Welcome to fastpapers

Play LEGO with papers.

#all_slow

fastpapers is a python library where I use fastai to reproduce papers on Jupyter Notebooks. I use nbdev to turn these notebooks into modules.

Install

pip install fastpapers

How to use

Train DETR in 3 lines of code

Download the data

path = download_coco(force_download=False)

Create the DataLoaders, the Learner, and fit.

dls = CocoDataLoaders.from_sources(path, vocab=coco_vocab, num_workers=0)
learnd = detr_learner(dls)
learnd.fit(1, lr=[1e-5, 1e-5, 1e-5])

Show the results

with learnd.removed_cbs(learnd.coco_eval): learnd.show_results(max_n=8, figsize=(10,10))

Superresolution in 4 lines of code

Download the data

path = untar_data(URLs.IMAGENETTE)

Create the DataLoaders, the Learner adn fit.

#hide_output
db = DataBlock(blocks=(ResImageBlock(72), ResImageBlock(288)),
               get_items=get_image_files,
               batch_tfms=Normalize.from_stats([0.5]*3, [0.5]*3))
dls = db.dataloaders(path, bs=4, num_workers=4)
learn = superres_learner(dls)
learn.fit(16, lr=1e-3, wd=0)

learn.show_results()

Library structure

The name of each module is the bibtexkey of the corresponing paper. For example, if you want to use the FID metric from Heusel, Martin, et al. 2017, you can import it like so:

from fastpapers.heusel2017gans import FIDMetric

If you want to train a pix2pix model from Isola, Phillip, et al you can import a pix2pix_learner

from fastpapers.isola2017image import pix2pix_learner

The core module contains functions and classes that are useful for several papers. For example, you have a ImageNTuple to work with an arbitrary amount of images as input.

path = untar_data(URLs.PETS)
files = get_image_files(path/"images")

it = ImageNTuple.create((files[0], files[1], files[2]))
it = Resize(224)(it)
it = ToTensor()(it)
it.show();

Or useful functions for debuging like explode_shapes or explode_ranges

explode_shapes(it)

[(3, 224, 224), (3, 224, 224), (3, 224, 224)]