Unofficial JAX implementations of Deep Learning models

Related tags

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

JAX Models

license-shield release-shield python-shield code-style

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

You might also like...
Very deep VAEs in JAX/Flax
Very deep VAEs in JAX/Flax

Very Deep VAEs in JAX/Flax Implementation of the experiments in the paper Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on I

Jamie Townsend 42 Dec 12, 2022
Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX
Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX

CQL-JAX This repository implements Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX (FLAX). Implementation is built on

Karush Suri 8 Nov 7, 2022
PyTorch implementations of neural network models for keyword spotting
PyTorch implementations of neural network models for keyword spotting

Honk: CNNs for Keyword Spotting Honk is a PyTorch reimplementation of Google's TensorFlow convolutional neural networks for keyword spotting, which ac

Castorini 475 Dec 15, 2022
Unofficial implementation of Proxy Anchor Loss for Deep Metric Learning
Unofficial implementation of Proxy Anchor Loss for Deep Metric Learning

Proxy Anchor Loss for Deep Metric Learning Unofficial pytorch, tensorflow and mxnet implementations of Proxy Anchor Loss for Deep Metric Learning. Not

Geonmo Gu 3 Jun 9, 2021
Time-series-deep-learning - Developing Deep learning LSTM, BiLSTM models, and NeuralProphet for multi-step time-series forecasting of stock price.
Time-series-deep-learning - Developing Deep learning LSTM, BiLSTM models, and NeuralProphet for multi-step time-series forecasting of stock price.

Stock Price Prediction Using Deep Learning Univariate Time Series Predicting stock price using historical data of a company using Neural networks for

Abdultawwab Safarji 7 Nov 27, 2022
FedJAX is a library for developing custom Federated Learning (FL) algorithms in JAX.

FedJAX: Federated learning with JAX What is FedJAX? FedJAX is a library for developing custom Federated Learning (FL) algorithms in JAX. FedJAX priori

Google 208 Dec 14, 2022
Objax Apache-2Objax (🥉19 · ⭐ 580) - Objax is a machine learning framework that provides an Object.. Apache-2 jax

Objax Tutorials | Install | Documentation | Philosophy This is not an officially supported Google product. Objax is an open source machine learning fr

Google 729 Jan 2, 2023
Plug-n-Play Reinforcement Learning in Python with OpenAI Gym and JAX
Plug-n-Play Reinforcement Learning in Python with OpenAI Gym and JAX

coax is built on top of JAX, but it doesn't have an explicit dependence on the jax python package. The reason is that your version of jaxlib will depend on your CUDA version.

null 128 Dec 27, 2022
JAX code for the paper
JAX code for the paper "Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation"

Optimal Model Design for Reinforcement Learning This repository contains JAX code for the paper Control-Oriented Model-Based Reinforcement Learning wi

Evgenii Nikishin 43 Sep 28, 2022
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
Releases(v0.5-van)
Owner
Helping Machines Learn Better 💻😃
GitHub Repository
Gym for multi-agent reinforcement learning

PettingZoo is a Python library for conducting research in multi-agent reinforcement learning, akin to a multi-agent version of Gym. Our website, with

Farama Foundation 1.6k Jan 09, 2023
CPF: Learning a Contact Potential Field to Model the Hand-object Interaction

Contact Potential Field This repo contains model, demo, and test codes of our paper: CPF: Learning a Contact Potential Field to Model the Hand-object

Lixin YANG 99 Dec 26, 2022
A CNN model to detect hand gestures.

