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DHDN_gray.py
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import torch
import torch.nn as nn
class _DCR_block(nn.Module):
def __init__(self, channel_in):
super(_DCR_block, self).__init__()
self.conv_1 = nn.Conv2d(in_channels=channel_in, out_channels=int(channel_in/2.), kernel_size=3, stride=1, padding=1)
self.relu1 = nn.PReLU()
self.conv_2 = nn.Conv2d(in_channels=int(channel_in*3/2.), out_channels=int(channel_in/2.), kernel_size=3, stride=1, padding=1)
self.relu2 = nn.PReLU()
self.conv_3 = nn.Conv2d(in_channels=channel_in*2, out_channels=channel_in, kernel_size=3, stride=1, padding=1)
self.relu3 = nn.PReLU()
def forward(self, x):
residual = x
out = self.relu1(self.conv_1(x))
conc = torch.cat([x, out], 1)
out = self.relu2(self.conv_2(conc))
conc = torch.cat([conc, out], 1)
out = self.relu3(self.conv_3(conc))
out = torch.add(out, residual)
return out
class _down(nn.Module):
def __init__(self, channel_in):
super(_down, self).__init__()
self.relu = nn.PReLU()
self.maxpool = nn.MaxPool2d(2)
self.conv = nn.Conv2d(in_channels=channel_in, out_channels=2*channel_in, kernel_size=1, stride=1, padding=0)
def forward(self, x):
out = self.maxpool(x)
out = self.relu(self.conv(out))
return out
class _up(nn.Module):
def __init__(self, channel_in):
super(_up, self).__init__()
self.relu = nn.PReLU()
self.subpixel = nn.PixelShuffle(2)
self.conv = nn.Conv2d(in_channels=channel_in, out_channels=channel_in, kernel_size=1, stride=1, padding=0)
def forward(self, x):
out = self.relu(self.conv(x))
out = self.subpixel(out)
return out
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv_i = nn.Conv2d(in_channels=1, out_channels=128, kernel_size=1, stride=1, padding=0)
self.relu1 = nn.PReLU()
self.DCR_block11 = self.make_layer(_DCR_block, 128)
self.DCR_block12 = self.make_layer(_DCR_block, 128)
self.down1 = self.make_layer(_down, 128)
self.DCR_block21 = self.make_layer(_DCR_block, 256)
self.DCR_block22 = self.make_layer(_DCR_block, 256)
self.down2 = self.make_layer(_down, 256)
self.DCR_block31 = self.make_layer(_DCR_block, 512)
self.DCR_block32 = self.make_layer(_DCR_block, 512)
self.down3 = self.make_layer(_down, 512)
self.DCR_block41 = self.make_layer(_DCR_block, 1024)
self.DCR_block42 = self.make_layer(_DCR_block, 1024)
self.up3 = self.make_layer(_up, 2048)
self.DCR_block33 = self.make_layer(_DCR_block, 1024)
self.DCR_block34 = self.make_layer(_DCR_block, 1024)
self.up2 = self.make_layer(_up, 1024)
self.DCR_block23 = self.make_layer(_DCR_block, 512)
self.DCR_block24 = self.make_layer(_DCR_block, 512)
self.up1 = self.make_layer(_up, 512)
self.DCR_block13 = self.make_layer(_DCR_block, 256)
self.DCR_block14 = self.make_layer(_DCR_block, 256)
self.conv_f = nn.Conv2d(in_channels=256, out_channels=1, kernel_size=1, stride=1, padding=0)
self.relu2 = nn.PReLU()
def make_layer(self, block, channel_in):
layers = []
layers.append(block(channel_in))
return nn.Sequential(*layers)
def forward(self, x):
residual = x
out = self.relu1(self.conv_i(x))
out = self.DCR_block11(out)
conc1 = self.DCR_block12(out)
out = self.down1(conc1)
out = self.DCR_block21(out)
conc2 = self.DCR_block22(out)
out = self.down2(conc2)
out = self.DCR_block31(out)
conc3 = self.DCR_block32(out)
conc4 = self.down3(conc3)
out = self.DCR_block41(conc4)
out = self.DCR_block42(out)
out = torch.cat([conc4, out], 1)
out = self.up3(out)
out = torch.cat([conc3, out], 1)
out = self.DCR_block33(out)
out = self.DCR_block34(out)
out = self.up2(out)
out = torch.cat([conc2, out], 1)
out = self.DCR_block23(out)
out = self.DCR_block24(out)
out = self.up1(out)
out = torch.cat([conc1, out], 1)
out = self.DCR_block13(out)
out = self.DCR_block14(out)
out = self.relu2(self.conv_f(out))
out = torch.add(residual, out)
return out