DataBlock and the major 4 lines of code of FastAI

[Major 4 lines of code]

path = untar_data(URLs.PETS)

dls = ImageDataLoaders.from_name_func(path, get_image_files(path/"images"),
label_func, item_tfms=Resize(224))

learn = cnn_learner(dls, resnet34, metrics=error_rate)

learn.fine_tune(1)

[DataBlock]

The way we usually build the data block in one go is by answering a list of questions:

• what is the types of your inputs/targets? Here images and categories
• where is your data? Here in filenames in subfolders
• does something need to be applied to inputs? Here no
• does something need to be applied to the target? Here the label_func function
• how to split the data? Here randomly
• do we need to apply something on formed items? Here a resize
• do we need to apply something on formed batches? Here no

This gives us this design:

dblock = DataBlock(blocks    = (ImageBlock, CategoryBlock),
                   get_items = get_image_files,
                   get_y     = label_func,
                   splitter  = RandomSplitter(),
                   item_tfms = Resize(224))

For two questions that got a no, the corresponding arguments we would pass if the anwser was different would be get_x and batch_tfms.