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Description:

glomtf 0.1.1

GLOM TensorFlow

This Python package attempts to implement GLOM in TensorFlow, which allows advances made by several different groups
transformers, neural fields, contrastive representation learning, distillation and capsules to be combined. This was
suggested by Geoffrey Hinton in his paper
"How to represent part-whole hierarchies in a neural network".
Further, Yannic Kilcher's video and Phil Wang's repo
was very helpful for me to implement this project.
Installation
Run the following to install:
pip install glom-tf

Developing glom-tf
To install glom-tf, along with tools you need to develop and test, run the following in your virtualenv:
git clone https://github.com/Rishit-dagli/GLOM-TensorFlow.git
# or clone your own fork

cd GLOM-TensorFlow
pip install -e .[dev]

A bit about GLOM
The GLOM architecture is composed of a large number of columns which
all use exactly the same weights. Each column is a stack of spatially local
autoencoders that learn multiple levels of representation for what is happening
in a small image patch. Each autoencoder transforms the embedding at one level
into the embedding at an adjacent level using a multilayer bottom-up encoder
and a multilayer top-down decoder. These levels correspond to the levels in a
part-whole hierarchy.

Interactions among the 3 levels in one column

An example shared by the author was as an example when show a face image, a single column might converge on embedding
vectors representing a nostril, a nose, a face, and a person.
At each discrete time and in each column separately, the embedding at a
level is updated to be the weighted average of:

bottom-up neural net acting on the embedding at the level below at the previous time
top-down neural net acting on the embedding at the level above at the previous time
embedding vector at the previous time step
attention-weighted average of the embeddings at the same level in nearby columns at the previous time

For a static image, the embeddings at a level should settle down over time to produce similar vectors.

A picture of the embeddings at a particular time

Usage
from glomtf import Glom

model = Glom(dim = 512,
levels = 5,
image_size = 224,
patch_size = 14)

img = tf.random.normal([1, 3, 224, 224])
levels = model(img, iters = 12) # (1, 256, 5, 12)
# 1 - batch
# 256 - patches
# 5 - levels
# 12 - dimensions

Use the return_all = True argument to get all the column and level states per iteration. This also gives you access
to all the level data across iterations for clustering, from which you can inspect the islands too.
from glomtf import Glom

model = Glom(dim = 512,
levels = 5,
image_size = 224,
patch_size = 14)

img = tf.random.normal([1, 3, 224, 224])
all_levels = model(img, iters = 12, return_all = True) # (13, 1, 256, 5, 12)
# 13 - time

# top level outputs after iteration 6
top_level_output = all_levels[7, :, :, -1] # (1, 256, 512)
# 1 - batch
# 256 - patches
# 512 - dimensions

Citations
@misc{hinton2021represent,
title = {How to represent part-whole hierarchies in a neural network},
author = {Geoffrey Hinton},
year = {2021},
eprint = {2102.12627},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}