Unofficial JAX implementations of Deep Learning models

Related tags

Deep Learningmachine-learningtransformersartificial-intelligenceconvolutional-neural-networksflaxjaxresearch-paper-implementation
Overview

JAX Models

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Table of Contents
  1. About The Project
  2. Getting Started
  3. Contributing
  4. License
  5. Contact

About The Project

The JAX Models repository aims to provide open sourced JAX/Flax implementations for research papers originally without code or code written with frameworks other than JAX. The goal of this project is to make a collection of models, layers, activations and other utilities that are most commonly used for research. All papers and derived or translated code is cited in either the README or the docstrings. If you think that any citation is missed then please raise an issue.

All implementations provided here are available on Papers With Code.


Available model implementations for JAX are:
  1. MetaFormer is Actually What You Need for Vision (Weihao Yu et al., 2021)
  2. Augmenting Convolutional networks with attention-based aggregation (Hugo Touvron et al., 2021)
  3. MPViT : Multi-Path Vision Transformer for Dense Prediction (Youngwan Lee et al., 2021)
  4. MLP-Mixer: An all-MLP Architecture for Vision (Ilya Tolstikhin et al., 2021)
  5. Patches Are All You Need (Anonymous et al., 2021)
  6. SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers (Enze Xie et al., 2021)
  7. A ConvNet for the 2020s (Zhuang Liu et al., 2021)
  8. Masked Autoencoders Are Scalable Vision Learners (Kaiming He et al., 2021)

Available layers for out-of-the-box integration:
  1. DropPath (Stochastic Depth) (Gao Huang et al., 2021)
  2. Squeeze-and-Excitation Layer (Jie Hu et al. 2019)
  3. Depthwise Convolution (François Chollet, 2017)

Prerequisites

Prerequisites can be installed separately through the requirements.txt file in the main directory using:

pip install -r requirements.txt

The use of a virtual environment is highly recommended to avoid version incompatibilites.

Installation

This project is built with Python 3 for the latest JAX/Flax versions and can be directly installed via pip.

pip install jax-models

If you wish to use the latest version then you can directly clone the repository too.

git clone https://github.com/DarshanDeshpande/jax-models.git

Usage

To see all model architectures available:

from jax_models.models.model_registry import list_models
from pprint import pprint

pprint(list_models())

To load your desired model:

from jax_models.models.model_registry import load_model
load_model('mpvit-base', attach_head=True, num_classes=1000, dropout=0.1)

Contributing

Please raise an issue if any implementation gives incorrect results, crashes unexpectedly during training/inference or if any citation is missing.

You can contribute to jax_models by supporting me with compute resources or by contributing your own resources to provide pretrained weights.

If you wish to donate to this inititative then please drop me a mail here.

License

Distributed under the Apache 2.0 License. See LICENSE for more information.

Contact

Feel free to reach out for any issues or requests related to these implementations

Darshan Deshpande - Email | Twitter | LinkedIn

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Comments
  • Missing Axis Swap in ExtractPatches and MergePatches

    Missing Axis Swap in ExtractPatches and MergePatches

    In patch_utils.py, the modules ExtractPatches and MergePatches are missing an axis swap between the reshapes, resulting in the extracted patches becoming horizontal stripes. For example, if we follow the code in ExtractPatches:

    >>> inputs = jnp.arange(16).reshape(1, 4, 4, 1)
>>> inputs[0, :, :, 0]

DeviceArray([[ 0,  1,  2,  3],
             [ 4,  5,  6,  7],
             [ 8,  9, 10, 11],
             [12, 13, 14, 15]], dtype=int32)

>>> patch_size = 2
>>> batch, height, width, channels = inputs.shape
>>> height, width = height // patch_size, width // patch_size
>>> x = jnp.reshape(inputs, (batch, height, patch_size, width, patch_size, channels))
>>> x = jnp.reshape(x, (batch, height * width, patch_size ** 2 * channels))
>>> x[0, 0, :]

DeviceArray([0, 1, 2, 3], dtype=int32)

    We see that the first patch extracted is not the patch containing [0, 1, 4, 5], but the horizontal stripe [0, 1, 2, 3]. To fix this problem, we should add an axis swap. For ExtractPatches, this should be:

    batch, height, width, channels = inputs.shape
height, width = height // patch_size, width // patch_size
x = jnp.reshape(
    inputs, (batch, height, patch_size, width, patch_size, channels)
)
x = jnp.swapaxes(x, 2, 3)
x = jnp.reshape(x, (batch, height * width, patch_size ** 2 * channels))

    For MergePatches, this should be:

    batch, length, _ = inputs.shape
height = width = int(length**0.5)
x = jnp.reshape(inputs, (batch, height, width, patch_size, patch_size, -1))
x = jnp.swapaxes(x, 2, 3)
x = jnp.reshape(x, (batch, height * patch_size, width * patch_size, -1))
    bug 
    opened by young-geng 4
  • fix convnext to make it work with jax.jit

    fix convnext to make it work with jax.jit

    Hey, first of all, thanks for the nice codebase. When doing inference using the convnext model, I noticed the following issue:

    Calling x.item() will call float(x), which breaks the jit tracer. We can remove the list comprehension in unnecessary conversion to make jax.jit work. Without jax.jit, the model is very slow for me, running with only ~30% GPU utilization (RTX 3090).

    This issue could apply to other models as well, maybe it is a good idea to include a test for applying jax.jit to each model?

    opened by maxidl 1
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