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model_unet.py
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model_unet.py
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import torch
import torch.nn as nn
class ReconstructiveSubNetwork(nn.Module):
def __init__(self,in_channels=3, out_channels=3, base_width=128):
super(ReconstructiveSubNetwork, self).__init__()
self.encoder = EncoderReconstructive(in_channels, base_width)
self.decoder = DecoderReconstructive(base_width, out_channels=out_channels)
def forward(self, x):
b5 = self.encoder(x)
output = self.decoder(b5)
return output
class DiscriminativeSubNetwork(nn.Module):
def __init__(self,in_channels=3, out_channels=3, base_channels=64, out_features=False):
super(DiscriminativeSubNetwork, self).__init__()
base_width = base_channels
self.encoder_segment = EncoderDiscriminative(in_channels, base_width)
self.decoder_segment = DecoderDiscriminative(base_width, out_channels=out_channels)
#self.segment_act = torch.nn.Sigmoid()
self.out_features = out_features
def forward(self, x):
b1,b2,b3,b4,b5,b6 = self.encoder_segment(x)
output_segment = self.decoder_segment(b1,b2,b3,b4,b5,b6)
if self.out_features:
return output_segment, b2, b3, b4, b5, b6
else:
return output_segment
class EncoderDiscriminative(nn.Module):
def __init__(self, in_channels, base_width):
super(EncoderDiscriminative, self).__init__()
self.block1 = nn.Sequential(
nn.Conv2d(in_channels,base_width, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width),
nn.ReLU(inplace=True),
nn.Conv2d(base_width, base_width, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width),
nn.ReLU(inplace=True))
self.mp1 = nn.Sequential(nn.MaxPool2d(2))
self.block2 = nn.Sequential(
nn.Conv2d(base_width,base_width*2, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*2),
nn.ReLU(inplace=True),
nn.Conv2d(base_width*2, base_width*2, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*2),
nn.ReLU(inplace=True))
self.mp2 = nn.Sequential(nn.MaxPool2d(2))
self.block3 = nn.Sequential(
nn.Conv2d(base_width*2,base_width*4, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*4),
nn.ReLU(inplace=True),
nn.Conv2d(base_width*4, base_width*4, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*4),
nn.ReLU(inplace=True))
self.mp3 = nn.Sequential(nn.MaxPool2d(2))
self.block4 = nn.Sequential(
nn.Conv2d(base_width*4,base_width*8, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*8),
nn.ReLU(inplace=True),
nn.Conv2d(base_width*8, base_width*8, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*8),
nn.ReLU(inplace=True))
self.mp4 = nn.Sequential(nn.MaxPool2d(2))
self.block5 = nn.Sequential(
nn.Conv2d(base_width*8,base_width*8, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*8),
nn.ReLU(inplace=True),
nn.Conv2d(base_width*8, base_width*8, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*8),
nn.ReLU(inplace=True))
self.mp5 = nn.Sequential(nn.MaxPool2d(2))
self.block6 = nn.Sequential(
nn.Conv2d(base_width*8,base_width*8, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*8),
nn.ReLU(inplace=True),
nn.Conv2d(base_width*8, base_width*8, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*8),
nn.ReLU(inplace=True))
def forward(self, x):
b1 = self.block1(x)
mp1 = self.mp1(b1)
b2 = self.block2(mp1)
mp2 = self.mp3(b2)
b3 = self.block3(mp2)
mp3 = self.mp3(b3)
b4 = self.block4(mp3)
mp4 = self.mp4(b4)
b5 = self.block5(mp4)
mp5 = self.mp5(b5)
b6 = self.block6(mp5)
return b1,b2,b3,b4,b5,b6
class DecoderDiscriminative(nn.Module):
def __init__(self, base_width, out_channels=1):
super(DecoderDiscriminative, self).__init__()
self.up_b = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
nn.Conv2d(base_width * 8, base_width * 8, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width * 8),
nn.ReLU(inplace=True))
self.db_b = nn.Sequential(
nn.Conv2d(base_width*(8+8), base_width*8, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*8),
nn.ReLU(inplace=True),
nn.Conv2d(base_width * 8, base_width * 8, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width * 8),
nn.ReLU(inplace=True)
)
self.up1 = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
nn.Conv2d(base_width * 8, base_width * 4, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width * 4),
nn.ReLU(inplace=True))
self.db1 = nn.Sequential(
nn.Conv2d(base_width*(4+8), base_width*4, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*4),
nn.ReLU(inplace=True),
nn.Conv2d(base_width * 4, base_width * 4, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width * 4),
nn.ReLU(inplace=True)
)
self.up2 = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
nn.Conv2d(base_width * 4, base_width * 2, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width * 2),
nn.ReLU(inplace=True))
self.db2 = nn.Sequential(
nn.Conv2d(base_width*(2+4), base_width*2, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*2),
nn.ReLU(inplace=True),
nn.Conv2d(base_width * 2, base_width * 2, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width * 2),
nn.ReLU(inplace=True)
)
self.up3 = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
nn.Conv2d(base_width * 2, base_width, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width),
nn.ReLU(inplace=True))
self.db3 = nn.Sequential(
nn.Conv2d(base_width*(2+1), base_width, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width),
