model.py

import torch 
import torch.nn as nn
from torch.nn import functional as F

class InConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.conv = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, 3, padding=1),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(inplace=True),
                nn.Conv2d(out_channels, out_channels, 3, padding=1),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(inplace=True)
                )
    def forward(self, x):
        return self.conv(x)
    
class Down(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.down = nn.Sequential(
                nn.MaxPool2d(2),
                InConv(in_channels, out_channels)
                )
    def forward(self, x):
        return self.down(x)
    
class Up(nn.Module):
    def __init__(self, in_channels, out_channels, bilinear=False):
        super().__init__()
        if bilinear:
            self.up = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
                                    nn.Conv2d(in_channels, in_channels // 2, 1))
        else:
            self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, 2, stride=2)
            
        self.conv = InConv(in_channels, out_channels)

    def forward(self, x1, x2):
        x1 = self.up(x1)
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]
        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2])
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)

class OutConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, 1)

    def forward(self, x):
        return self.conv(x)
    

class Unet(nn.Module):
    def __init__(self, in_channels, classes):
        super(Unet, self).__init__()
        self.n_channels = in_channels
        self.n_classes =  classes

        self.inc = InConv(in_channels, 64)  #[1, 64, 256, 256]
        self.down1 = Down(64, 128)          #[1, 128, 128, 128]
        self.down2 = Down(128, 256)         #[1, 256, 64, 64]
        self.down3 = Down(256, 512)         #[1, 512, 32, 32]
        self.down4 = Down(512, 1024)        #[1, 1024, 16, 16]
        self.up1 = Up(1024, 512)            #[1, 512, 32, 32]
        self.up2 = Up(512, 256)             #[1, 256, 64, 64]
        self.up3 = Up(256, 128)             #[1, 128, 128, 128]
        self.up4 = Up(128, 64)              #[1, 64, 256, 256]
        self.outc = OutConv(64, classes)    #[1, 1, 256, 256]

    def forward(self, x):
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        x = self.outc(x)
        return x