model.py

import torch
import torch.nn as nn
import torchvision.transforms.functional as TF
from torch.optim.lr_scheduler import ReduceLROnPlateau
import torch.nn.functional as F

class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels, dropout_prob=0.0):
        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),
            nn.Dropout2d(p=dropout_prob)  # Add dropout layer
        )

    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], dropout_prob=0.0,
    ):
        super(UNET, self).__init__()
        self.ups = nn.ModuleList()
        self.downs = 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, dropout_prob=dropout_prob))
            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, dropout_prob=dropout_prob))

        self.bottleneck = DoubleConv(features[-1], features[-1] * 2, dropout_prob=dropout_prob)
        self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)

        # Gradient clipping
        self.gradient_clip = nn.utils.clip_grad_norm_

    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]

            # Adjust padding to ensure skip connection compatibility
            diffY = skip_connection.size()[2] - x.size()[2]
            diffX = skip_connection.size()[3] - x.size()[3]

            x = F.pad(x, [diffX // 2, diffX - diffX // 2,
                          diffY // 2, diffY - diffY // 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, 161, 161))
    model = UNET(in_channels=1, out_channels=1)
    preds = model(x)
    assert preds.shape == x.shape


if __name__ == '__main__':
    test()