Metadata-Version: 2.1
Name: deep-learning-utils
Version: 0.0.1rc2
Summary: UNKNOWN
Home-page: UNKNOWN
Author: Justus Schock
Maintainer: Justus Schock
Maintainer-email: justus.schock@rwth-aachen.de
License: MIT
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: torch
Requires-Dist: rising
Requires-Dist: tqdm
Requires-Dist: tensorboard
Requires-Dist: hydra-core
Requires-Dist: omegaconf
Requires-Dist: scipy

# dl-utils: Utilities for Deep Learning with PyTorch

## Content
This package contains mainly loss functions, model definitions and metrics in both functional and modular and (whenever possible) pure PyTorch implementations.


## Subpackages
Currently there are the following subpackages:

* `dlutils.data`: contains data utilities (so far just a dataset for random fake data)
* `dlutils.losses`: extends the losses given in PyTorch itself by a few more loss functions
* `dlutils.metrics`: implements some common metrics
* `dlutils.models`: contains Nd implementations of many popular models
    - `dlutils.models.gans`: contains many basic gan implementations, but so far not for arbitrary dimensions
* `dlutils.optims`: containis additional optimizers
* `dlutils.utils`: contains additional utilities such as tensor operations and module loading

## Note
* Most of this code was only tested sparely and not with a proper CI/CD and unittests. I'm currently working on that and any contributions are highly welcomed.

* All implementations are done for pure PyTorch. You can employ them in whatever training framework you want (like [pytorch/ignite]{https://github.com/pytorch/ignite) or [Pytorch-Lightning](https://github.com/PyTorchLightning/pytorch-lightning)) or in your custom training loops


