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

Related tags

Deep Learningdeep-learningartificial-intelligenceprotein-folding
Overview

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

$ pip install triangle-multiplicative-module

Usage

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

@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)},
}
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Comments
  • Definition about 'ingoing' and 'outgoing'

    Definition about 'ingoing' and 'outgoing'

    Hi Luci!

    I've seen the lines define the 'ingoing' and 'outgoing': if mix == 'ingoing': self.mix_einsum_eq = '... i k d, ... j k d -> ... i j d' elif mix == 'outgoing': self.mix_einsum_eq = '... k j d, ... k i d -> ... i j d'

    but based on the original paper, I found maybe should be the inverse case, that: if mix == 'ingoing': self.mix_einsum_eq = '... k i d, ... k j d -> ... i j d' elif mix == 'outgoing': self.mix_einsum_eq = '... j k d, ... i k d -> ... i j d'

    based on what I understand, 'ingoing' should be the case that information comes from other edges (like 'ki','kj') aggregate togther and bind with edge 'ij'.

    Thanks for your contribution!

    opened by CiaoHe 1
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