dummy_dataset

class mfai.pytorch.dummy_dataset.DummyDataModule(task='binary', batch_size=2, dim_x=64, dim_y=64, nb_input_channels=2, nb_output_channels=1)

Bases: LightningDataModule

A Lightning DataModule wrapping our dummy dataset. It defines the train/valid/test/predict datasets and their dataloaders.

Parameters:

• task (Literal['binary', 'multiclass', 'multilabel', 'regression'])

• batch_size (int)

• dim_x (int)

• dim_y (int)

• nb_input_channels (int)

• nb_output_channels (int)

predict_dataloader()

An iterable or collection of iterables specifying prediction samples.

For more information about multiple dataloaders, see this section.

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

• predict()

• prepare_data()

• setup()

Note

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

Return type:

DataLoader

Returns:

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

setup(stage='')

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 (str) – either 'fit', 'validate', 'test', or 'predict'

Return type:

None

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()

An iterable or collection of iterables specifying test samples.

For more information about multiple dataloaders, see this section.

For data processing use the following pattern: :rtype: DataLoader

• 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 tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

Note

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

Return type:

DataLoader

train_dataloader()

An iterable or collection of iterables specifying training samples.

For more information about multiple dataloaders, see this section.

The dataloader you return will not be reloaded unless you set :rtype: DataLoader

:paramref:`~lightning.pytorch.trainer.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 tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

Return type:

DataLoader

val_dataloader()

An iterable or collection of iterables specifying validation samples.

For more information about multiple dataloaders, see this section.

The dataloader you return will not be reloaded unless you set :rtype: DataLoader

:paramref:`~lightning.pytorch.trainer.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 tries to add the correct sampler for distributed and arbitrary hardware There is no need to set it yourself.

Note

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

Return type:

DataLoader

class mfai.pytorch.dummy_dataset.DummyDataset(split, task='binary', dim_x=64, dim_y=64, nb_input_channels=2, nb_output_channels=1)

Bases: Dataset

A dummy segmentation dataset to test our training modules. X is a random float tensor of chosen size. Y is a random binary tensor of chosen size. X and Y share the same height and width.

Parameters:

• split (str)

• task (Literal['binary', 'multiclass', 'multilabel', 'regression'])

• dim_x (int)

• dim_y (int)

• nb_input_channels (int)

• nb_output_channels (int)

class mfai.pytorch.dummy_dataset.DummyMultiModalDataModule(batch_size=2, dim_x=64, dim_y=64, nb_input_channels=2, context_length=8)

Bases: LightningDataModule

A Lightning DataModule wrapping our dummy dataset. It defines the train/valid/test/predict datasets and their dataloaders.

Parameters:

• batch_size (int)

• dim_x (int)

• dim_y (int)

• nb_input_channels (int)

• context_length (int)

collate_fn_fit(batch)

Collate a batch of multimodal data.

Return type:

Tuple[NamedTensor, Tensor, Tensor]

Parameters:

batch (List[Tuple[NamedTensor, Tensor, Tensor]])

collate_text(batch, target=False, prompt=False)

Collate a batch of text tensors.

Return type:

Tensor

Parameters:

• batch (List[Tensor])

• target (bool)

• prompt (bool)

setup(stage='')

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 (str) – either 'fit', 'validate', 'test', or 'predict'

Return type:

None

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()

An iterable or collection of iterables specifying test samples.

For more information about multiple dataloaders, see this section.

For data processing use the following pattern: :rtype: DataLoader

• 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 tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

Note

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

Return type:

DataLoader

train_dataloader()

An iterable or collection of iterables specifying training samples.

For more information about multiple dataloaders, see this section.

The dataloader you return will not be reloaded unless you set :rtype: DataLoader

:paramref:`~lightning.pytorch.trainer.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 tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

Return type:

DataLoader

val_dataloader()

An iterable or collection of iterables specifying validation samples.

For more information about multiple dataloaders, see this section.

The dataloader you return will not be reloaded unless you set :rtype: DataLoader

:paramref:`~lightning.pytorch.trainer.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 tries to add the correct sampler for distributed and arbitrary hardware There is no need to set it yourself.

Note

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

Return type:

DataLoader

class mfai.pytorch.dummy_dataset.DummyMultiModalDataset(split, dim_x=64, dim_y=64, nb_input_channels=2, context_length=8)

Bases: Dataset

A dummy multimodal dataset to test our training modules.

• image is a random float tensor of size (nb_input_channels, dim_x, dim_y).

• input_text is a random integer tensor of size (n_tokens, emb_dim).

• output_text is a random integer tensor of size (n_tokens+1, emb_dim).

Parameters:

• split (str)

• dim_x (int)

• dim_y (int)

• nb_input_channels (int)

• context_length (int)