My implementation of transformers related papers for computer vision in pytorch

Related tags

Deep Learningpytorch-vision-transformers
Overview

vision_transformers

This is my personnal repo to implement new transofrmers based and other computer vision DL models

I am currenlty working without a lot of GPU ressources therefore I mainly trained models on CIFAR 10. But my implementation are build to be fast and effective at scale.

Current paper implemented:

  • An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, from Dosovitskiy et al (2020)
  • Patch Are All You Need ? anonymous

Baseline:

  • Deep Residual Learning for Image Recognition, from He et al (2015)

Models are implemented in pure pytorch and trained via pytorchlightning. Dependencies are managed by poetry. It is included an Dockerfile to create a cuda ready container with jupyter lab inside. On the development part, I use jupytext in order to avoid commit every metadata change on the notebook. Fully tested with pytest and formatted with black and isort.

If you want to create a project with similar config, just use my boilerplat.

How to use it ?

first install the dependecies:

poetry install

Then, only for development:

add the precommit hook

poetry run pre-commit install

sync the notebook (only once)

poetry shell
make notebook-sync

launch a jupyter lab session

poetry run jupyter lab

Use tensorboard

poetry shell
make tensorboard

Format the code without the precommit hook

poetry shell
make formatting

Tests:

to run the tests:

poetry shell
make tests
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