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UNet.py
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import torch
import torch.nn as nn
class DoubleConv2d(nn.Module):
def __init__(self,in_channels,features):
super(DoubleConv2d, self).__init__()
self.block = nn.Sequential(
nn.Conv2d(in_channels, features, kernel_size=3, padding=1),
#nn.BatchNorm2d(features),
nn.GroupNorm(num_groups=int(features/16), num_channels=features),
nn.ReLU(inplace=True),
nn.Conv2d(features, features, kernel_size=3, padding=1),
#nn.BatchNorm2d(features),
nn.GroupNorm(num_groups=int(features/16), num_channels=features),
nn.ReLU(inplace=True)
)
def forward(self,x):
return self.block(x)
class UNet(nn.Module):
#original UNet
def __init__(self,in_channels=1,out_channels=1,init_features=64):
super(UNet, self).__init__()
features = init_features
self.encoder1 = DoubleConv2d(in_channels,features)
self.pool1 = nn.MaxPool2d(2, 2)
self.encoder2 = DoubleConv2d(features,2*features)
self.pool2 = nn.MaxPool2d(2, 2)
self.encoder3 = DoubleConv2d(2*features,4*features)
self.pool3 = nn.MaxPool2d(2, 2)
self.encoder4 = DoubleConv2d(4*features,8*features)
self.pool4 = nn.MaxPool2d(2, 2)
self.bottleneck = DoubleConv2d(8*features,16*features)
self.upconv4 = nn.ConvTranspose2d(16*features,8*features,kernel_size=2,stride=2)
self.decoder4 = DoubleConv2d(16*features,8*features) #concate, 2*8=16
self.upconv3 = nn.ConvTranspose2d(8*features,4*features,kernel_size=2,stride=2)
self.decoder3 = DoubleConv2d(8*features,4*features) #concate, 2*4=8
self.upconv2 = nn.ConvTranspose2d(4*features,2*features,kernel_size=2,stride=2)
self.decoder2 = DoubleConv2d(4*features,2*features) #concate, 2*2=4
self.upconv1 = nn.ConvTranspose2d(2*features,features,kernel_size=2,stride=2)
self.decoder1 = DoubleConv2d(2*features,features) #concate, 2*1=2
self.conv_out = nn.Conv2d(features, 1, 1)
def forward(self, input):
enc1 = self.encoder1(input)
enc2 = self.encoder2(self.pool1(enc1))
enc3 = self.encoder3(self.pool2(enc2))
enc4 = self.encoder4(self.pool3(enc3))
bottleneck = self.bottleneck(self.pool4(enc4))
dec4 = torch.cat([enc4,self.upconv4(bottleneck)],dim=1)
dec4 = self.decoder4(dec4)
dec3 = torch.cat([enc3,self.upconv3(dec4)],dim=1)
dec3 = self.decoder3(dec3)
dec2 = torch.cat([enc2,self.upconv2(dec3)],dim=1)
dec2 = self.decoder2(dec2)
dec1 = torch.cat([enc1,self.upconv1(dec2)],dim=1)
dec1 = self.decoder1(dec1)
output = torch.sigmoid(self.conv_out(dec1))
return output
def get_bottleneck(self,input):
enc1 = self.encoder1(input)
enc2 = self.encoder2(self.pool1(enc1))
enc3 = self.encoder3(self.pool2(enc2))
enc4 = self.encoder4(self.pool3(enc3))
bottleneck = self.bottleneck(self.pool4(enc4))
return bottleneck
class UNet2(nn.Module):
#UNet with 1*5+5*1 conv kernel
def __init__(self,in_channels=1,out_channels=1,init_features=64):
super(UNet2, self).__init__()
features = init_features
self.encoder1 = UNet2.DoubleConv2d(in_channels,features)
self.pool1 = nn.MaxPool2d(2, 2)
self.encoder2 = UNet2.DoubleConv2d(features,2*features)
self.pool2 = nn.MaxPool2d(2, 2)
self.encoder3 = UNet2.DoubleConv2d(2*features,4*features)
self.pool3 = nn.MaxPool2d(2, 2)
self.encoder4 = UNet2.DoubleConv2d(4*features,8*features)
self.pool4 = nn.MaxPool2d(2, 2)
self.bottleneck = UNet2.DoubleConv2d(8*features,16*features)
