Metadata-Version: 2.1
Name: pytorch-complex
Version: 0.1.1
Summary: Complex Modules for PyTorch
Home-page: https://github.com/soumickmj/pytorch-complex
Author: Soumick Chatterjee
Author-email: soumick.chatterjee@ovgu.de
License: MIT license
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

# pytorch-complex

Install it using pip: 

pip install pytorch-complex

Usage:
Similar to PyTorch.
For using the Complex features of this library, just change the regular torch imports with torchcomplex imports.
For example:
import torchcomplex.nn as nn  instead of import torch.nn as nn
Then, simply nn.Conv2d for both torch and torchcomplex, for 2D Convolution

## Credits

If you like this repository, please click on Star!

If you use this package or benift from the codes of this repo, please cite the following in your publications:

> [Soumick Chatterjee, Chompunuch Sarasaen, Alessandro Sciarra, Mario Breitkopf, Steffen Oeltze-Jafra, Andreas Nürnberger, Oliver Speck: Going beyond the image space: undersampled MRI reconstruction directly in the k-space using a complex valued residual neural network (ISMRM, May 2021)](https://www.researchgate.net/publication/349589092_Going_beyond_the_image_space_undersampled_MRI_reconstruction_directly_in_the_k-space_using_a_complex_valued_residual_neural_network)

BibTeX entry:

```bibtex
@inproceedings{mickISMRM21ksp,
      author = {Chatterjee, Soumick and Sarasaen, Chompunuch and Sciarra, Alessandro and Breitkopf, Mario and Oeltze-Jafra, Steffen and Nürnberger, Andreas and                     Speck, Oliver},
      year = {2021},
      month = {05},
      pages = {1757},
      title = {Going beyond the image space: undersampled MRI reconstruction directly in the k-space using a complex valued residual neural network},
      booktitle={2021 ISMRM \& SMRT Annual Meeting \& Exhibition}
}
```
Thank you so much for your support.


History
=======

0.1.1 (2023-06-16)
------------------

* modReLU implementation changed
* New activation functions added: Hirose and modSigmoid

0.1.0 (2022-11-06)
------------------

* Default behaviour of complex_weights has been changed to True
* Basic max and avg functional callers added
* Bug fix with the new pooling functionals

0.0.8 (2020-12-08)
------------------

* Complex Weight Initializations added (Few basics and trabelsi_standard, trabelsi_independent) 

0.0.1 (2020-11-06)
------------------

* First release (of the package) on PyPI. 
* torchcomplex.nn -> Convolutions, Linears, Dropout, Pooling, BatchNorm, Few Actications
* Untested first version, could be buggy

0.0.0 (2020-11-06)
------------------

* First release (basic structure, not the actual package code) on PyPI.
