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dbpn_v1.py
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dbpn_v1.py
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import os
import torch.nn as nn
import torch.optim as optim
from base_networks import *
from torchvision.transforms import *
class Net(nn.Module):
def __init__(self, num_channels, base_filter, feat, num_stages, scale_factor):
super(Net, self).__init__()
if scale_factor == 2:
kernel = 6
stride = 2
padding = 2
elif scale_factor == 4:
kernel = 8
stride = 4
padding = 2
elif scale_factor == 8:
kernel = 12
stride = 8
padding = 2
#Initial Feature Extraction
self.feat0 = ConvBlock(num_channels, feat, 3, 1, 1, activation='prelu', norm=None)
self.feat1 = ConvBlock(feat, base_filter, 1, 1, 0, activation='prelu', norm=None)
#Back-projection stages
self.up1 = UpBlock(base_filter, kernel, stride, padding)
self.down1 = DownBlock(base_filter, kernel, stride, padding)
self.up2 = UpBlock(base_filter, kernel, stride, padding)
self.down2 = D_DownBlock(base_filter, kernel, stride, padding, 2)
self.up3 = D_UpBlock(base_filter, kernel, stride, padding, 2)
self.down3 = D_DownBlock(base_filter, kernel, stride, padding, 3)
self.up4 = D_UpBlock(base_filter, kernel, stride, padding, 3)
self.down4 = D_DownBlock(base_filter, kernel, stride, padding, 4)
self.up5 = D_UpBlock(base_filter, kernel, stride, padding, 4)
self.down5 = D_DownBlock(base_filter, kernel, stride, padding, 5)
self.up6 = D_UpBlock(base_filter, kernel, stride, padding, 5)
self.down6 = D_DownBlock(base_filter, kernel, stride, padding, 6)
self.up7 = D_UpBlock(base_filter, kernel, stride, padding, 6)
self.down7 = D_DownBlock(base_filter, kernel, stride, padding, 7)
self.up8 = D_UpBlock(base_filter, kernel, stride, padding, 7)
self.down8 = D_DownBlock(base_filter, kernel, stride, padding, 8)
self.up9 = D_UpBlock(base_filter, kernel, stride, padding, 8)
self.down9 = D_DownBlock(base_filter, kernel, stride, padding, 9)
self.up10 = D_UpBlock(base_filter, kernel, stride, padding, 9)
#Reconstruction
self.output_conv = ConvBlock(num_stages*base_filter, num_channels, 3, 1, 1, activation=None, norm=None)
for m in self.modules():
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
torch.nn.init.kaiming_normal_(m.weight)
if m.bias is not None:
m.bias.data.zero_()
elif classname.find('ConvTranspose2d') != -1:
torch.nn.init.kaiming_normal_(m.weight)
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
x = self.feat0(x)
x = self.feat1(x)
h1 = self.up1(x)
l1 = self.down1(h1)
h2 = self.up2(l1)
concat_h = torch.cat((h2, h1),1)
l = self.down2(concat_h)
concat_l = torch.cat((l, l1),1)
h = self.up3(concat_l)
concat_h = torch.cat((h, concat_h),1)
l = self.down3(concat_h)
concat_l = torch.cat((l, concat_l),1)
h = self.up4(concat_l)
concat_h = torch.cat((h, concat_h),1)
l = self.down4(concat_h)
concat_l = torch.cat((l, concat_l),1)
h = self.up5(concat_l)
concat_h = torch.cat((h, concat_h),1)
l = self.down5(concat_h)
concat_l = torch.cat((l, concat_l),1)
h = self.up6(concat_l)
concat_h = torch.cat((h, concat_h),1)
l = self.down6(concat_h)
concat_l = torch.cat((l, concat_l),1)
h = self.up7(concat_l)
concat_h = torch.cat((h, concat_h),1)
l = self.down7(concat_h)
concat_l = torch.cat((l, concat_l),1)
h = self.up8(concat_l)
concat_h = torch.cat((h, concat_h),1)
l = self.down8(concat_h)
concat_l = torch.cat((l, concat_l),1)
h = self.up9(concat_l)
concat_h = torch.cat((h, concat_h),1)
l = self.down9(concat_h)
concat_l = torch.cat((l, concat_l),1)
h = self.up10(concat_l)
concat_h = torch.cat((h, concat_h),1)
x = self.output_conv(concat_h)
return x