Data block

High level API to quickly get your data in a DataLoaders

📘 Note: Several domain-specific blocks such as ImageBlock, BBoxBlock, PointBlock, and CategoryBlock are implemented on top of TransformBlock. These blocks are designed to handle common tasks in computer vision, classification, and regression. See the Vision Blocks section for more details.

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TransformBlock

def TransformBlock(
    type_tfms:list=None, # One or more `Transform`s
    item_tfms:list=None, # `ItemTransform`s, applied on an item
    batch_tfms:list=None, # `Transform`s or `RandTransform`s, applied by batch
    dl_type:TfmdDL=None, # Task specific `TfmdDL`, defaults to `TfmdDL`
    dls_kwargs:dict=None, # Additional arguments to be passed to `DataLoaders`
):

A basic wrapper that links defaults transforms for the data block API

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CategoryBlock

def CategoryBlock(
    vocab:collections.abc.MutableSequence | pandas.Series=None, # List of unique class names
    sort:bool=True, # Sort the classes alphabetically
    add_na:bool=False, # Add `#na#` to `vocab`
):

TransformBlock for single-label categorical targets

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MultiCategoryBlock

def MultiCategoryBlock(
    encoded:bool=False, # Whether the data comes in one-hot encoded
    vocab:collections.abc.MutableSequence | pandas.Series=None, # List of unique class names
    add_na:bool=False, # Add `#na#` to `vocab`
):

TransformBlock for multi-label categorical targets

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RegressionBlock

def RegressionBlock(
    n_out:int=None, # Number of output values
):

TransformBlock for float targets

General API

#For example, so not exported
from fastai.vision.core import *
from fastai.vision.data import *

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DataBlock

def DataBlock(
    blocks:list=None, # One or more `TransformBlock`s
    dl_type:TfmdDL=None, # Task specific `TfmdDL`, defaults to `block`'s dl_type or`TfmdDL`
    getters:list=None, # Getter functions applied to results of `get_items`
    n_inp:int=None, # Number of inputs
    item_tfms:list=None, # `ItemTransform`s, applied on an item
    batch_tfms:list=None, # `Transform`s or `RandTransform`s, applied by batch
    get_items:NoneType=None, splitter:NoneType=None, get_y:NoneType=None, get_x:NoneType=None
):

Generic container to quickly build Datasets and DataLoaders.

To build a DataBlock you need to give the library four things: the types of your input/labels, and at least two functions: get_items and splitter. You may also need to include get_x and get_y or a more generic list of getters that are applied to the results of get_items.

splitter is a callable which, when called with items, returns a tuple of iterables representing the indices of the training and validation data.

Once those are provided, you automatically get a Datasets or a DataLoaders:

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DataBlock.datasets

def datasets(
    source, # The data source
    verbose:bool=False, # Show verbose messages
)->Datasets:

Create a Datasets object from source

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DataBlock.dataloaders

def dataloaders(
    source, # The data source
    path:str='.', # Data source and default `Learner` path
    verbose:bool=False, # Show verbose messages
    bs:int=64, # Size of batch
    shuffle:bool=False, # Whether to shuffle data
    num_workers:int=None, # Number of CPU cores to use in parallel (default: All available up to 16)
    do_setup:bool=True, # Whether to run `setup()` for batch transform(s)
    pin_memory:bool=False, timeout:int=0, batch_size:NoneType=None, drop_last:bool=False, indexed:NoneType=None,
    n:NoneType=None, device:NoneType=None, persistent_workers:bool=False, pin_memory_device:str='',
    wif:NoneType=None, before_iter:NoneType=None, after_item:NoneType=None, before_batch:NoneType=None,
    after_batch:NoneType=None, after_iter:NoneType=None, create_batches:NoneType=None, create_item:NoneType=None,
    create_batch:NoneType=None, retain:NoneType=None, get_idxs:NoneType=None, sample:NoneType=None,
    shuffle_fn:NoneType=None, do_batch:NoneType=None
)->DataLoaders:

Create a DataLoaders object from source

You can create a DataBlock by passing functions:

mnist = DataBlock(blocks = (ImageBlock(cls=PILImageBW),CategoryBlock),
                  get_items = get_image_files,
                  splitter = GrandparentSplitter(),
                  get_y = parent_label)

Each type comes with default transforms that will be applied:

• at the base level to create items in a tuple (usually input,target) from the base elements (like filenames)
• at the item level of the datasets
• at the batch level

They are called respectively type transforms, item transforms, batch transforms. In the case of MNIST, the type transforms are the method to create a PILImageBW (for the input) and the Categorize transform (for the target), the item transform is ToTensor and the batch transforms are Cuda and IntToFloatTensor. You can add any other transforms by passing them in DataBlock.datasets or DataBlock.dataloaders.

test_eq(mnist.type_tfms[0], [PILImageBW.create])
test_eq(mnist.type_tfms[1].map(type), [Categorize])
test_eq(mnist.default_item_tfms.map(type), [ToTensor])
test_eq(mnist.default_batch_tfms.map(type), [IntToFloatTensor])

dsets = mnist.datasets(untar_data(URLs.MNIST_TINY))
test_eq(dsets.vocab, ['3', '7'])
x,y = dsets.train[0]
test_eq(x.size,(28,28))
show_at(dsets.train, 0, cmap='Greys', figsize=(2,2));

with expect_fail(): DataBlock(wrong_kwarg=42, wrong_kwarg2='foo')

We can pass any number of blocks to DataBlock, we can then define what are the input and target blocks by changing n_inp. For example, defining n_inp=2 will consider the first two blocks passed as inputs and the others as targets.

mnist = DataBlock((ImageBlock, ImageBlock, CategoryBlock), get_items=get_image_files, splitter=GrandparentSplitter(),
                   get_y=parent_label)
dsets = mnist.datasets(untar_data(URLs.MNIST_TINY))
test_eq(mnist.n_inp, 2)
test_eq(len(dsets.train[0]), 3)

with expect_fail(msg='get_y contains 2 functions, but must contain 1 (one for each output)'): DataBlock((ImageBlock, ImageBlock, CategoryBlock), get_items=get_image_files, splitter=GrandparentSplitter(),
                  get_y=[parent_label, noop],
                  n_inp=2)

mnist = DataBlock((ImageBlock, ImageBlock, CategoryBlock), get_items=get_image_files, splitter=GrandparentSplitter(),
                  n_inp=1,
                  get_y=[noop, Pipeline([noop, parent_label])])
dsets = mnist.datasets(untar_data(URLs.MNIST_TINY))
test_eq(len(dsets.train[0]), 3)

Debugging

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DataBlock.summary

def summary(
    source, # The data source
    bs:int=4, # The batch size
    show_batch:bool=False, # Call `show_batch` after the summary
    **kwargs
):

Steps through the transform pipeline for one batch, and optionally calls show_batch(**kwargs) on the transient Dataloaders.

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DataBlock.summary

def summary(
    source, # The data source
    bs:int=4, # The batch size
    show_batch:bool=False, # Call `show_batch` after the summary
    **kwargs
):

Steps through the transform pipeline for one batch, and optionally calls show_batch(**kwargs) on the transient Dataloaders.

Besides stepping through the transformation, summary() provides a shortcut dls.show_batch(...), to see the data. E.g.

pets.summary(path/"images", bs=8, show_batch=True, unique=True,...)

is a shortcut to:

pets.summary(path/"images", bs=8)
dls = pets.dataloaders(path/"images", bs=8)
dls.show_batch(unique=True,...)  # See different tfms effect on the same image.