chenxuanhong
48 lines
2.0 KiB
Python
48 lines
2.0 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, 19th February 2022 6:16:08 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 tokenize import group
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from torch import nn
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import math
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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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b = 1
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gamma = 2
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k_size = int(abs(math.log(out_channels,2)+b)/gamma)
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k_size = k_size if k_size % 2 else k_size+1
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.se = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
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self.sigmoid = nn.Sigmoid()
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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.conv = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=padding_size, bias=False, groups=out_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.conv1x1(input)
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h = self.upsampling(h)
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y = self.avg_pool(h)
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y = self.se(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
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y = self.sigmoid(y)
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h = self.conv(h)
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return h * y.expand_as(h) |