scvi.dataloaders.DataSplitter

scvi.dataloaders.DataSplitter#

class scvi.dataloaders.DataSplitter(adata_manager, train_size=0.9, validation_size=None, shuffle_set_split=True, load_sparse_tensor=False, pin_memory=False, external_indexing=None, **kwargs)[source]#

Creates data loaders train_set, validation_set, test_set.

If train_size + validation_set < 1 then test_set is non-empty.

Parameters:

  • adata_manager (AnnDataManager) – AnnDataManager object that has been created via setup_anndata.

  • train_size (float (default: 0.9)) – float, or None (default is 0.9)

  • validation_size (float | None (default: None)) – float, or None (default is None)

  • shuffle_set_split (bool (default: True)) – Whether to shuffle indices before splitting. If False, the val, train, and test set are split in the sequential order of the data according to validation_size and train_size percentages.

  • load_sparse_tensor (bool (default: False)) – EXPERIMENTAL If True, loads sparse CSR or CSC arrays in the input dataset as sparse Tensor with the same layout. Can lead to significant speedups in transferring data to GPUs, depending on the sparsity of the data.

  • pin_memory (bool (default: False)) – Whether to copy tensors into device-pinned memory before returning them. Passed into AnnDataLoader.

  • external_indexing (list[array, array, array] | None (default: None)) – A list of data split indices in the order of training, validation, and test sets. Validation and test set are not required and can be left empty.

  • **kwargs – Keyword args for data loader. If adata has labeled data, data loader class is SemiSupervisedDataLoader, else data loader class is AnnDataLoader.

Examples

>>> adata = scvi.data.synthetic_iid()
>>> scvi.model.SCVI.setup_anndata(adata)
>>> adata_manager = scvi.model.SCVI(adata).adata_manager
>>> splitter = DataSplitter(adata)
>>> splitter.setup()
>>> train_dl = splitter.train_dataloader()

Attributes table#

CHECKPOINT_HYPER_PARAMS_KEY

CHECKPOINT_HYPER_PARAMS_NAME

CHECKPOINT_HYPER_PARAMS_TYPE

hparams

The collection of hyperparameters saved with save_hyperparameters().

hparams_initial

The collection of hyperparameters saved with save_hyperparameters().

name

Methods table#

from_datasets([train_dataset, val_dataset, ...])

Create an instance from torch.utils.data.Dataset.

load_from_checkpoint(checkpoint_path[, ...])

Primary way of loading a datamodule from a checkpoint.

load_state_dict(state_dict)

Called when loading a checkpoint, implement to reload datamodule state given datamodule state_dict.

on_after_batch_transfer(batch, dataloader_idx)

Converts sparse tensors to dense if necessary.

on_before_batch_transfer(batch, dataloader_idx)

Override to alter or apply batch augmentations to your batch before it is transferred to the device.

on_exception(exception)

Called when the trainer execution is interrupted by an exception.

predict_dataloader()

An iterable or collection of iterables specifying prediction samples.

prepare_data()

Use this to download and prepare data.

save_hyperparameters(*args[, ignore, frame, ...])

Save arguments to hparams attribute.

setup([stage])

Split indices in train/test/val sets.

state_dict()

Called when saving a checkpoint, implement to generate and save datamodule state.

teardown(stage)

Called at the end of fit (train + validate), validate, test, or predict.

test_dataloader()

Create test data loader.

train_dataloader()

Create train data loader.

transfer_batch_to_device(batch, device, ...)

Override this hook if your DataLoader returns tensors wrapped in a custom data structure.

val_dataloader()

Create validation data loader.

Attributes#

DataSplitter.CHECKPOINT_HYPER_PARAMS_KEY = 'datamodule_hyper_parameters'#
DataSplitter.CHECKPOINT_HYPER_PARAMS_NAME = 'datamodule_hparams_name'#
DataSplitter.CHECKPOINT_HYPER_PARAMS_TYPE = 'datamodule_hparams_type'#
DataSplitter.hparams[source]#

The collection of hyperparameters saved with save_hyperparameters(). It is mutable by the user. For the frozen set of initial hyperparameters, use hparams_initial.

Returns:

Mutable hyperparameters dictionary

DataSplitter.hparams_initial[source]#

The collection of hyperparameters saved with save_hyperparameters(). These contents are read-only. Manual updates to the saved hyperparameters can instead be performed through hparams.

Returns:

immutable initial hyperparameters

Return type:

AttributeDict

DataSplitter.name: Optional[str] = None#

Methods#

classmethod DataSplitter.from_datasets(train_dataset=None, val_dataset=None, test_dataset=None, predict_dataset=None, batch_size=1, num_workers=0, **datamodule_kwargs)[source]#

Create an instance from torch.utils.data.Dataset.

