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cifar10datamodule

CIFAR10DataModule

Bases: LightningDataModule

A LightningDataModule for handling CIFAR10 data.

Torchvision provides many built-in datasets in the torchvision.datasets module,

as well as utility classes for building your own datasets. All datasets are subclasses of torch.utils.data.Dataset i.e, they have getitem and len methods implemented. Hence, they can all be passed to a torch.utils.data.DataLoader which can load multiple samples in parallel using torch.multiprocessing workers, see [1].

Parameters:

Name Type Description Default
batch_size int

The size of the batch.

 required
data_dir str

The directory where the data is stored. Defaults to “./data”.

 './data'
num_workers int

The number of workers for data loading. Defaults to 0.

 0

Attributes:

Name Type Description
data_train Dataset

The training dataset.
data_val Dataset

The validation dataset.
data_test Dataset

The test dataset.
References

[1] https://pytorch.org/vision/stable/datasets.html

Source code in spotpython/light/cifar10/cifar10datamodule.py 
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class CIFAR10DataModule(pl.LightningDataModule):
    """
    A LightningDataModule for handling CIFAR10 data.

    Note: Torchvision provides many built-in datasets in the torchvision.datasets module,
        as well as utility classes for building your own datasets. All datasets are subclasses
        of torch.utils.data.Dataset i.e, they have __getitem__ and __len__ methods implemented.
        Hence, they can all be passed to a torch.utils.data.DataLoader which can load multiple
        samples in parallel using torch.multiprocessing workers, see [1].

    Args:
        batch_size (int): The size of the batch.
        data_dir (str): The directory where the data is stored. Defaults to "./data".
        num_workers (int): The number of workers for data loading. Defaults to 0.

    Attributes:
        data_train (Dataset): The training dataset.
        data_val (Dataset): The validation dataset.
        data_test (Dataset): The test dataset.

    References:
        [1] [https://pytorch.org/vision/stable/datasets.html](https://pytorch.org/vision/stable/datasets.html)
    """

    def __init__(self, batch_size: int, data_dir: str = "./data", num_workers: int = 0):
        super().__init__()
        self.batch_size = batch_size
        self.data_dir = data_dir
        self.num_workers = num_workers

    def prepare_data(self) -> None:
        """Prepares the data for use."""
        # download
        CIFAR10(root=self.data_dir, train=True, download=True)
        CIFAR10(root=self.data_dir, train=False, download=True)

    def setup(self, stage: Optional[str] = None) -> None:
        """
        Sets up the data for use.

        Args:
            stage (Optional[str]): The current stage. Defaults to None.

        """
        # Assign appropriate data transforms, see
        # https://lightning.ai/docs/pytorch/latest/notebooks/course_UvA-DL/04-inception-resnet-densenet.html
        DATA_MEANS = (0.49139968, 0.48215841, 0.44653091)
        DATA_STDS = (0.24703223, 0.24348513, 0.26158784)
        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(DATA_MEANS, DATA_STDS)])
        # Assign train/val datasets for use in dataloaders
        if stage == "fit" or stage is None:
            data_full = CIFAR10(root=self.data_dir, train=True, transform=transform)
            test_abs = int(len(data_full) * 0.6)
            self.data_train, self.data_val = random_split(data_full, [test_abs, len(data_full) - test_abs])

        # Assign test dataset for use in dataloader(s)
        if stage == "test" or stage is None:
            self.data_test = CIFAR10(root=self.data_dir, train=False, transform=transform)

    def train_dataloader(self) -> DataLoader:
        """
        Returns the training dataloader.

        Returns:
            DataLoader: The training dataloader.

        """
        print("train_dataloader: self.batch_size", self.batch_size)
        return DataLoader(self.data_train, batch_size=self.batch_size, shuffle=True, drop_last=True, num_workers=self.num_workers)

    def val_dataloader(self) -> DataLoader:
        """
        Returns the validation dataloader.

