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Description:

pughtorch 0.4.0

Pugh Torch

Functions, losses, module blocks to share between experiments.

Package Features

Additional methods to TensorBoard summary writer for adding normalized images and semantic segmentation images.
hetero_cross_entropy for cross_entropy loss across heterogeneous datasets
Convenient dataset downloading/unpacking to ~/.pugh_torch/datasets/.

You can override this via the ENV variable ROOT_DATASET_PATH.

Installation
Stable Release: pip install pugh_torch
Development Head: pip install git+https://github.com/BrianPugh/pugh_torch.git
Experiments
A big part of this repo is a framework to quickly be able to iterate on ideas.
To accomplish this, we provide the following:

A docker container brianpugh/pugh_torch that contains many dependencies
experimenters would like to use.

You can pull the docker image and launch the container via:
docker pull brianpugh/pugh_torch
./docker_run.sh

This will map ~/.pugh_torch and the local copy of the git repo
into the container. You may change this if you like.
This will also pass in any available GPUs and set other common
docker flags for running/training neural nets.
This container runs a VNC server, incase you need to perform some visual
actions, like using matplotlib.pyplot

A unified training driver experiments/train.py to run experiments.

From the experiments/ folder, run python3 train.py template to begin
training the default resnet50 architecture on ImageNet.
ImageNet cannot be automatically downloaded (see the error raised). To
get training started with an easier-to-obtain dataset, run:
python3 train.py template dataset=cifar100 model=cifar100

A template project experiments/template that should get you going. The goal
here is to provide maximum flexibility while minimizing "project startup
costs". We leverage the following libraries:

Hydra for managing experiment
hyperparameters and other configuration. It's a good idea to make your
code configurable via this configuration rather than directly tweaking
code to make experiments more trackable and reproduceable.
PyTorch-Lightning
for general project organization and training.
pugh_torch for various tweaks and helpers that make using the above
libraries easier for common projects and tasks.

Documentation
For full package documentation please visit BrianPugh.github.io/pugh_torch.
Free software: MIT license
Changelog
All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog,
and this project adheres to Semantic Versioning.
[0.4.0] - TBD
Added

pytorch-lightning callbacks (TensorBoardAddSS, TensorBoardAddClassification)
for add_ss and add_rgb for segmentation and classification tasks, respectively.
Initial form of a project template to get ideas going quickly.
ADE20K dataset
various optimizers and getters
various activation functions and getters
LoadStateDictMixin that adds verbosity to model loading and has more laxed
strict shape requirements.
pretrained resnet models (from torchvision) that utilize LoadStateDictMixin
Label smoothing losses

[0.3.1] - 2020-09-21
Added

Aliased ResizeShortest to ShortestMaxSize to be consistent with albumentations.augmentations.transforms.LongestMaxSize

Fixed

Add missing interpolation attribute in ResizeShortest transform.
Fixed ResizeShortest producing erroenous results when both sides are the same length.

[0.3.0] - 2020-09-21
Added

Text label adding to TensorBoard Images
ResizeShortest augmentation transform
Unit Testing utilities
basic Datasets API
A bunch of useful dependencies added.

[0.2.0] - 2020-09-15
Added

Additional extra_requires in preparation for docker release.

[0.1.0] - 2020-09-13
Added

Initial Release