Torchélie

Torchélie is a set of tools for pyTorch. It includes losses, optimizers, algorithms, utils, layers, models and training loops.

Feedback is absolutely welcome.

You may want to read the detailed docs

Installation

pip install git+https://github.com/vermeille/Torchelie

It depends on Pytorch (obvi), and has an optional dependency on OpenCV for some transforms (Canny, as of today). It also depends on Visdom for realtime visualizations, plotting, etc.

To install visdom: pip install visdom. Then, you need to run a Visdom server with python -m visdom.server, direct your browser to http://localhost:8097. Now you're ready to use VisdomLogger and enjoy realtime tracking of your experiments.

WARNINGS

torchelie.recipes

Classes implementing full algorithms, from training to usage

torchelie.utils

Functions:

torchelie.nn

Debug modules:

Normalization modules:

Misc modules:

Container modules:

Model manipulation modules:

Net Blocks:

torchelie.models

Debug models:

torchelie.loss

Modules:

Functions (torchelie.loss.functional):

torchelie.loss.gan

Each submodule is a GAN loss function. They all contain three methods: real(x) and fake(x) to train the discriminator, and ŋenerated(x) to improve the Generator.

Available:

torchelie.transforms

Torchvision-like transforms:

torchelie.transforms.differentiable

Contains some transforms that can be backpropagated through. Its API is unstable now.

torchelie.lr_scheduler

Classes:

torchelie.datasets

torchelie.datasets.debug

torchelie.metrics

torchelie.opt

torchelie.data_learning

Data parameterization for optimization, like neural style or feature viz.

Modules:

Testing