https://github.com/justusschock/dl-utils
Utilities for Deep Learning with PyTorch (models, losses, metrics etc.)
https://github.com/justusschock/dl-utils
cnn deep-learning deep-neural-networks loss-functions metrics models neural-network python pytorch
Last synced: 7 days ago
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Utilities for Deep Learning with PyTorch (models, losses, metrics etc.)
- Host: GitHub
- URL: https://github.com/justusschock/dl-utils
- Owner: justusschock
- License: mit
- Created: 2020-03-23T07:53:41.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-07-20T07:51:19.000Z (almost 5 years ago)
- Last Synced: 2025-03-31T04:41:31.652Z (about 1 month ago)
- Topics: cnn, deep-learning, deep-neural-networks, loss-functions, metrics, models, neural-network, python, pytorch
- Language: Python
- Homepage:
- Size: 233 KB
- Stars: 14
- Watchers: 0
- Forks: 4
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.
## Installation
### From source
`pip install git+https://github.com/justusschock/dl-utils`
### From PyPi
`pip install deep-learning-utils`
## 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