https://github.com/arogozhnikov/readable_capsnet

Blazingly fast capsule networks in 75 lines of pytorch+einops
https://github.com/arogozhnikov/readable_capsnet

Last synced: about 2 months ago
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Blazingly fast capsule networks in 75 lines of pytorch+einops

• Host: GitHub
• URL: https://github.com/arogozhnikov/readable_capsnet
• Owner: arogozhnikov
• License: mit
• Created: 2020-09-12T02:17:37.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2021-09-23T15:12:00.000Z (about 3 years ago)
• Last Synced: 2024-06-08T23:34:58.689Z (4 months ago)
• Language: Python
• Homepage:
• Size: 33.2 KB
• Stars: 25
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Readable Capsule Networks

Capsule network in < 80 lines.

Research-friendly implementation of capsule networks from [Dynamic Routing Between Capsules](https://papers.nips.cc/paper/6975-dynamic-routing-between-capsules.pdf)

in pytorch.

### What are capsules?

Capsules are groups of neurons each describing an entity with positional characteristics. 

Capsules are meant to form a 'soft parsing tree' for a scene with strength of confidence encoded by norm of capsule's activations.

### Intuition behind CapsNets







Video of lecture: 

## What is different about this implementation

- completely readable and very compact

- capsule layers are perfectly stackable

- auto inference of number of capsules after convolutional stem

- **memory efficiency:** 


  almost 3x less GPU memory foorprint compared to other [implementation](https://github.com/cedrickchee/capsule-net-pytorch) (2Gb vs 5.9Gb for batch size of 256)

- **blazingly fast:** 
 

  15 sec/epoch vs 747 sec/epoch on single V100 vs other [implementation](https://github.com/cedrickchee/capsule-net-pytorch)

and, well, I didn't even use `torch.jit.trace` and did not use `fp16`, which will provide an additional boost in efficiency.

Two most important changes that made this possible are:

- all convolutional capsules are packed into one fat convolution instead of splitting into several convolutions and then reshaping and concatenating, 

  `einops` takes care of making that efficiently. Additionally `einops` resolves weight management for capsules.

- ugly routing implementation made efficient - all split/concat and all unnecessary repeats are eliminated by proper usage of `einsum` and `einops`

## Project origins:

There is a ton of implementations for CapsNets. 

Most of them are hardly readable, almost all are inefficient and a large fraction is simply wrong.

All of them require to make some computations specific for each dataset and disallow easy tweaking of parts.   

@michaelklachko [suggested](https://github.com/arogozhnikov/einops/issues/53)

to rewrite 

his [implementation](https://github.com/michaelklachko/CapsNet/blob/master/capsnet_cifar.py#L68-L91) 

of routing algorithm to pytorch using ein-notation.

This turned out to be a very nice exercise. 

Additionally to improvements in routing done by Michael, 

I've minified all tricky places in encoder/decoder with `einops` layers.