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Official implementation of 'Understanding and Enhancing Mixed Sample Data Augmentation'

License: MIT License

Python 95.42% Jupyter Notebook 3.03% Shell 1.55%

fmix's Introduction

FMix

This repository contains the official implementation of the paper 'Understanding and Enhancing Mixed Sample Data Augmentation'

ArXiv • Papers With Code • About • Getting Started • Pre-trained Models (Coming Soon)

About

FMix is a variant of MixUp, CutMix, etc. introduced in our paper 'Understanding and Enhancing Mixed Sample Data Augmentation'. It uses masks sampled from Fourier space to mix training examples. Here's an example:

Getting Started

The core implementation of FMix uses numpy and can be found in fmix.py. We provide bindings for this in PyTorch (with Torchbearer or PyTorch-Lightning) and Tensorflow.

Torchbearer

The FMix callback in torchbearer_implementation.py can be added directly to your torchbearer code:

from implementations.torchbearer_implementation import FMix

fmix = FMix()
trial = Trial(model, optimiser, fmix.loss(), callbacks=[fmix])

See an example in test_torchbearer.py.

PyTorch-Lightning

For PyTorch-Lightning, we provide a class, FMix in lightning.py that can be used in your LightningModule:

from implementations.lightning import FMix

class CoolSystem(pl.LightningModule):
    def __init__(self):
        ...
        
        self.fmix = FMix()
    
    def training_step(self, batch, batch_nb):
        x, y = batch
        x = self.fmix(x)

        x = self.forward(x)

        loss = self.fmix.loss(x, y)
        return {'loss': loss}

See an example in test_lightning.py.

Tensorflow

For Tensorflow, we provide a class, FMix in tensorflow_implementation.py that can be used in your tensorflow code:

from implementations.tensorflow_implementation import FMix

fmix = FMix()

def loss(model, x, y, training=True):
    x = fmix(x)
    y_ = model(x, training=training)
    return tf.reduce_mean(fmix.loss(y_, y))

See an example in test_tensorflow.py.