Unet_model.py

import torch 
import torchvision.transforms.functional as TF
import torch.nn as nn


class DoubleConv(nn.Module):
    def __init__(self,in_channels , out_channels):
        super(DoubleConv,self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, 3,1,1,bias = False ),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, 3,1,1,bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )
    def forward(self,x):
        return self.conv(x)


class Unet(nn.Module):
    def __init__(
        self,in_channels=3,out_channels=1,features=[64,128,256,512]):
        super(Unet, self).__init__()
        self.ups = nn.ModuleList()
        self.downs = nn.ModuleList()
        self.pool = nn.MaxPool2d(kernel_size= 2 , stride = 2)

        # Down part 
        for feature in features:
            self.downs.append(DoubleConv(in_channels,feature))
            in_channels = feature
        
        # Up part of the unet

        for feature in reversed(features):
            self.ups.append(
                nn.ConvTranspose2d(feature *2, feature, kernel_size=2,stride =2)
            )
            self.ups.append(DoubleConv(feature*2,feature))
        
        self.bottleneck = DoubleConv(features[-1], features[-1]*2)
        self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size =1)

    def forward(self ,x ):
        skip_connections = []

        for down in self.downs:
            x = down(x)
            skip_connections.append(x)
            x = self.pool(x)
        
        x = self.bottleneck(x)
        skip_connections = skip_connections[::-1] 

        for idx in range(0,len(self.ups),2):
            x =self.ups[idx](x)
            skip_connection = skip_connections[idx//2]
            if x.shape != skip_connection.shape:
                x = TF.resize(x, size=skip_connection.shape[2:])
            concat_skip = torch.cat((skip_connection,x),dim=1)
            x = self.ups[idx+1](concat_skip)
        
        return self.final_conv(x)

def test():
    x = torch.randn((3,1,160,160))
    model = Unet(in_channels =1  , out_channels =1)
    preds = model(x)
    print(preds.shape)
    print(x.shape)
    assert preds.shape == x.shape

if __name__  == "__main__":
    test()