def makeDataSet(IMAGE_SHAPE=300, DATA_PATH='./data_after_splitting/'):
    image_transforms = {'train': transforms.Compose([transforms.Resize((IMAGE_SHAPE, IMAGE_SHAPE)), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), 'val': transforms.Compose([transforms.Resize((IMAGE_SHAPE, IMAGE_SHAPE)), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])}
    train_dataset = datasets.ImageFolder(root=(DATA_PATH + 'train'), transform=image_transforms['train'])
    val_dataset = datasets.ImageFolder(root=(DATA_PATH + 'val'), transform=image_transforms['val'])
    train_dataloader = DataLoader(train_dataset, batch_size=4, num_workers=2, shuffle=True)
    val_dataloader = DataLoader(val_dataset, batch_size=4, num_workers=2, shuffle=True)
    return (train_dataloader, val_dataloader)
def makeDataSet(IMAGE_SHAPE=300, DATA_PATH='./data_after_splitting/'):
    image_transforms = {'train': transforms.Compose([transforms.Resize((IMAGE_SHAPE, IMAGE_SHAPE)), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), 'val': transforms.Compose([transforms.Resize((IMAGE_SHAPE, IMAGE_SHAPE)), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])}
    train_dataset = datasets.ImageFolder(root=(DATA_PATH + 'train'), transform=image_transforms['train'])
    val_dataset = datasets.ImageFolder(root=(DATA_PATH + 'val'), transform=image_transforms['val'])
    train_dataloader = DataLoader(train_dataset, batch_size=4, num_workers=2, shuffle=True)
    val_dataloader = DataLoader(val_dataset, batch_size=4, num_workers=2, shuffle=True)
    return (train_dataloader, val_dataloader)