ML-helper-tools

This repo contains helper functions and skeletons for training ML models in an attempt to avoid repeating code search.

The idea is to have a single repo to refer to for implementation and editing instead of writting from scratch, this is not supposed to be a script repo.

 

Losses

WassersteinLoss

• Earth-mover distance is a better metric for small permutations in image data compared to MSELoss.
• Original repo

PerceptualLoss

• Uses an encoder network to compare extracted features between target and generated, useful for SuperResolution. paper

TotalVariationLoss

• Jitters the image and calculates MSE.
• Used in SR paper

DeepEnergyLoss

• Maximizes difference between labels rather than set to 1 or 0.
• Docs

CycleGANLoss

• Extracted Cyclegan loss pipeline in case we want to abstract it to somthing different.

 

Models

CycleGAN

• Unaligned datasets for domain tranformation.
• Original repo

Siamese Network

• Classifies differences in latent space of encoder.
• This is mine :)

ResNet Generator

• Standard ResNet
• Original repo

MultiLayer Perceptron (Linear and Convolutional)

• ¯\(ツ)/¯

 

Utils

LightningWrapper for training

• Allows to easily use distributed training and GPU/TPU usage.
• Pytorch Lightning

TripletDataset

• Handles tuples of 3 (anchor [reference], positive [simmilar], negative [different]).

ABDataset

• Matches 2 classes of data in pairs.

MulticlassDataset

• Matches N classes of data in pairs.

ZCA Whitening

• Normalizes images so that covariance $\sum$ is the Identity matrix leading to decorrelated features.
• According to the paper, it should be applied batch-wise.