Software Used python - programming language used, tested on v3.8 miniconda - for managing virtual environment Libraries Used opencv - pip install open

Shivanshu 6 Jul 14, 2022
HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR. CVPR 2022

HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR. CVPR 2022 [Project page | Video] Getting sta

51 Nov 29, 2022
Fantasy Points Prediction and Dream Team Formation

Fantasy-Points-Prediction-and-Dream-Team-Formation Collected Data from open source resources that have over 100 Parameters for predicting cricket play

Akarsh Singh 2 Sep 13, 2022
Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently

Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently This repository is the official implementat

VITA 4 Dec 20, 2022
Python Jupyter kernel using Poetry for reproducible notebooks

Poetry Kernel Use per-directory Poetry environments to run Jupyter kernels. No need to install a Jupyter kernel per Python virtual environment! The id

Pathbird 204 Jan 04, 2023
Fast sparse deep learning on CPUs

SPARSEDNN **If you want to use this repo, please send me an email: [email pro

Ziheng Wang 44 Nov 30, 2022
HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation Official PyTorch Implementation

: We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder. Furthermore, to allow maximal adaptivity, the w

Yuval Nirkin 182 Dec 14, 2022
OpenDILab RL Kubernetes Custom Resource and Operator Lib

DI Orchestrator DI Orchestrator is designed to manage DI (Decision Intelligence) jobs using Kubernetes Custom Resource and Operator. Prerequisites A w

OpenDILab 205 Dec 29, 2022
Unsupervised Feature Ranking via Attribute Networks.

FRANe Unsupervised Feature Ranking via Attribute Networks (FRANe) converts a dataset into a network (graph) with nodes that correspond to the features

7 Sep 29, 2022
automated systems to assist guarding corona Virus precautions for Closed Rooms (e.g. Halls, offices, etc..)

Automatic-precautionary-guard automated systems to assist guarding corona Virus precautions for Closed Rooms (e.g. Halls, offices, etc..) what is this

badra 0 Jan 06, 2022
✨风纪委员会自动投票脚本，利用Github Action帮你进行裁决操作（为了让其他风纪委员有案件可判，本程序从中午12点才开始运行，有需要请自己修改运行时间）

风纪委员会自动投票 本脚本通过使用Github Action来实现B站风纪委员的自动投票功能，喜欢请给我点个STAR吧！ 如果你不是风纪委员，在符合风纪委员申请条件的情况下，本脚本会自动帮你申请 投票时间是早上八点，如果有需要请自行修改.github/workflows/Judge.yml中的时间，

Pesy Wu 25 Feb 17, 2021
Official code of the paper "Expanding Low-Density Latent Regions for Open-Set Object Detection" (CVPR 2022)

OpenDet Expanding Low-Density Latent Regions for Open-Set Object Detection (CVPR2022) Jiaming Han, Yuqiang Ren, Jian Ding, Xingjia Pan, Ke Yan, Gui-So

csuhan 64 Jan 07, 2023
PaSST: Efficient Training of Audio Transformers with Patchout

PaSST: Efficient Training of Audio Transformers with Patchout This is the implementation for Efficient Training of Audio Transformers with Patchout Pa

165 Dec 26, 2022
Self-Supervised Multi-Frame Monocular Scene Flow (CVPR 2021)

Self-Supervised Multi-Frame Monocular Scene Flow 3D visualization of estimated depth and scene flow (overlayed with input image) from temporally conse

Visual Inference Lab @TU Darmstadt 85 Dec 22, 2022
Source Code For Template-Based Named Entity Recognition Using BART

Template-Based NER Source Code For Template-Based Named Entity Recognition Using BART Training Training train.py Inference inference.py Corpus ATIS (h

174 Dec 19, 2022
Computer Vision application in the web

Computer Vision application in the web Preview Usage Clone this repo git clone https://github.com/amineHY/WebApp-Computer-Vision-streamlit.git cd Web

Amine Hadj-Youcef. PhD 35 Dec 06, 2022
Combining Diverse Feature Priors

Combining Diverse Feature Priors This repository contains code for reproducing the results of our paper. Paper: https://arxiv.org/abs/2110.08220 Blog

Madry Lab 5 Nov 12, 2022
Like Dirt-Samples, but cleaned up

Clean-Samples Like Dirt-Samples, but cleaned up, with clear provenance and license info (generally a permissive creative commons licence but check the

TidalCycles 39 Nov 30, 2022