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DataModule

class flash.core.data.data_module.DataModule(train_input=None, val_input=None, test_input=None, predict_input=None, data_fetcher=None, val_split=None, batch_size=None, num_workers=0, sampler=None, pin_memory=True, persistent_workers=True, output_transform=None)[source]

A basic DataModule class for all Flash tasks. This class includes references to a Input and a BaseDataFetcher.

Parameters
  • train_input (Optional[Input]) – Input dataset for training. Defaults to None.

  • val_input (Optional[Input]) – Input dataset for validating model performance during training. Defaults to None.

  • test_input (Optional[Input]) – Input dataset to test model performance. Defaults to None.

  • predict_input (Optional[Input]) – Input dataset for predicting. Defaults to None.

  • data_fetcher (Optional[BaseDataFetcher]) – The BaseDataFetcher to attach to the InputTransform. If None, the output from configure_data_fetcher() will be used.

  • val_split (Optional[float]) – An optional float which gives the relative amount of the training dataset to use for the validation dataset.

  • batch_size (Optional[int]) – The batch size to be used by the DataLoader. Defaults to 1.

  • num_workers (int) – The number of workers to use for parallelized loading. Defaults to None which equals the number of available CPU threads, or 0 for Windows or Darwin platform.

  • sampler (Optional[Type[Sampler]]) – A sampler following the Sampler type. Will be passed to the DataLoader for the training dataset. Defaults to None.

static configure_data_fetcher(*args, **kwargs)[source]

This function is used to configure a BaseDataFetcher.

Override with your custom one.

Return type

BaseDataFetcher

property data_fetcher: flash.core.data.callback.BaseDataFetcher

This property returns the data fetcher.

Return type

BaseDataFetcher

property data_pipeline: flash.core.data.data_pipeline.DataPipeline

Property that returns the full data pipeline including the data source, input transform and postprocessing.

Return type

DataPipeline

property input_transform: flash.core.data.io.input_transform.InputTransform

Property that returns the input transform class used on input data.

Return type

InputTransform

input_transform_cls

alias of flash.core.data.io.input_transform.InputTransform

property inputs: Optional[Union[flash.core.data.io.input.Input, List[flash.core.data.io.input.InputBase]]]

Property that returns the inputs associated with this DataModule.

Return type

Union[Input, List[InputBase], None]

property labels: Optional[int]

Property that returns the labels if this DataModule contains classification data.

Return type

Optional[int]

property multi_label: Optional[bool]

Property that returns True if this DataModule contains multi-label data.

Return type

Optional[bool]

property num_classes: Optional[int]

Property that returns the number of classes of the datamodule if a multiclass task.

Return type

Optional[int]

property output_transform: flash.core.data.io.output_transform.OutputTransform

Property that returns the OutputTransform used to output_transform the model outputs.

Return type

OutputTransform

output_transform_cls

alias of flash.core.data.io.output_transform.OutputTransform

property predict_dataset: Optional[flash.core.data.io.input.Input]

This property returns the predict dataset.

Return type

Optional[Input]

show_predict_batch(hooks_names='load_sample', reset=True)[source]

This function is used to visualize a batch from the predict dataloader.

Return type

None

show_test_batch(hooks_names='load_sample', reset=True)[source]

This function is used to visualize a batch from the test dataloader.

Return type

None

show_train_batch(hooks_names='load_sample', reset=True)[source]

This function is used to visualize a batch from the train dataloader.

Return type

None

show_val_batch(hooks_names='load_sample', reset=True)[source]

This function is used to visualize a batch from the validation dataloader.

Return type

None

property test_dataset: Optional[flash.core.data.io.input.Input]

This property returns the test dataset.

Return type

Optional[Input]

property train_dataset: Optional[flash.core.data.io.input.Input]

This property returns the train dataset.

Return type

Optional[Input]

property val_dataset: Optional[flash.core.data.io.input.Input]

This property returns the validation dataset.

Return type

Optional[Input]

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