nn.ReLU(inplace=True),
nn.Conv2d(base_width, base_width, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width),
nn.ReLU(inplace=True)
)
self.up4 = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
nn.Conv2d(base_width, base_width, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width),
nn.ReLU(inplace=True))
self.db4 = nn.Sequential(
nn.Conv2d(base_width*2, base_width, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width),
nn.ReLU(inplace=True),
nn.Conv2d(base_width, base_width, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width),
nn.ReLU(inplace=True)
)
self.fin_out = nn.Sequential(nn.Conv2d(base_width, out_channels, kernel_size=3, padding=1))
def forward(self, b1,b2,b3,b4,b5,b6):
up_b = self.up_b(b6)
cat_b = torch.cat((up_b,b5),dim=1)
db_b = self.db_b(cat_b)
up1 = self.up1(db_b)
cat1 = torch.cat((up1,b4),dim=1)
db1 = self.db1(cat1)
up2 = self.up2(db1)
cat2 = torch.cat((up2,b3),dim=1)
db2 = self.db2(cat2)
up3 = self.up3(db2)
cat3 = torch.cat((up3,b2),dim=1)
db3 = self.db3(cat3)
up4 = self.up4(db3)
cat4 = torch.cat((up4,b1),dim=1)
db4 = self.db4(cat4)
out = self.fin_out(db4)
return out
class EncoderReconstructive(nn.Module):
def __init__(self, in_channels, base_width):
super(EncoderReconstructive, self).__init__()
self.block1 = nn.Sequential(
nn.Conv2d(in_channels,base_width, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width),
nn.ReLU(inplace=True),
nn.Conv2d(base_width, base_width, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width),
nn.ReLU(inplace=True))
self.mp1 = nn.Sequential(nn.MaxPool2d(2))
self.block2 = nn.Sequential(
nn.Conv2d(base_width,base_width*2, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*2),
nn.ReLU(inplace=True),
nn.Conv2d(base_width*2, base_width*2, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*2),
nn.ReLU(inplace=True))
self.mp2 = nn.Sequential(nn.MaxPool2d(2))
self.block3 = nn.Sequential(
nn.Conv2d(base_width*2,base_width*4, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*4),
nn.ReLU(inplace=True),
nn.Conv2d(base_width*4, base_width*4, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*4),
nn.ReLU(inplace=True))
self.mp3 = nn.Sequential(nn.MaxPool2d(2))
self.block4 = nn.Sequential(
nn.Conv2d(base_width*4,base_width*8, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*8),
nn.ReLU(inplace=True),
nn.Conv2d(base_width*8, base_width*8, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*8),
nn.ReLU(inplace=True))
self.mp4 = nn.Sequential(nn.MaxPool2d(2))
self.block5 = nn.Sequential(
nn.Conv2d(base_width*8,base_width*8, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*8),
nn.ReLU(inplace=True),
nn.Conv2d(base_width*8, base_width*8, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*8),
nn.ReLU(inplace=True))
def forward(self, x):
b1 = self.block1(x)
mp1 = self.mp1(b1)
b2 = self.block2(mp1)
mp2 = self.mp3(b2)
b3 = self.block3(mp2)
mp3 = self.mp3(b3)
b4 = self.block4(mp3)
mp4 = self.mp4(b4)
b5 = self.block5(mp4)
return b5
class DecoderReconstructive(nn.Module):
def __init__(self, base_width, out_channels=1):
super(DecoderReconstructive, self).__init__()
self.up1 = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
nn.Conv2d(base_width * 8, base_width * 8, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width * 8),
nn.ReLU(inplace=True))
self.db1 = nn.Sequential(
nn.Conv2d(base_width*8, base_width*8, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*8),
nn.ReLU(inplace=True),
nn.Conv2d(base_width * 8, base_width * 4, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width * 4),
nn.ReLU(inplace=True)
)
self.up2 = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
nn.Conv2d(base_width * 4, base_width * 4, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width * 4),
nn.ReLU(inplace=True))
self.db2 = nn.Sequential(
nn.Conv2d(base_width*4, base_width*4, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*4),
nn.ReLU(inplace=True),
nn.Conv2d(base_width * 4, base_width * 2, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width * 2),
nn.ReLU(inplace=True)
)
self.up3 = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
nn.Conv2d(base_width * 2, base_width*2, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*2),
nn.ReLU(inplace=True))
# cat with base*1
self.db3 = nn.Sequential(
nn.Conv2d(base_width*2, base_width*2, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*2),
nn.ReLU(inplace=True),
nn.Conv2d(base_width*2, base_width*1, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width*1),
nn.ReLU(inplace=True)
)
self.up4 = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
nn.Conv2d(base_width, base_width, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width),
nn.ReLU(inplace=True))
self.db4 = nn.Sequential(
nn.Conv2d(base_width*1, base_width, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width),
nn.ReLU(inplace=True),
nn.Conv2d(base_width, base_width, kernel_size=3, padding=1),
nn.BatchNorm2d(base_width),
nn.ReLU(inplace=True)
)
self.fin_out = nn.Sequential(nn.Conv2d(base_width, out_channels, kernel_size=3, padding=1))
#self.fin_out = nn.Conv2d(base_width, out_channels, kernel_size=3, padding=1)
def forward(self, b5):
up1 = self.up1(b5)
db1 = self.db1(up1)
up2 = self.up2(db1)
db2 = self.db2(up2)
up3 = self.up3(db2)
db3 = self.db3(up3)
up4 = self.up4(db3)
db4 = self.db4(up4)
out = self.fin_out(db4)
return out