chenxuanhong
35 lines
1.5 KiB
Python
35 lines
1.5 KiB
Python
#!/usr/bin/env python3
|
|
# -*- coding:utf-8 -*-
|
|
#############################################################
|
|
# File: DeConv copy.py
|
|
# Created Date: Tuesday July 20th 2021
|
|
# Author: Chen Xuanhong
|
|
# Email: chenxuanhongzju@outlook.com
|
|
# Last Modified: Thursday, 17th February 2022 10:20:46 am
|
|
# Modified By: Chen Xuanhong
|
|
# Copyright (c) 2021 Shanghai Jiao Tong University
|
|
#############################################################
|
|
from tokenize import group
|
|
from torch import nn
|
|
|
|
class DeConv(nn.Module):
|
|
def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2, padding="zero", up_mode = "bilinear"):
|
|
super().__init__()
|
|
if up_mode.lower() == "bilinear":
|
|
self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale)
|
|
elif up_mode.lower() == "nearest":
|
|
self.upsampling = nn.UpsamplingNearest2d(scale_factor=upsampl_scale)
|
|
padding_size = int((kernel_size -1)/2)
|
|
self.conv1x1 = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= 1)
|
|
self.conv = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=padding_size, bias=False, groups=out_channels)
|
|
# nn.init.xavier_uniform_(self.conv.weight)
|
|
# self.__weights_init__()
|
|
|
|
# def __weights_init__(self):
|
|
# nn.init.xavier_uniform_(self.conv.weight)
|
|
|
|
def forward(self, input):
|
|
h = self.conv1x1(input)
|
|
h = self.upsampling(h)
|
|
h = self.conv(h)
|
|
return h |