Data Augmentation with Variational Autoencoders

Overview



Documentation 	Status Downloads 	Status

Documentation

Pyraug

This library provides a way to perform Data Augmentation using Variational Autoencoders in a reliable way even in challenging contexts such as high dimensional and low sample size data.

Installation

To install the library from pypi.org run the following using pip

$ pip install pyraug

or alternatively you can clone the github repo to access to tests, tutorials and scripts.

$ git clone https://github.com/clementchadebec/pyraug.git

and install the library

$ cd pyraug
$ pip install .

Augmenting your Data

In Pyraug, a typical augmentation process is divided into 2 distinct parts:

  1. Train a model using the Pyraug's TrainingPipeline or using the provided scripts/training.py script
  2. Generate new data from a trained model using Pyraug's GenerationPipeline or using the provided scripts/generation.py script

There exist two ways to augment your data pretty straightforwardly using Pyraug's built-in functions.

Using Pyraug's Pipelines

Pyraug provides two pipelines that may be used to either train a model on your own data or generate new data with a pretrained model.

note: These pipelines are independent of the choice of the model and sampler. Hence, they can be used even if you want to access to more advanced features such as defining your own autoencoding architecture.

Launching a model training

To launch a model training, you only need to call a TrainingPipeline instance. In its most basic version the TrainingPipeline can be built without any arguments. This will by default train a RHVAE model with default autoencoding architecture and parameters.

>>> from pyraug.pipelines import TrainingPipeline
>>> pipeline = TrainingPipeline()
>>> pipeline(train_data=dataset_to_augment)

where dataset_to_augment is either a numpy.ndarray, torch.Tensor or a path to a folder where each file is a data (handled data formats are .pt, .nii, .nii.gz, .bmp, .jpg, .jpeg, .png).

More generally, you can instantiate your own model and train it with the TrainingPipeline. For instance, if you want to instantiate a basic RHVAE run:

>>> from pyraug.models import RHVAE
>>> from pyraug.models.rhvae import RHVAEConfig
>>> model_config = RHVAEConfig(
...    input_dim=int(intput_dim)
... ) # input_dim is the shape of a flatten input data
...   # needed if you did not provide your own architectures
>>> model = RHVAE(model_config)

In case you instantiate yourself a model as shown above and you did not provide all the network architectures (encoder, decoder & metric if applicable), the ModelConfig instance will expect you to provide the input dimension of your data which equals to n_channels x height x width x .... Pyraug's VAE models' networks indeed default to Multi Layer Perceptron neural networks which automatically adapt to the input data shape.

note: In case you have different size of data, Pyraug will reshape it to the minimum size min_n_channels x min_height x min_width x ...

Then the TrainingPipeline can be launched by running:

>>> from pyraug.pipelines import TrainingPipeline
>>> pipe = TrainingPipeline(model=model)
>>> pipe(train_data=dataset_to_augment)

At the end of training, the model weights models.pt and model config model_config.json file will be saved in a folder outputs/my_model/training_YYYY-MM-DD_hh-mm-ss/final_model.

Important: For high dimensional data we advice you to provide you own network architectures and potentially adapt the training and model parameters see documentation for more details.

Launching data generation

To launch the data generation process from a trained model, run the following.

>>> from pyraug.pipelines import GenerationPipeline
>>> from pyraug.models import RHVAE
>>> model = RHVAE.load_from_folder('path/to/your/trained/model') # reload the model
>>> pipe = GenerationPipeline(model=model) # define pipeline
>>> pipe(samples_number=10) # This will generate 10 data points

The generated data is in .pt files in dummy_output_dir/generation_YYYY-MM-DD_hh-mm-ss. By default, it stores batch data of a maximum of 500 samples.

Retrieve generated data

Generated data can then be loaded pretty easily by running

>>> import torch
>>> data = torch.load('path/to/generated_data.pt')

Using the provided scripts

Pyraug provides two scripts allowing you to augment your data directly with commandlines.

note: To access to the predefined scripts you should first clone the Pyraug's repository. The following scripts are located in scripts folder. For the time being, only RHVAE model training and generation is handled by the provided scripts. Models will be added as they are implemented in pyraug.models

Launching a model training:

To launch a model training, run

$ python scripts/training.py --path_to_train_data "path/to/your/data/folder" 

The data must be located in path/to/your/data/folder where each input data is a file. Handled image types are .pt, .nii, .nii.gz, .bmp, .jpg, .jpeg, .png. Depending on the usage, other types will be progressively added.

At the end of training, the model weights models.pt and model config model_config.json file will be saved in a folder outputs/my_model_from_script/training_YYYY-MM-DD_hh-mm-ss/final_model.