self.upconv4 = nn.ConvTranspose2d(16*features,8*features,kernel_size=2,stride=2)
self.decoder4 = UNet2.DoubleConv2d(16*features,8*features) #concate, 2*8=16
self.upconv3 = nn.ConvTranspose2d(8*features,4*features,kernel_size=2,stride=2)
self.decoder3 = UNet2.DoubleConv2d(8*features,4*features) #concate, 2*4=8
self.upconv2 = nn.ConvTranspose2d(4*features,2*features,kernel_size=2,stride=2)
self.decoder2 = UNet2.DoubleConv2d(4*features,2*features) #concate, 2*2=4
self.upconv1 = nn.ConvTranspose2d(2*features,features,kernel_size=2,stride=2)
self.decoder1 = UNet2.DoubleConv2d(2*features,features) #concate, 2*1=2
self.conv_out = nn.Conv2d(features, 1, 1)
def forward(self, input):
enc1 = self.encoder1(input)
enc2 = self.encoder2(self.pool1(enc1))
enc3 = self.encoder3(self.pool2(enc2))
enc4 = self.encoder4(self.pool3(enc3))
bottleneck = self.bottleneck(self.pool4(enc4))
dec4 = torch.cat([enc4,self.upconv4(bottleneck)],dim=1)
dec4 = self.decoder4(dec4)
dec3 = torch.cat([enc3,self.upconv3(dec4)],dim=1)
dec3 = self.decoder3(dec3)
dec2 = torch.cat([enc2,self.upconv2(dec3)],dim=1)
dec2 = self.decoder2(dec2)
dec1 = torch.cat([enc1,self.upconv1(dec2)],dim=1)
dec1 = self.decoder1(dec1)
output = torch.sigmoid(self.conv_out(dec1))
return output
def DoubleConv2d(in_channels,features):
return nn.Sequential(
nn.Conv2d(in_channels, features, kernel_size=[1,5], padding=[0,2]),
nn.BatchNorm2d(features),
nn.ReLU(inplace=True),
nn.Conv2d(features, features, kernel_size=[5,1], padding=[2,0]),
nn.BatchNorm2d(features),
nn.ReLU(inplace=True)
)
class UNet3(nn.Module):
#fc for attention
def __init__(self,in_channels=1,out_channels=1,init_features=64,input_size=256):
super(UNet3, self).__init__()
features = init_features
self.encoder1 = DoubleConv2d(in_channels,features)
self.pool1 = nn.MaxPool2d(2, 2)
self.encoder2 = DoubleConv2d(features,2*features)
self.pool2 = nn.MaxPool2d(2, 2)
self.encoder3 = DoubleConv2d(2*features,4*features)
self.pool3 = nn.MaxPool2d(2, 2)
self.encoder4 = DoubleConv2d(4*features,8*features)
self.pool4 = nn.MaxPool2d(2, 2)
self.bottleneck = DoubleConv2d(8*features,16*features)
self.fc = nn.Sequential(nn.Conv2d(16*features, 1, kernel_size=1),
nn.ReLU(inplace=True),
nn.Flatten(),
nn.Linear(int((input_size/16)**2),64),
nn.ReLU(inplace=True),
nn.Linear(64,64),
nn.ReLU(inplace=True),
nn.Linear(64,int((input_size/16)**2)),
nn.ReLU(inplace=True)
)
self.upconv4 = nn.ConvTranspose2d(16*features+1,8*features,kernel_size=2,stride=2)
self.decoder4 = DoubleConv2d(16*features,8*features) #concate, 2*8=16
self.upconv3 = nn.ConvTranspose2d(8*features,4*features,kernel_size=2,stride=2)
self.decoder3 = DoubleConv2d(8*features,4*features) #concate, 2*4=8
self.upconv2 = nn.ConvTranspose2d(4*features,2*features,kernel_size=2,stride=2)
self.decoder2 = DoubleConv2d(4*features,2*features) #concate, 2*2=4
self.upconv1 = nn.ConvTranspose2d(2*features,features,kernel_size=2,stride=2)
self.decoder1 = DoubleConv2d(2*features,features) #concate, 2*1=2
self.conv_out = nn.Conv2d(features, 1, 1)
def forward(self, input):
enc1 = self.encoder1(input)
enc2 = self.encoder2(self.pool1(enc1))
enc3 = self.encoder3(self.pool2(enc2))
enc4 = self.encoder4(self.pool3(enc3))
bottleneck = self.bottleneck(self.pool4(enc4))
ext = self.fc(bottleneck).view(-1,1,bottleneck.shape[2],bottleneck.shape[3])
