unet.py

import os
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
from torch.utils.data import DataLoader,Dataset
from PIL import Image


def double_convolution(in_channels, out_channels):
    
    conv_op = nn.Sequential(
        nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
        nn.ReLU(inplace=True)
    )
    return conv_op


class UNet(nn.Module):
    def __init__(self, in_channels,out_channels):
        super(UNet, self).__init__()
        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
        
        self.down_convolution_1 = double_convolution(in_channels, 64)
        self.down_convolution_2 = double_convolution(64, 128)
        self.down_convolution_3 = double_convolution(128, 256)
        self.down_convolution_4 = double_convolution(256, 512)
        self.down_convolution_5 = double_convolution(512, 1024)
        
        self.up_transpose_1 = nn.ConvTranspose2d(
            in_channels=1024, out_channels=512,
            kernel_size=2, 
            stride=2)
        
        self.up_convolution_1 = double_convolution(1024, 512)
        self.up_transpose_2 = nn.ConvTranspose2d(
            in_channels=512, out_channels=256,
            kernel_size=2, 
            stride=2)
        self.up_convolution_2 = double_convolution(512, 256)
        self.up_transpose_3 = nn.ConvTranspose2d(
            in_channels=256, out_channels=128,
            kernel_size=2, 
            stride=2)
        self.up_convolution_3 = double_convolution(256, 128)
        self.up_transpose_4 = nn.ConvTranspose2d(
            in_channels=128, out_channels=64,
            kernel_size=2, 
            stride=2)
        self.up_convolution_4 = double_convolution(128, 64)
        
        self.out = nn.Conv2d(
            in_channels=64, out_channels=out_channels, 
            kernel_size=1
        ) 
    def forward(self, x):
        down_1 = self.down_convolution_1(x)
        down_2 = self.max_pool2d(down_1)
        down_3 = self.down_convolution_2(down_2)
        down_4 = self.max_pool2d(down_3)
        down_5 = self.down_convolution_3(down_4)
        down_6 = self.max_pool2d(down_5)
        down_7 = self.down_convolution_4(down_6)
        down_8 = self.max_pool2d(down_7)
        down_9 = self.down_convolution_5(down_8)        
        
        
        up_1 = self.up_transpose_1(down_9)
        x = self.up_convolution_1(torch.cat([down_7, up_1], 1))
        up_2 = self.up_transpose_2(x)
        x = self.up_convolution_2(torch.cat([down_5, up_2], 1))
        up_3 = self.up_transpose_3(x)
        x = self.up_convolution_3(torch.cat([down_3, up_3], 1))
        up_4 = self.up_transpose_4(x)
        x = self.up_convolution_4(torch.cat([down_1, up_4], 1))
        out = self.out(x)
        return out


class CustomDataset(Dataset):
    def __init__(self, image_dir, mask_dir, transform=None):
        self.image_dir = image_dir
        self.mask_dir = mask_dir
        self.transform = transform
        self.image_filenames = os.listdir(image_dir)
        self.mask_filenames = os.listdir(mask_dir)

    def __len__(self):
        return len(self.image_filenames)
    
    def __getitem__(self , idx):
        image_path = os.path.join(self.image_dir, self.image_filenames[idx])
        mask_path = os.path.join(self.mask_dir, self.mask_filenames[idx])

        image = Image.open(image_path).convert("RGB")
        mask = Image.open(mask_path).convert("L")

        if self.transform:
            image = self.transform(image)
            mask = self.transform(mask)
        
        return image,mask



def train_model(model, dataloader, criterion, optimizer, num_epochs=25):
    for epoch in range(num_epochs):
        model.train()
        running_loss = 0.0
        for images,masks in dataloader:

            optimizer.zero_grad()
            outputs = model(images)
            loss = criterion(outputs, masks)
            loss.backward()
            optimizer.step()
            running_loss +=loss.item()

        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(dataloader):.4f}')
        


if __name__ == "__main__":

    transform = transforms.Compose([
        transforms.Resize((256,256)),
        transforms.ToTensor(),
    ])

    image_dir = "face-synthetics-glasses/train/images"
    mask_dir = "face-synthetics-glasses/train/masks"

    dataset = CustomDataset(image_dir , mask_dir ,transform=transform)
    dataloader = DataLoader(dataset,batch_size=2,shuffle=True)

    model = UNet(3,1)
    criterion = nn.BCEWithLogitsLoss()
    optimizer = optim.Adam(model.parameters(),lr=0.001)
    print("moving ahead")

    # train_model(model,dataloader,criterion,optimizer,num_epochs=25)

    # torch.save(model.state_dict(),"base_bat_ball.pth")