datamodules.Classification package

Subpackages

• datamodules.Classification.utils package
  • Submodules
  • datamodules.Classification.utils.image_analytics module
  • datamodules.Classification.utils.misc module
  • Module contents

Submodules

datamodules.Classification.datamodule module

class ClassificationDatamodule(data_dir: str, selection_train: Optional[Union[int, List[str]]] = None, selection_val: Optional[Union[int, List[str]]] = None, num_workers: int = 4, batch_size: int = 8, shuffle: bool = True, drop_last: bool = True)

Bases: AbstractDatamodule

Datamodule for a classification task. It takes advantage of the ImageFolder class from PyTorch

The data is expected to be in the following format:

data_dir
    ├── train
    │   ├── 0
    │   │   ├── image_1.png
    │   │   ├── ...
    │   │   └── image_N.png
    │   ├── ...
    │   └── N
    │       ├── image_1.png
    │       ├── ...
    │       └── image_N.png
    ├──  val
    │   ├── 0
    │   │   ├── image_1.png
    │   │   ├── ...
    │   │   └── image_N.png
    │   ├── ...
    │   └── N
    │       ├── image_1.png
    │       ├── ...
    │       └── image_N.png
    └── test
        ├── 0
        │   ├── image_1.png
        │   ├── ...
        │   └── image_N.png
        ├── ...
        └── N
            ├── image_1.png
            ├── ...
            └── image_N.png

Parameters:

• data_dir (str) – Path to the root directory of the dataset.

• selection_train (Optional[Union[int, List[str]]]) – Either an integer or a list of strings. If an integer is provided, the first n classes are selected. If a list of strings is provided, the classes with the given names are selected.

• selection_val (Optional[Union[int, List[str]]]) – Either an integer or a list of strings. If an integer is provided, the first n classes are selected. If a list of strings is provided, the classes with the given names are selected.

• num_workers (int) – Number of workers for the dataloaders.

• batch_size (int) – Batch size for the dataloaders.

• shuffle (bool) – Whether to shuffle the data.

• drop_last (bool) – Whether to drop the last batch if it is smaller than the batch size.

setup(stage: Optional[str] = None)

Called at the beginning of fit (train + validate), validate, test, or predict. This is a good hook when you need to build models dynamically or adjust something about them. This hook is called on every process when using DDP.

Parameters:

stage – either 'fit', 'validate', 'test', or 'predict'

Example:

class LitModel(...):
    def __init__(self):
        self.l1 = None

    def prepare_data(self):
        download_data()
        tokenize()

        # don't do this
        self.something = else

    def setup(self, stage):
        data = load_data(...)
        self.l1 = nn.Linear(28, data.num_classes)

test_dataloader(*args, **kwargs) → Union[DataLoader, List[DataLoader]]

Implement one or multiple PyTorch DataLoaders for testing.

For data processing use the following pattern:

• download in prepare_data()

• process and split in setup()

However, the above are only necessary for distributed processing.

Warning

do not assign state in prepare_data

• test()

• prepare_data()

• setup()

Note

Lightning adds the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

Returns:

A torch.utils.data.DataLoader or a sequence of them specifying testing samples.

Example:

def test_dataloader(self):
    transform = transforms.Compose([transforms.ToTensor(),
                                    transforms.Normalize((0.5,), (1.0,))])
    dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,
                    download=True)
    loader = torch.utils.data.DataLoader(
        dataset=dataset,
        batch_size=self.batch_size,
        shuffle=False
    )

    return loader

# can also return multiple dataloaders
def test_dataloader(self):
    return [loader_a, loader_b, ..., loader_n]

Note

If you don’t need a test dataset and a test_step(), you don’t need to implement this method.

Note

In the case where you return multiple test dataloaders, the test_step() will have an argument dataloader_idx which matches the order here.

train_dataloader(*args, **kwargs) → DataLoader

Implement one or more PyTorch DataLoaders for training.

Returns:

A collection of torch.utils.data.DataLoader specifying training samples. In the case of multiple dataloaders, please see this section.

The dataloader you return will not be reloaded unless you set :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_n_epochs` to a positive integer.

For data processing use the following pattern:

• download in prepare_data()

• process and split in setup()

However, the above are only necessary for distributed processing.

Warning

do not assign state in prepare_data

• fit()

• prepare_data()

• setup()

Note

Lightning adds the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

Example:

# single dataloader
def train_dataloader(self):
    transform = transforms.Compose([transforms.ToTensor(),
                                    transforms.Normalize((0.5,), (1.0,))])
    dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,
                    download=True)
    loader = torch.utils.data.DataLoader(
        dataset=dataset,
        batch_size=self.batch_size,
        shuffle=True
    )
    return loader

# multiple dataloaders, return as list
def train_dataloader(self):
    mnist = MNIST(...)
    cifar = CIFAR(...)
    mnist_loader = torch.utils.data.DataLoader(
        dataset=mnist, batch_size=self.batch_size, shuffle=True
    )
    cifar_loader = torch.utils.data.DataLoader(
        dataset=cifar, batch_size=self.batch_size, shuffle=True
    )
    # each batch will be a list of tensors: [batch_mnist, batch_cifar]
    return [mnist_loader, cifar_loader]

# multiple dataloader, return as dict
def train_dataloader(self):
    mnist = MNIST(...)
    cifar = CIFAR(...)
    mnist_loader = torch.utils.data.DataLoader(
        dataset=mnist, batch_size=self.batch_size, shuffle=True
    )
    cifar_loader = torch.utils.data.DataLoader(
        dataset=cifar, batch_size=self.batch_size, shuffle=True
    )
    # each batch will be a dict of tensors: {'mnist': batch_mnist, 'cifar': batch_cifar}
    return {'mnist': mnist_loader, 'cifar': cifar_loader}

val_dataloader(*args, **kwargs) → Union[DataLoader, List[DataLoader]]

Implement one or multiple PyTorch DataLoaders for validation.

The dataloader you return will not be reloaded unless you set :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_n_epochs` to a positive integer.

It’s recommended that all data downloads and preparation happen in prepare_data().

• fit()

• validate()

• prepare_data()

• setup()

Note

Lightning adds the correct sampler for distributed and arbitrary hardware There is no need to set it yourself.

Returns:

A torch.utils.data.DataLoader or a sequence of them specifying validation samples.

Examples:

def val_dataloader(self):
    transform = transforms.Compose([transforms.ToTensor(),
                                    transforms.Normalize((0.5,), (1.0,))])
    dataset = MNIST(root='/path/to/mnist/', train=False,
                    transform=transform, download=True)
    loader = torch.utils.data.DataLoader(
        dataset=dataset,
        batch_size=self.batch_size,
        shuffle=False
    )

    return loader

# can also return multiple dataloaders
def val_dataloader(self):
    return [loader_a, loader_b, ..., loader_n]

Note

If you don’t need a validation dataset and a validation_step(), you don’t need to implement this method.

Note

In the case where you return multiple validation dataloaders, the validation_step() will have an argument dataloader_idx which matches the order here.

Module contents