bottleneck = torch.cat([ext,bottleneck],dim=1)
dec4 = torch.cat([enc4,self.upconv4(bottleneck)],dim=1)
dec4 = self.decoder4(dec4)
dec3 = torch.cat([enc3,self.upconv3(dec4)],dim=1)
dec3 = self.decoder3(dec3)
dec2 = torch.cat([enc2,self.upconv2(dec3)],dim=1)
dec2 = self.decoder2(dec2)
dec1 = torch.cat([enc1,self.upconv1(dec2)],dim=1)
dec1 = self.decoder1(dec1)
output = torch.sigmoid(self.conv_out(dec1))
return output
class UNet_OF(nn.Module):
#simplest optical attention
def __init__(self,in_channels=1,out_channels=1,init_features=64,input_size=256):
super(UNet_OF, self).__init__()
features = init_features
self.encoder_optfl = nn.Sequential(DoubleConv2d(in_channels,32),
nn.Conv2d(32,1,kernel_size=1),
nn.Sigmoid()
)
self.encoder1 = DoubleConv2d(in_channels,features)
self.pool1 = nn.MaxPool2d(2, 2)
self.encoder2 = DoubleConv2d(features,2*features)
self.pool2 = nn.MaxPool2d(2, 2)
self.encoder3 = DoubleConv2d(2*features,4*features)
self.pool3 = nn.MaxPool2d(2, 2)
self.encoder4 = DoubleConv2d(4*features,8*features)
self.pool4 = nn.MaxPool2d(2, 2)
self.bottleneck = DoubleConv2d(8*features,16*features)
self.upconv4 = nn.ConvTranspose2d(16*features,8*features,kernel_size=2,stride=2)
self.decoder4 = DoubleConv2d(16*features,8*features) #concate, 2*8=16
self.upconv3 = nn.ConvTranspose2d(8*features,4*features,kernel_size=2,stride=2)
self.decoder3 = DoubleConv2d(8*features,4*features) #concate, 2*4=8
self.upconv2 = nn.ConvTranspose2d(4*features,2*features,kernel_size=2,stride=2)
self.decoder2 = DoubleConv2d(4*features,2*features) #concate, 2*2=4
self.upconv1 = nn.ConvTranspose2d(2*features,features,kernel_size=2,stride=2)
self.decoder1 = DoubleConv2d(2*features,features) #concate, 2*1=2
self.conv_out = nn.Conv2d(features, 1, 1)
def forward(self, input, mask, mode):
if mode:
# mode==1: with optical flow attention
tmp = self.encoder1(input)
enc1 = tmp*self.encoder_optfl(mask)+tmp
else:
# mode==0: normal UNet FP
enc1 = self.encoder1(input)
enc2 = self.encoder2(self.pool1(enc1))
enc3 = self.encoder3(self.pool2(enc2))
enc4 = self.encoder4(self.pool3(enc3))
bottleneck = self.bottleneck(self.pool4(enc4))
dec4 = torch.cat([enc4,self.upconv4(bottleneck)],dim=1)
dec4 = self.decoder4(dec4)
dec3 = torch.cat([enc3,self.upconv3(dec4)],dim=1)
dec3 = self.decoder3(dec3)
dec2 = torch.cat([enc2,self.upconv2(dec3)],dim=1)
dec2 = self.decoder2(dec2)
dec1 = torch.cat([enc1,self.upconv1(dec2)],dim=1)
dec1 = self.decoder1(dec1)
output = torch.sigmoid(self.conv_out(dec1))
return output
class AttentionBlock(nn.Module):
def __init__(self,in_channels,mask_channels=16):
super(AttentionBlock, self).__init__()
self.conv_dec = nn.Conv2d(in_channels, mask_channels, kernel_size=3, padding=1)
self.conv_att = nn.Conv2d(2*mask_channels, 1, kernel_size=3, padding=1)
def forward(self,dec_input,mask):
tmp_dec = nn.functional.relu(self.conv_dec(dec_input))
tmp_mask = torch.cat([tmp_dec,mask],dim=1)
att = torch.sigmoid(self.conv_att(tmp_mask))
output = dec_input*att+dec_input
return output
class ConvDownsampling(nn.Module):
def __init__(self,in_channels,out_channels):
super(ConvDownsampling, self).__init__()
self.block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2)
)
def forward(self,input):
return self.block(input)
class UNet_OF2(nn.Module):
#attention layers
