agnapprox.datamodules.approx_datamodule#

Wrapper for LightningDataModule with boilerplate functionality

Module Contents#

Classes#

ApproxDataModule

Superclass that provides a common dataloader boilerplate
class agnapprox.datamodules.approx_datamodule.ApproxDataModule(**kwargs)[source]#

Bases: pytorch_lightning.LightningDataModule

Superclass that provides a common dataloader boilerplate functionality for all datasets. Mostly derived from the Pytorch Lightning Docs. This class is not expected to be instantiated directly.

_create_data_loader(data)[source]#
train_dataloader()[source]#

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 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()[source]#

Implement one or multiple PyTorch DataLoaders for validation.

The dataloader you return will not be reloaded unless you set 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.

test_dataloader()[source]#

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.

sample_dataloader(num_samples=128)[source]#

Load a random sample from the training dataset.

Parameters:

num_samples – Number of samples to return. Defaults to 128.

Returns:

A dataloader instance with num_samples samples