Parameters:

  • train_dataset (Union[Dataset, Iterable[Dataset], None] (default: None)) – Optional dataset or iterable of datasets to be used for train_dataloader()

  • val_dataset (Union[Dataset, Iterable[Dataset], None] (default: None)) – Optional dataset or iterable of datasets to be used for val_dataloader()

  • test_dataset (Union[Dataset, Iterable[Dataset], None] (default: None)) – Optional dataset or iterable of datasets to be used for test_dataloader()

  • predict_dataset (Union[Dataset, Iterable[Dataset], None] (default: None)) – Optional dataset or iterable of datasets to be used for predict_dataloader()

  • batch_size (int (default: 1)) – Batch size to use for each dataloader. Default is 1. This parameter gets forwarded to the __init__ if the datamodule has such a name defined in its signature.

  • num_workers (int (default: 0)) – Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process. Number of CPUs available. This parameter gets forwarded to the __init__ if the datamodule has such a name defined in its signature.

  • **datamodule_kwargs (Any) – Additional parameters that get passed down to the datamodule’s __init__.

Return type:

LightningDataModule

DataSplitter.load_from_checkpoint(checkpoint_path, map_location=None, hparams_file=None, **kwargs)[source]#

Primary way of loading a datamodule from a checkpoint. When Lightning saves a checkpoint it stores the arguments passed to __init__ in the checkpoint under "datamodule_hyper_parameters".

Any arguments specified through **kwargs will override args stored in "datamodule_hyper_parameters".

Parameters:

  • checkpoint_path (Union[str, Path, IO]) – Path to checkpoint. This can also be a URL, or file-like object

  • map_location (Union[device, str, int, Callable[[UntypedStorage, str], Optional[UntypedStorage]], Dict[Union[device, str, int], Union[device, str, int]], None] (default: None)) – If your checkpoint saved a GPU model and you now load on CPUs or a different number of GPUs, use this to map to the new setup. The behaviour is the same as in torch.load().

  • hparams_file (Union[str, Path, None] (default: None)) –

    Optional path to a .yaml or .csv file with hierarchical structure as in this example:

    dataloader:
    batch_size: 32

    You most likely won’t need this since Lightning will always save the hyperparameters to the checkpoint. However, if your checkpoint weights don’t have the hyperparameters saved, use this method to pass in a .yaml file with the hparams you’d like to use. These will be converted into a dict and passed into your LightningDataModule for use.

    If your datamodule’s hparams argument is Namespace and .yaml file has hierarchical structure, you need to refactor your datamodule to treat hparams as dict.

  • **kwargs (Any) – Any extra keyword args needed to init the datamodule. Can also be used to override saved hyperparameter values.

Return type:

Self

Returns:

LightningDataModule instance with loaded weights and hyperparameters (if available).

Note

load_from_checkpoint is a class method. You must use your LightningDataModule class to call it instead of the LightningDataModule instance, or a TypeError will be raised.

Example:

# load weights without mapping ...
datamodule = MyLightningDataModule.load_from_checkpoint('path/to/checkpoint.ckpt')

# or load weights and hyperparameters from separate files.
datamodule = MyLightningDataModule.load_from_checkpoint(
    'path/to/checkpoint.ckpt',
    hparams_file='/path/to/hparams_file.yaml'
)

# override some of the params with new values
datamodule = MyLightningDataModule.load_from_checkpoint(
    PATH,
    batch_size=32,
    num_workers=10,
)
DataSplitter.load_state_dict(state_dict)[source]#

Called when loading a checkpoint, implement to reload datamodule state given datamodule state_dict.

Parameters:

state_dict (Dict[str, Any]) – the datamodule state returned by state_dict.

Return type:

None

DataSplitter.on_after_batch_transfer(batch, dataloader_idx)[source]#

Converts sparse tensors to dense if necessary.

DataSplitter.on_before_batch_transfer(batch, dataloader_idx)[source]#

Override to alter or apply batch augmentations to your batch before it is transferred to the device.

Note

To check the current state of execution of this hook you can use self.trainer.training/testing/validating/predicting so that you can add different logic as per your requirement.

Parameters:

  • batch (Any) – A batch of data that needs to be altered or augmented.

  • dataloader_idx (int) – The index of the dataloader to which the batch belongs.

Return type:

Any

Returns:

A batch of data

Example:

def on_before_batch_transfer(self, batch, dataloader_idx):
    batch['x'] = transforms(batch['x'])
    return batch

See also

DataSplitter.on_exception(exception)[source]#

Called when the trainer execution is interrupted by an exception.

Return type:

None

DataSplitter.predict_dataloader()[source]#

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().

