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gray_model.py
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import torch
import torch.nn as nn
import math
class _Residual_Block(nn.Module):
def __init__(self):
super(_Residual_Block, self).__init__()
#res1
self.conv1 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.relu2 = nn.PReLU()
self.conv3 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.relu4 = nn.PReLU()
#res1
#concat1
self.conv5 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2, padding=1, bias=False)
self.relu6 = nn.PReLU()
#res2
self.conv7 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1, bias=False)
self.relu8 = nn.PReLU()
#res2
#concat2
self.conv9 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, stride=2, padding=1, bias=False)
self.relu10 = nn.PReLU()
#res3
self.conv11 = nn.Conv2d(in_channels=1024, out_channels=1024, kernel_size=3, stride=1, padding=1, bias=False)
self.relu12 = nn.PReLU()
#res3
self.conv13 = nn.Conv2d(in_channels=1024, out_channels=2048, kernel_size=1, stride=1, padding=0, bias=False)
self.up14 = nn.PixelShuffle(2)
#concat2
self.conv15 = nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=1, stride=1, padding=0, bias=False)
#res4
self.conv16 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1, bias=False)
self.relu17 = nn.PReLU()
#res4
self.conv18 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=1, stride=1, padding=0, bias=False)
self.up19 = nn.PixelShuffle(2)
#concat1
self.conv20 = nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1, stride=1, padding=0, bias=False)
#res5
self.conv21 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.relu22 = nn.PReLU()
self.conv23 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.relu24 = nn.PReLU()
#res5
self.conv25 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
def forward(self, x):
res1 = x
out = self.relu4(self.conv3(self.relu2(self.conv1(x))))
out = torch.add(res1, out)
cat1 = out
out = self.relu6(self.conv5(out))
res2 = out
out = self.relu8(self.conv7(out))
out = torch.add(res2, out)
cat2 = out
out = self.relu10(self.conv9(out))
res3 = out
out = self.relu12(self.conv11(out))
out = torch.add(res3, out)
out = self.up14(self.conv13(out))
out = torch.cat([out, cat2], 1)
out = self.conv15(out)
res4 = out
out = self.relu17(self.conv16(out))
out = torch.add(res4, out)
out = self.up19(self.conv18(out))
out = torch.cat([out, cat1], 1)
out = self.conv20(out)
res5 = out
out = self.relu24(self.conv23(self.relu22(self.conv21(out))))
out = torch.add(res5, out)
out = self.conv25(out)
out = torch.add(out, res1)
return out
class Recon_Block(nn.Module):
def __init__(self):
super(Recon_Block, self).__init__()
self.conv1 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.relu2 = nn.PReLU()
self.conv3 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.relu4 = nn.PReLU()
self.conv5 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.relu6= nn.PReLU()
self.conv7 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.relu8 = nn.PReLU()
self.conv9 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.relu10 = nn.PReLU()
self.conv11 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.relu12 = nn.PReLU()
self.conv13 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.relu14 = nn.PReLU()
self.conv15 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.relu16 = nn.PReLU()
self.conv17 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
def forward(self, x):
res1 = x
output = self.relu4(self.conv3(self.relu2(self.conv1(x))))
output = torch.add(output, res1)
res2 = output
output = self.relu8(self.conv7(self.relu6(self.conv5(output))))
output = torch.add(output, res2)
res3 = output
output = self.relu12(self.conv11(self.relu10(self.conv9(output))))
output = torch.add(output, res3)
res4 = output
output = self.relu16(self.conv15(self.relu14(self.conv13(output))))
output = torch.add(output, res4)
output = self.conv17(output)
output = torch.add(output, res1)
return output
class _NetG(nn.Module):
def __init__(self):
super(_NetG, self).__init__()
self.conv_input = nn.Conv2d(in_channels=1, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.relu1 = nn.PReLU()
self.conv_down = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=2, padding=1, bias=False)
self.relu2 = nn.PReLU()
self.recursive_A = _Residual_Block()
self.recursive_B = _Residual_Block()
self.recursive_C = _Residual_Block()
self.recursive_D = _Residual_Block()
self.recursive_E = _Residual_Block()
self.recursive_F = _Residual_Block()
self.recon = Recon_Block()
#concat
self.conv_mid = nn.Conv2d(in_channels=1536, out_channels=256, kernel_size=1, stride=1, padding=0, bias=False)
self.relu3 = nn.PReLU()
self.conv_mid2 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.relu4 = nn.PReLU()
self.subpixel = nn.PixelShuffle(2)
self.conv_output = nn.Conv2d(in_channels=64, out_channels=1, kernel_size=3, stride=1, padding=1, bias=False)
def forward(self, x):
residual = x
out = self.relu1(self.conv_input(x))
out = self.relu2(self.conv_down(out))
out1 = self.recursive_A(out)
out2 = self.recursive_B(out1)
out3 = self.recursive_C(out2)
out4 = self.recursive_D(out3)
out5 = self.recursive_E(out4)
out6 = self.recursive_F(out5)
recon1 = self.recon(out1)
recon2 = self.recon(out2)
recon3 = self.recon(out3)
recon4 = self.recon(out4)
recon5 = self.recon(out5)
recon6 = self.recon(out6)
out = torch.cat([recon1, recon2, recon3, recon4, recon5, recon6], 1)
out = self.relu3(self.conv_mid(out))
residual2 = out
out = self.relu4(self.conv_mid2(out))
out = torch.add(out, residual2)
out= self.subpixel(out)
out = self.conv_output(out)
out = torch.add(out, residual)
return out