        Returns:
            DataLoader: The validation dataloader.


        """
        print("val_dataloader: self.batch_size", self.batch_size)
        return DataLoader(self.data_val, batch_size=self.batch_size, shuffle=False, drop_last=False, num_workers=self.num_workers)

    def test_dataloader(self) -> DataLoader:
        """
        Returns the test dataloader.

        Returns:
            DataLoader: The test dataloader.


        """
        print("train_data_loader: self.batch_size", self.batch_size)
        return DataLoader(self.data_test, batch_size=self.batch_size, shuffle=False, drop_last=False, num_workers=self.num_workers)

prepare_data()

Prepares the data for use.

Source code in spotpython/light/cifar10/cifar10datamodule.py 
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def prepare_data(self) -> None:
    """Prepares the data for use."""
    # download
    CIFAR10(root=self.data_dir, train=True, download=True)
    CIFAR10(root=self.data_dir, train=False, download=True)

setup(stage=None)

Sets up the data for use.

Parameters:

Name Type Description Default
stage Optional[str]

The current stage. Defaults to None.

 None
Source code in spotpython/light/cifar10/cifar10datamodule.py 
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def setup(self, stage: Optional[str] = None) -> None:
    """
    Sets up the data for use.

    Args:
        stage (Optional[str]): The current stage. Defaults to None.

    """
    # Assign appropriate data transforms, see
    # https://lightning.ai/docs/pytorch/latest/notebooks/course_UvA-DL/04-inception-resnet-densenet.html
    DATA_MEANS = (0.49139968, 0.48215841, 0.44653091)
    DATA_STDS = (0.24703223, 0.24348513, 0.26158784)
    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(DATA_MEANS, DATA_STDS)])
    # Assign train/val datasets for use in dataloaders
    if stage == "fit" or stage is None:
        data_full = CIFAR10(root=self.data_dir, train=True, transform=transform)
        test_abs = int(len(data_full) * 0.6)
        self.data_train, self.data_val = random_split(data_full, [test_abs, len(data_full) - test_abs])

    # Assign test dataset for use in dataloader(s)
    if stage == "test" or stage is None:
        self.data_test = CIFAR10(root=self.data_dir, train=False, transform=transform)

test_dataloader()

Returns the test dataloader.

Returns:

Name Type Description
DataLoader DataLoader

The test dataloader.
Source code in spotpython/light/cifar10/cifar10datamodule.py 
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def test_dataloader(self) -> DataLoader:
    """
    Returns the test dataloader.

    Returns:
        DataLoader: The test dataloader.


    """
    print("train_data_loader: self.batch_size", self.batch_size)
    return DataLoader(self.data_test, batch_size=self.batch_size, shuffle=False, drop_last=False, num_workers=self.num_workers)

train_dataloader()

Returns the training dataloader.

Returns:

Name Type Description
DataLoader DataLoader

The training dataloader.
Source code in spotpython/light/cifar10/cifar10datamodule.py 
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def train_dataloader(self) -> DataLoader:
    """
    Returns the training dataloader.

    Returns:
        DataLoader: The training dataloader.

    """
    print("train_dataloader: self.batch_size", self.batch_size)
    return DataLoader(self.data_train, batch_size=self.batch_size, shuffle=True, drop_last=True, num_workers=self.num_workers)

val_dataloader()

Returns the validation dataloader.

Returns:

Name Type Description
DataLoader DataLoader

The validation dataloader.
Source code in spotpython/light/cifar10/cifar10datamodule.py 
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def val_dataloader(self) -> DataLoader:
    """
    Returns the validation dataloader.

    Returns:
        DataLoader: The validation dataloader.


    """
    print("val_dataloader: self.batch_size", self.batch_size)
    return DataLoader(self.data_val, batch_size=self.batch_size, shuffle=False, drop_last=False, num_workers=self.num_workers)