Launching data generation

Then, to launch the data generation process from a trained model, you only need to run

$ python scripts/generation.py --num_samples 10 --path_to_model_folder 'path/to/your/trained/model/folder' 

The generated data is stored in several .pt files in outputs/my_generated_data_from_script/generation_YYYY-MM-DD_hh_mm_ss. By default, it stores batch data of 500 samples.

Important: In the simplest configuration, default configurations are used in the scripts. You can easily override as explained in documentation. See tutorials for a more in depth example.

Retrieve generated data

Generated data can then be loaded pretty easily by running

>>> import torch
>>> data = torch.load('path/to/generated_data.pt')

Getting your hands on the code

To help you to understand the way Pyraug works and how you can augment your data with this library we also provide tutorials that can be found in examples folder:

Dealing with issues

If you are experiencing any issues while running the code or request new features please open an issue on github

Citing

If you use this library please consider citing us:

@article{chadebec_data_2021,
	title = {Data {Augmentation} in {High} {Dimensional} {Low} {Sample} {Size} {Setting} {Using} a {Geometry}-{Based} {Variational} {Autoencoder}},
	copyright = {All rights reserved},
	journal = {arXiv preprint arXiv:2105.00026},
  	arxiv = {2105.00026},
	author = {Chadebec, Clément and Thibeau-Sutre, Elina and Burgos, Ninon and Allassonnière, Stéphanie},
	year = {2021}
}

Credits

Logo: SaulLu

You might also like...
Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners

Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners This repository is built upon BEiT, thanks very much! Now, we on

PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners for self-supervised ViT.

MAE for Self-supervised ViT Introduction This is an unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners for self-sup

 An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners
An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners

An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners This is a coarse version for MAE, only make the pretrain model, the fine

A framework that constructs deep neural networks, autoencoders, logistic regressors, and linear networks

A framework that constructs deep neural networks, autoencoders, logistic regressors, and linear networks without the use of any outside machine learning libraries - all from scratch.

Autoencoders pretraining using clustering

Autoencoders pretraining using clustering

Re-implememtation of MAE (Masked Autoencoders Are Scalable Vision Learners) using PyTorch.

mae-repo PyTorch re-implememtation of "masked autoencoders are scalable vision learners". In this repo, it heavily borrows codes from codebase https:/

ConvMAE: Masked Convolution Meets Masked Autoencoders
ConvMAE: Masked Convolution Meets Masked Autoencoders

ConvMAE ConvMAE: Masked Convolution Meets Masked Autoencoders Peng Gao1, Teli Ma1, Hongsheng Li2, Jifeng Dai3, Yu Qiao1, 1 Shanghai AI Laboratory, 2 M

Code and pre-trained models for MultiMAE: Multi-modal Multi-task Masked Autoencoders
Code and pre-trained models for MultiMAE: Multi-modal Multi-task Masked Autoencoders

MultiMAE: Multi-modal Multi-task Masked Autoencoders Roman Bachmann*, David Mizrahi*, Andrei Atanov, Amir Zamir Website | arXiv | BibTeX Official PyTo

This is the official Pytorch implementation of
This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et al. 2020

README This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et a

Comments
  • It takes a long time to train the model

    It takes a long time to train the model

    I am trying to train a RHVAE model for data augmentation and the model starts training but it takes a long time training and do not see any results. I do not know if is an error from my dataset, computer or from the library. Could you help me?

    opened by mikel-hernandezj 2
  • Geodesics computation

    Geodesics computation

    It would be great to have a function to compute geodesics, given a trained model and two points in the latent space.

    The goal would be to allow the exploration of the latent space via geodesics, as visualised in Figure 2 of (Chadebec et al., 2021):

    Screenshot 2021-09-28 at 10 06 34 enhancement 
    opened by Virgiliok 2
  • riemann_tools

    riemann_tools

    Hi,

    In on of your example notebooks (geodesic_computation_example), you import the function Geodesic_autodiff from the package riemann_tools. I cannot find any mention of this package however. Could you perhaps provide some documentation on how to install/import the riemann_tools? Thank you in advance!

    Edit: removing the import solved the problem

    opened by VivienvV 0
Releases(v0.0.6)
FAVD: Featherweight Assisted Vulnerability Discovery

FAVD: Featherweight Assisted Vulnerability Discovery This repository contains the replication package for the paper "Featherweight Assisted Vulnerabil

secureIT 4 Sep 16, 2022
[UNMAINTAINED] Automated machine learning for analytics & production

auto_ml Automated machine learning for production and analytics Installation pip install auto_ml Getting started from auto_ml import Predictor from au

Preston Parry 1.6k Jan 02, 2023
Code for CPM-2 Pre-Train

CPM-2 Pre-Train Pre-train CPM-2 此分支为110亿非 MoE 模型的预训练代码,MoE 模型的预训练代码请切换到 moe 分支 CPM-2技术报告请参考link。 0 模型下载 请在智源资源下载页面进行申请,文件介绍如下: 文件名 描述 参数大小 100000.tar

Tsinghua AI 136 Dec 28, 2022
A novel benchmark dataset for Monocular Layout prediction

AutoLay AutoLay: Benchmarking Monocular Layout Estimation Kaustubh Mani, N. Sai Shankar, J. Krishna Murthy, and K. Madhava Krishna Abstract In this pa

Kaustubh Mani 39 Apr 26, 2022
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN.

