%global _empty_manifest_terminate_build 0 Name: python-continuum Version: 1.2.7 Release: 1 Summary: A clean and simple library for Continual Learning in PyTorch. License: MIT License URL: https://github.com/Continvvm/continuum Source0: https://mirrors.nju.edu.cn/pypi/web/packages/39/86/f3ab27dd5d2169e58a92fbf08717fbd584217342a4d5248cfe03fa3297fc/continuum-1.2.7.tar.gz BuildArch: noarch %description

# Continuum: Simple Management of Complex Continual Learning Scenarios [![PyPI version](https://badge.fury.io/py/continuum.svg)](https://badge.fury.io/py/continuum) [![Build Status](https://travis-ci.com/Continvvm/continuum.svg?branch=master)](https://travis-ci.com/Continvvm/continuum) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/c3a31475bebc4036a13e6048c24eb3e0)](https://www.codacy.com/gh/Continvvm/continuum?utm_source=github.com&utm_medium=referral&utm_content=Continvvm/continuum&utm_campaign=Badge_Grade) [![DOI](https://zenodo.org/badge/254864913.svg)](https://zenodo.org/badge/latestdoi/254864913) [![Documentation Status](https://readthedocs.org/projects/continuum/badge/?version=latest)](https://continuum.readthedocs.io/en/latest/?badge=latest) [![coverage](coverage.svg)]() [![Doc](https://img.shields.io/badge/Documentation-link-blue)](https://continuum.readthedocs.io/) [![Paper](https://img.shields.io/badge/arXiv-2102.06253-brightgreen)](https://arxiv.org/abs/2102.06253) [![Youtube](https://img.shields.io/badge/Youtube-link-purple)](https://www.youtube.com/watch?v=ntSR5oYKyhM)

## A library for PyTorch's loading of datasets in the field of Continual Learning Aka Continual Learning, Lifelong-Learning, Incremental Learning, etc. Read the [documentation](https://continuum.readthedocs.io/en/latest/).
Test Continuum on [Colab](https://colab.research.google.com/drive/1bRx3M1YFcol9RZxBZ51brxqGWrf4-Bzn?usp=sharing) ! ### Example: Install from and PyPi: ```bash pip3 install continuum ``` And run! ```python from torch.utils.data import DataLoader from continuum import ClassIncremental from continuum.datasets import MNIST from continuum.tasks import split_train_val dataset = MNIST("my/data/path", download=True, train=True) scenario = ClassIncremental( dataset, increment=1, initial_increment=5 ) print(f"Number of classes: {scenario.nb_classes}.") print(f"Number of tasks: {scenario.nb_tasks}.") for task_id, train_taskset in enumerate(scenario): train_taskset, val_taskset = split_train_val(train_taskset, val_split=0.1) train_loader = DataLoader(train_taskset, batch_size=32, shuffle=True) val_loader = DataLoader(val_taskset, batch_size=32, shuffle=True) for x, y, t in train_loader: # Do your cool stuff here ``` ### Supported Types of Scenarios |Name | Acronym | Supported | Scenario | |:----|:---|:---:|:---:| | **New Instances** | NI | :white_check_mark: | [Instances Incremental](https://continuum.readthedocs.io/en/latest/_tutorials/scenarios/scenarios.html#instance-incremental)| | **New Classes** | NC | :white_check_mark: |[Classes Incremental](https://continuum.readthedocs.io/en/latest/_tutorials/scenarios/scenarios.html#classes-incremental)| | **New Instances & Classes** | NIC | :white_check_mark: | [Data Incremental](https://continuum.readthedocs.io/en/latest/_tutorials/scenarios/scenarios.html#new-class-and-instance-incremental)| ### Supported Datasets: Most dataset from [torchvision.dasasets](https://pytorch.org/docs/stable/torchvision/datasets.html) are supported, for the complete list, look at the documentation page on datasets [here](https://continuum.readthedocs.io/en/latest/_tutorials/datasets/dataset.html). Furthermore some "Meta"-datasets are can be create or used from numpy array or any torchvision.datasets or from a folder for datasets having a tree-like structure or by combining several dataset and creating dataset fellowships! ### Indexing All our continual loader are iterable (i.e. you can for loop on them), and are also indexable. Meaning that `clloader[2]` returns the third task (index starts at 0). Likewise, if you want to evaluate after each task, on all seen tasks do `clloader_test[:n]`. ### Example of Sample Images from a Continuum scenario **CIFAR10**: |

| |:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:| |Task 0 | Task 1 | Task 2 | Task 3 | Task 4| **MNIST Fellowship (MNIST + FashionMNIST + KMNIST)**: |

| |:-------------------------:|:-------------------------:|:-------------------------:| |Task 0 | Task 1 | Task 2 | **PermutedMNIST**: |

| |:-------------------------:|:-------------------------:|:-------------------------:| |Task 0 | Task 1 | Task 2 | ### Citation If you find this library useful in your work, please consider citing it: ``` @misc{douillardlesort2021continuum, author={Douillard, Arthur and Lesort, Timothée}, title={Continuum: Simple Management of Complex Continual Learning Scenarios}, publisher={arXiv: 2102.06253}, year={2021} } ``` ### Maintainers This project was started by a joint effort from [Arthur Douillard](https://arthurdouillard.com/) & [Timothée Lesort](https://tlesort.github.io/), and we are currently the two maintainers. Feel free to contribute! If you want to propose new features, please create an issue. Contributors: [Lucas Caccia](https://github.com/pclucas14) [Lucas Cecchi](https://github.com/Lucasc-99) [Pau Rodriguez](https://github.com/prlz77), [Yury Antonov](https://github.com/yantonov), [psychicmario](https://github.com/psychicmario), [fcld94](https://github.com/fcdl94), [Ashok Arjun](https://github.com/ashok-arjun), [Md Rifat Arefin](https://github.com/rarefin), [DanieleMugnai](https://github.com/mugnaidaniele), [Xiaohan Zou](https://github.com/Renovamen), [Umberto Cappellazzo](https://github.com/umbertocappellazzo). ### On PyPi Our project is available on PyPi! ```bash pip3 install continuum ``` Note that previously another project, a CI tool, was using that name. It is now there [continuum_ci](https://pypi.org/project/continuum_ci/). %prep %autosetup -n continuum-1.2.7 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-continuum -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 1.2.7-1 - Package Spec generated