nn.UpsamplingNearest2d(scale_factor=upsampl_scale)
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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)
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# self.same_padding = nn.ReflectionPad2d(padding_size)
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if padding.lower() == "reflect":
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self.conv = involution(out_channels,kernel_size,1)
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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, 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 = involution(out_channels,kernel_size,1)
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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.conv1x1(input)
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h = self.upsampling(h)
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h = self.conv(h)
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return h |