Ryan Murdock has done it again, combining OpenAI's CLIP and the generator from a BigGAN! This repository wraps up his work so it is easily accessible to anyone who owns a GPU.

Phil Wang 2.3k Jan 09, 2023
This repository contains the accompanying code for Deep Virtual Markers for Articulated 3D Shapes, ICCV'21

Deep Virtual Markers This repository contains the accompanying code for Deep Virtual Markers for Articulated 3D Shapes, ICCV'21 Getting Started Get sa

KimHyomin 45 Oct 07, 2022
Official implementation of "One-Shot Voice Conversion with Weight Adaptive Instance Normalization".

One-Shot Voice Conversion with Weight Adaptive Instance Normalization By Shengjie Huang, Yanyan Xu*, Dengfeng Ke*, Mingjie Chen, Thomas Hain. This rep

31 Dec 07, 2022
PyTorch implementation of MuseMorphose, a Transformer-based model for music style transfer.

MuseMorphose This repository contains the official implementation of the following paper: Shih-Lun Wu, Yi-Hsuan Yang MuseMorphose: Full-Song and Fine-

Yating Music, Taiwan AI Labs 142 Jan 08, 2023
A keras implementation of ENet (abandoned for the foreseeable future)

ENet-keras This is an implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation, ported from ENet-training (lua-t

Pavlos 115 Nov 23, 2021
Get started with Machine Learning with Python - An introduction with Python programming examples

Machine Learning With Python Get started with Machine Learning with Python An engaging introduction to Machine Learning with Python TL;DR Download all

Learn Python with Rune 130 Jan 02, 2023
Reference models and tools for Cloud TPUs.

Cloud TPUs This repository is a collection of reference models and tools used with Cloud TPUs. The fastest way to get started training a model on a Cl

5k Jan 05, 2023
The 1st Place Solution of the Facebook AI Image Similarity Challenge (ISC21) : Descriptor Track.

ISC21-Descriptor-Track-1st The 1st Place Solution of the Facebook AI Image Similarity Challenge (ISC21) : Descriptor Track. You can check our solution

lyakaap 75 Jan 08, 2023
FcaNet: Frequency Channel Attention Networks

FcaNet: Frequency Channel Attention Networks PyTorch implementation of the paper "FcaNet: Frequency Channel Attention Networks". Simplest usage Models

327 Dec 27, 2022
The Pytorch code of "Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification", CVPR 2022 (Oral).

DeepBDC for few-shot learning        Introduction In this repo, we provide the implementation of the following paper: "Joint Distribution Matters: Dee

FeiLong 116 Dec 19, 2022
[TIP 2020] Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion

Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion Code for Multi-Temporal Scene Classification and Scene Ch

Lixiang Ru 33 Dec 12, 2022
CityLearn Challenge Multi-Agent Reinforcement Learning for Intelligent Energy Management, 2020, PikaPika team

Citylearn Challenge This is the PyTorch implementation for PikaPika team, CityLearn Challenge Multi-Agent Reinforcement Learning for Intelligent Energ

bigAIdream projects 10 Oct 10, 2022
PyTorch implementation of 1712.06087 "Zero-Shot" Super-Resolution using Deep Internal Learning

Unofficial PyTorch implementation of "Zero-Shot" Super-Resolution using Deep Internal Learning Unofficial Implementation of 1712.06087 "Zero-Shot" Sup

Jacob Gildenblat 196 Nov 27, 2022
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis

Readme File for "Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis" by Ham, Imai, and Janson. (2022) All scripts were written and

0 Jan 27, 2022
ParmeSan: Sanitizer-guided Greybox Fuzzing

ParmeSan: Sanitizer-guided Greybox Fuzzing ParmeSan is a sanitizer-guided greybox fuzzer based on Angora. Published Work USENIX Security 2020: ParmeSa

VUSec 158 Dec 31, 2022
Mixed Transformer UNet for Medical Image Segmentation

MT-UNet Update 2022/01/05 By another round of training based on previous weights, our model also achieved a better performance on ACDC (91.61% DSC). W

dotman 92 Dec 25, 2022