def __init__(self,in_channels=1,out_channels=1,init_features=64,mask_channels=16):
super(UNet_OF2, self).__init__()
features = init_features
self.encoder_optfl = DoubleConv2d(in_channels,mask_channels)
self.conv_optfl1 = ConvDownsampling(mask_channels,mask_channels)
self.conv_optfl2 = ConvDownsampling(mask_channels,mask_channels)
self.conv_optfl3 = ConvDownsampling(mask_channels,mask_channels)
self.conv_optfl4 = ConvDownsampling(mask_channels,mask_channels)
self.encoder1 = DoubleConv2d(in_channels,features)
self.pool1 = nn.MaxPool2d(2, 2)
self.encoder2 = DoubleConv2d(features,2*features)
self.pool2 = nn.MaxPool2d(2, 2)
self.encoder3 = DoubleConv2d(2*features,4*features)
self.pool3 = nn.MaxPool2d(2, 2)
self.encoder4 = DoubleConv2d(4*features,8*features)
self.pool4 = nn.MaxPool2d(2, 2)
self.bottleneck = DoubleConv2d(8*features,16*features)
self.att_bot = AttentionBlock(16*features,mask_channels)
self.upconv4 = nn.ConvTranspose2d(16*features,8*features,kernel_size=2,stride=2)
self.decoder4 = DoubleConv2d(16*features,8*features) #concate, 2*8=16
self.att4 = AttentionBlock(8*features,mask_channels)
self.upconv3 = nn.ConvTranspose2d(8*features,4*features,kernel_size=2,stride=2)
self.decoder3 = DoubleConv2d(8*features,4*features) #concate, 2*4=8
self.att3 = AttentionBlock(4*features,mask_channels)
self.upconv2 = nn.ConvTranspose2d(4*features,2*features,kernel_size=2,stride=2)
self.decoder2 = DoubleConv2d(4*features,2*features) #concate, 2*2=4
self.att2 = AttentionBlock(2*features,mask_channels)
self.upconv1 = nn.ConvTranspose2d(2*features,features,kernel_size=2,stride=2)
self.decoder1 = DoubleConv2d(2*features,features) #concate, 2*1=2
self.att1 = AttentionBlock(features,mask_channels)
self.conv_out = nn.Conv2d(features, 1, 1)
def forward(self, input, mask, mode):
if mode:
# mode==1: with optical flow attention
opt1 = self.encoder_optfl(mask)
enc1 = self.encoder1(input)
opt2 = self.conv_optfl1(opt1)
enc2 = self.encoder2(self.pool1(enc1))
opt3 = self.conv_optfl2(opt2)
enc3 = self.encoder3(self.pool2(enc2))
opt4 = self.conv_optfl3(opt3)
enc4 = self.encoder4(self.pool3(enc3))
opt_bot = self.conv_optfl4(opt4)
bottleneck = self.bottleneck(self.pool4(enc4))
bottleneck = self.att_bot(bottleneck,opt_bot)
dec4 = torch.cat([enc4,self.upconv4(bottleneck)],dim=1)
dec4 = self.decoder4(dec4)
dec4 = self.att4(dec4,opt4)
dec3 = torch.cat([enc3,self.upconv3(dec4)],dim=1)
dec3 = self.decoder3(dec3)
dec3 = self.att3(dec3,opt3)
dec2 = torch.cat([enc2,self.upconv2(dec3)],dim=1)
dec2 = self.decoder2(dec2)
dec2 = self.att2(dec2,opt2)
dec1 = torch.cat([enc1,self.upconv1(dec2)],dim=1)
dec1 = self.decoder1(dec1)
dec1 = self.att1(dec1,opt1)
output = torch.sigmoid(self.conv_out(dec1))
else:
# mode==0: normal UNet FP
enc1 = self.encoder1(input)
enc2 = self.encoder2(self.pool1(enc1))
enc3 = self.encoder3(self.pool2(enc2))
enc4 = self.encoder4(self.pool3(enc3))
bottleneck = self.bottleneck(self.pool4(enc4))
dec4 = torch.cat([enc4,self.upconv4(bottleneck)],dim=1)
dec4 = self.decoder4(dec4)
dec3 = torch.cat([enc3,self.upconv3(dec4)],dim=1)
dec3 = self.decoder3(dec3)
dec2 = torch.cat([enc2,self.upconv2(dec3)],dim=1)
dec2 = self.decoder2(dec2)
dec1 = torch.cat([enc1,self.upconv1(dec2)],dim=1)
dec1 = self.decoder1(dec1)
output = torch.sigmoid(self.conv_out(dec1))
return output