Data core

Core functionality for gathering data

The classes here provide functionality for applying a list of transforms to a set of items (TfmdLists, Datasets) or a DataLoader (TfmdDl) as well as the base class used to gather the data for model training: DataLoaders.

TfmdDL -

show_batch is a type-dispatched function that is responsible for showing decoded samples. x and y are the input and the target in the batch to be shown, and are passed along to dispatch on their types. There is a different implementation of show_batch if x is a TensorImage or a TensorText for instance (see vision.core or text.data for more details). ctxs can be passed but the function is responsible to create them if necessary. kwargs depend on the specific implementation.

show_results is a type-dispatched function that is responsible for showing decoded samples and their corresponding outs. Like in show_batch, x and y are the input and the target in the batch to be shown, and are passed along to dispatch on their types. ctxs can be passed but the function is responsible to create them if necessary. kwargs depend on the specific implementation.

A TfmdDL is a DataLoader that creates Pipeline from a list of Transforms for the callbacks after_item, before_batch and after_batch. As a result, it can decode or show a processed batch.

Methods

DataLoaders -

Multiple transforms can by added to multiple dataloaders using Dataloaders.add_tfms. You can specify the dataloaders by list of names dls.add_tfms(...,'valid',...) or by index dls.add_tfms(...,1,....), by default transforms are added to all dataloaders. event is a required argument and determined when the transform will be run, for more information on events please refer to TfmdDL. tfms is a list of Transform, and is a required argument.

Methods

TfmdLists -

A TfmdLists combines a collection of object with a Pipeline. tfms can either be a Pipeline or a list of transforms, in which case, it will wrap them in a Pipeline. use_list is passed along to L with the items and split_idx are passed to each transform of the Pipeline. do_setup indicates if the Pipeline.setup method should be called during initialization.

Here's how we can use TfmdLists.setup to implement a simple category list, getting labels from a mock file list:

Datasets -

A Datasets creates a tuple from items (typically input,target) by applying to them each list of Transform (or Pipeline) in tfms. Note that if tfms contains only one list of tfms, the items given by Datasets will be tuples of one element.

n_inp is the number of elements in the tuples that should be considered part of the input and will default to 1 if tfms consists of one set of transforms, len(tfms)-1 otherwise. In most cases, the number of elements in the tuples spit out by Datasets will be 2 (for input,target) but it can happen that there is 3 (Siamese networks or tabular data) in which case we need to be able to determine when the inputs end and the targets begin.

Methods

Used to create dataloaders. You may prepend 'val_' as in val_shuffle to override functionality for the validation set. dl_kwargs gives finer per dataloader control if you need to work with more than one dataloader.

Add test set for inference

Export -