https://github.com/lucidrains/triangle-multiplicative-module
Implementation of the Triangle Multiplicative module, used in Alphafold2 as an efficient way to mix rows or columns of a 2d feature map, as a standalone package for Pytorch
https://github.com/lucidrains/triangle-multiplicative-module
artificial-intelligence deep-learning protein-folding
Last synced: 3 months ago
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Implementation of the Triangle Multiplicative module, used in Alphafold2 as an efficient way to mix rows or columns of a 2d feature map, as a standalone package for Pytorch
- Host: GitHub
- URL: https://github.com/lucidrains/triangle-multiplicative-module
- Owner: lucidrains
- License: mit
- Created: 2021-07-19T17:28:12.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-08-03T05:18:08.000Z (about 4 years ago)
- Last Synced: 2025-06-03T15:11:47.946Z (4 months ago)
- Topics: artificial-intelligence, deep-learning, protein-folding
- Language: Python
- Homepage:
- Size: 51.8 KB
- Stars: 30
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Triangle Multiplicative Module - Pytorch
Implementation of the Triangle Multiplicative module, used in Alphafold2 as an efficient way to mix rows or columns of a 2d feature map, as a standalone package for Pytorch
## Install
```bash
$ pip install triangle-multiplicative-module
```
## Usage
```python
import torch
from triangle_multiplicative_module import TriangleMultiplicativeModule
model = TriangleMultiplicativeModule(
dim = 64, # feature map dimension
hidden_dim = 128, # intermediate dimension size
mix = 'outgoing' # either 'ingoing' or 'outgoing'
)
fmap = torch.randn(1, 256, 256, 64)
mask = torch.ones(1, 256, 256).bool()
model(fmap, mask = mask) # (1, 256, 256, 64)
```
## Citations
```bibtex
@Article{AlphaFold2021,
author = {Jumper, John and Evans, Richard and Pritzel, Alexander and Green, Tim and Figurnov, Michael and Ronneberger, Olaf and Tunyasuvunakool, Kathryn and Bates, Russ and {\v{Z}}{\'\i}dek, Augustin and Potapenko, Anna and Bridgland, Alex and Meyer, Clemens and Kohl, Simon A A and Ballard, Andrew J and Cowie, Andrew and Romera-Paredes, Bernardino and Nikolov, Stanislav and Jain, Rishub and Adler, Jonas and Back, Trevor and Petersen, Stig and Reiman, David and Clancy, Ellen and Zielinski, Michal and Steinegger, Martin and Pacholska, Michalina and Berghammer, Tamas and Bodenstein, Sebastian and Silver, David and Vinyals, Oriol and Senior, Andrew W and Kavukcuoglu, Koray and Kohli, Pushmeet and Hassabis, Demis},
journal = {Nature},
title = {Highly accurate protein structure prediction with {AlphaFold}},
year = {2021},
doi = {10.1038/s41586-021-03819-2},
note = {(Accelerated article preview)},
}
```