Metadata-Version: 2.1
Name: fastpapers
Version: 0.0.6
Summary: Fastai implementation of papers.
Home-page: https://github.com/ababino/fastpapers/tree/master/
Author: Andres Babino
Author-email: ababino@gmail.com
License: Apache Software License 2.0
Description: # Welcome to fastpapers
        > Play LEGO with papers.
        
        
        `fastpapers` is a python library where I reproduce papers on [Jupyter Notebooks](https://jupyter.org/). I use [nbdev](https://nbdev.fast.ai/) to turn these notebooks into modules. The implementations are done using [fastai](https://docs.fast.ai/). This a personal project that I use to learn and it is not a tested library.
        
        ## Install
        
        `pip install fastpapers`
        
        ## How to use
        
        The name of each module is the [bibtexkey](https://en.wikipedia.org/wiki/BibTeX#Field_types) of the corresponing paper.
        For example, if you want to use the FID metric from [Heusel, Martin, et al. 2017](http://papers.nips.cc/paper/7240-gans-trained-by-a-two-t), you can import it like so:
        
        ```
        from fastpapers.heusel2017gans import FIDMetric
        ```
        
        If you want to train a pix2pix model from Isola, [Phillip, et al](https://openaccess.thecvf.com/content_cvpr_2017/papers/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.pdf) 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();
        ```
        
        
        ![png](docs/images/output_10_0.png)
        
        
        Or useful functions for debuging like `explode_shapes` or `explode_ranges`
        
        ```
        explode_shapes(it)
        ```
        
        
        
        
            [(3, 224, 224), (3, 224, 224), (3, 224, 224)]
        
        
        
Keywords: fastai research ml vision nbdev
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
