slcs

Developer Guide

Setup

# create conda environment
$ mamba env create -f env.yml

# update conda environment
$ mamba env update -n slcs --file env.yml

Install

pip install -e .

# install from pypi
pip install slcs

nbdev

# activate conda environment
$ conda activate slcs

# make sure the slcs package is installed in development mode
$ pip install -e .

# make changes under nbs/ directory
# ...

# compile to have changes apply to the slcs package
$ nbdev_prepare

Publishing

# publish to pypi
$ nbdev_pypi

# publish to conda
$ nbdev_conda --build_args '-c conda-forge'
$ nbdev_conda --mambabuild --build_args '-c conda-forge -c dsm-72'

Usage

Installation

Install latest from the GitHub repository:

$ pip install git+https://github.com/dsm-72/slcs.git

or from conda

$ conda install -c dsm-72 slcs

or from pypi

$ pip install slcs

Documentation

Documentation can be found hosted on GitHub repository pages. Additionally you can find package manager specific guidelines on conda and pypi respectively.

PyTorch Documentation:

PyTorch Models to Consider:

Diffusion Video AutoEncoders
ConvLSTM AutoEncoder
Recurrent All Pairs Field Transforms for Optical Flow
Optical Flow Toolbox (mmflow)
torchvision.models.optical_flow.raft