model.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""ANIMESH BALA ANI"""

# Import Modules
import torch
import torch.nn as nn
import torchvision.transforms.functional as TF


# Double Conv Block
class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__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)


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

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

        # Up part of 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:])
            
            x = self.ups[idx+1](torch.cat((skip_connection, x), dim=1))
        
        return self.final_conv(x)





# Test Block
def test():
    x = torch.randn((3, 3, 161, 161))
    model = UNet(in_channels=3, out_channels=1)
    print(model(x).shape)

if __name__ == "__main__":
    test()