data/datamodule.py

import pytorch_lightning as pl
from torchvision.datasets import VOCSegmentation
from torchvision.transforms import transforms
from torch.utils.data import DataLoader, random_split

class SegmentationDataModule(pl.LightningDataModule):
    def __init__(self, data_dir: str, config):
        super().__init__()
        self.config = config

        # Transformación para la imagen y la máscara
        image_transform = transforms.Compose([
            transforms.Resize((128, 128)), 
            transforms.ToTensor()
        ])
        mask_transform = transforms.Compose([
            transforms.Resize((128, 128)),  # Puedes añadir más transformaciones si lo deseas
            transforms.ToTensor(),
            lambda x: x.long()
        ])
        self.transform = DualTransform(image_transform, mask_transform)
        self.data_dir = data_dir

    def prepare_data(self):
        # Descargar el dataset (si es necesario)
        VOCSegmentation(root=self.data_dir, year='2012', image_set='trainval', download=False)

    def setup(self, stage=None):
        # Inicializa el dataset
        self.dataset = VOCSegmentation(root=self.data_dir, year='2012', image_set='trainval', transforms=self.transform)

        # Dividir el dataset y asignar a sets de entrenamiento/validación
        train_len = int(0.8 * len(self.dataset))
        val_len = len(self.dataset) - train_len
        self.train_dataset, self.val_dataset = random_split(self.dataset, [train_len, val_len])

    def train_dataloader(self):
        return DataLoader(self.train_dataset, batch_size=self.config.batch_size, shuffle=self.config.shuffle, num_workers=self.config.num_workers)

    def val_dataloader(self):
        return DataLoader(self.val_dataset, batch_size=self.config.batch_size, shuffle=False, num_workers=self.config.num_workers)

    # Puedes añadir un test_dataloader si lo deseas

class DualTransform:
    def __init__(self, image_transform, mask_transform):
        self.image_transform = image_transform
        self.mask_transform = mask_transform

    def __call__(self, image, mask):
        return self.image_transform(image), self.mask_transform(mask)