Note

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

Return type:

Any

Returns:

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

DataSplitter.prepare_data()[source]#

Use this to download and prepare data. Downloading and saving data with multiple processes (distributed settings) will result in corrupted data. Lightning ensures this method is called only within a single process, so you can safely add your downloading logic within. :rtype: None

Warning

DO NOT set state to the model (use setup instead) since this is NOT called on every device

Example:

def prepare_data(self):
    # good
    download_data()
    tokenize()
    etc()

    # bad
    self.split = data_split
    self.some_state = some_other_state()

In a distributed environment, prepare_data can be called in two ways (using prepare_data_per_node)

  1. Once per node. This is the default and is only called on LOCAL_RANK=0.

  2. Once in total. Only called on GLOBAL_RANK=0.

Example:

# DEFAULT
# called once per node on LOCAL_RANK=0 of that node
class LitDataModule(LightningDataModule):
    def __init__(self):
        super().__init__()
        self.prepare_data_per_node = True


# call on GLOBAL_RANK=0 (great for shared file systems)
class LitDataModule(LightningDataModule):
    def __init__(self):
        super().__init__()
        self.prepare_data_per_node = False

This is called before requesting the dataloaders:

model.prepare_data()
initialize_distributed()
model.setup(stage)
model.train_dataloader()
model.val_dataloader()
model.test_dataloader()
model.predict_dataloader()
DataSplitter.save_hyperparameters(*args, ignore=None, frame=None, logger=True)[source]#

Save arguments to hparams attribute.

Parameters:

  • args (Any) – single object of dict, NameSpace or OmegaConf or string names or arguments from class __init__

  • ignore (Union[Sequence[str], str, None] (default: None)) – an argument name or a list of argument names from class __init__ to be ignored

  • frame (Optional[FrameType] (default: None)) – a frame object. Default is None

  • logger (bool (default: True)) – Whether to send the hyperparameters to the logger. Default: True

Return type:

None

Example::
>>> from lightning.pytorch.core.mixins import HyperparametersMixin
>>> class ManuallyArgsModel(HyperparametersMixin):
...     def __init__(self, arg1, arg2, arg3):
...         super().__init__()
...         # manually assign arguments
...         self.save_hyperparameters('arg1', 'arg3')
...     def forward(self, *args, **kwargs):
...         ...
>>> model = ManuallyArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg3": 3.14

>>> from lightning.pytorch.core.mixins import HyperparametersMixin
>>> class AutomaticArgsModel(HyperparametersMixin):
...     def __init__(self, arg1, arg2, arg3):
...         super().__init__()
...         # equivalent automatic
...         self.save_hyperparameters()
...     def forward(self, *args, **kwargs):
...         ...
>>> model = AutomaticArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg2": abc
"arg3": 3.14

>>> from lightning.pytorch.core.mixins import HyperparametersMixin
>>> class SingleArgModel(HyperparametersMixin):
...     def __init__(self, params):
...         super().__init__()
...         # manually assign single argument
...         self.save_hyperparameters(params)
...     def forward(self, *args, **kwargs):
...         ...
>>> model = SingleArgModel(Namespace(p1=1, p2='abc', p3=3.14))
>>> model.hparams
"p1": 1
"p2": abc
"p3": 3.14

>>> from lightning.pytorch.core.mixins import HyperparametersMixin
>>> class ManuallyArgsModel(HyperparametersMixin):
...     def __init__(self, arg1, arg2, arg3):
...         super().__init__()
...         # pass argument(s) to ignore as a string or in a list
...         self.save_hyperparameters(ignore='arg2')
...     def forward(self, *args, **kwargs):
...         ...
>>> model = ManuallyArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg3": 3.14
DataSplitter.setup(stage=None)[source]#

Split indices in train/test/val sets.

DataSplitter.state_dict()[source]#

Called when saving a checkpoint, implement to generate and save datamodule state.

Return type:

Dict[str, Any]

Returns:

A dictionary containing datamodule state.

DataSplitter.teardown(stage)[source]#

Called at the end of fit (train + validate), validate, test, or predict.

Parameters:

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

Return type:

None

DataSplitter.test_dataloader()[source]#

Create test data loader.

DataSplitter.train_dataloader()[source]#

Create train data loader.

DataSplitter.transfer_batch_to_device(batch, device, dataloader_idx)[source]#

Override this hook if your DataLoader returns tensors wrapped in a custom data structure.

The data types listed below (and any arbitrary nesting of them) are supported out of the box:

  • torch.Tensor or anything that implements .to(…)

  • list

  • dict

  • tuple

For anything else, you need to define how the data is moved to the target device (CPU, GPU, TPU, …).

Note

This hook should only transfer the data and not modify it, nor should it move the data to any other device than the one passed in as argument (unless you know what you are doing). To check the current state of execution of this hook you can use self.trainer.training/testing/validating/predicting so that you can add different logic as per your requirement.

Parameters:

  • batch (Any) – A batch of data that needs to be transferred to a new device.

  • device (device) – The target device as defined in PyTorch.

  • dataloader_idx (int) – The index of the dataloader to which the batch belongs.

Return type:

Any

Returns:

A reference to the data on the new device.

Example:

def transfer_batch_to_device(self, batch, device, dataloader_idx):
    if isinstance(batch, CustomBatch):
        # move all tensors in your custom data structure to the device
        batch.samples = batch.samples.to(device)
        batch.targets = batch.targets.to(device)
    elif dataloader_idx == 0:
        # skip device transfer for the first dataloader or anything you wish
        pass
    else:
        batch = super().transfer_batch_to_device(batch, device, dataloader_idx)
    return batch

See also

  • move_data_to_device()

  • apply_to_collection()

DataSplitter.val_dataloader()[source]#

Create validation data loader.

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