MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.

Related tags

Deep Learningpytorchgangenerativeopenmmlabmmcv
Overview

Documentation: https://mmgeneration.readthedocs.io/

Introduction

English | 简体中文

MMGeneration is a powerful toolkit for generative models, especially for GANs now. It is based on PyTorch and MMCV. The master branch works with PyTorch 1.5+.

Major Features

  • High-quality Training Performance: We currently support training on Unconditional GANs, Internal GANs, and Image Translation Models. Support for conditional models will come soon.
  • Powerful Application Toolkit: A plentiful toolkit containing multiple applications in GANs is provided to users. GAN interpolation, GAN projection, and GAN manipulations are integrated into our framework. It's time to play with your GANs! (Tutorial for applications)
  • Efficient Distributed Training for Generative Models: For the highly dynamic training in generative models, we adopt a new way to train dynamic models with MMDDP. (Tutorial for DDP)
  • New Modular Design for Flexible Combination: A new design for complex loss modules is proposed for customizing the links between modules, which can achieve flexible combination among different modules. (Tutorial for new modular design)
Training Visualization
GAN Interpolation
GAN Projector
GAN Manipulation

Highlight

  • Positional Encoding as Spatial Inductive Bias in GANs (CVPR2021) has been released in MMGeneration. [Config], [Project Page]

Changelog

v0.1.0 was released on 20/04/2021. Please refer to changelog.md for details and release history.

ModelZoo

These methods have been carefully studied and supported in our frameworks:

Unconditional GANs (click to collapse)
Image2Image Translation (click to collapse)
Internal Learing (click to collapse)

License

This project is released under the Apache 2.0 license. Some operations in MMGeneration are with other licenses instead of Apache2.0. Please refer to LICENSES.md for the careful check, if you are using our code for commercial matters.

Installation

Please refer to get_started.md for installation.

Getting Started

Please see get_started.md for the basic usage of MMGeneration. docs/quick_run.md can offer full guidance for quick run. For other details and tutorials, please go to our documentation.

Contributing

We appreciate all contributions to improve MMGeneration. Please refer to CONTRIBUTING.md in MMCV for more details about the contributing guideline.

Citation

If you find this project useful in your research, please consider cite:

@misc{2021mmgeneration,
    title={{MMGeneration}: OpenMMLab Generative Model Toolbox and Benchmark},
    author={MMGeneration Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmgeneration}},
    year={2021}
}

Projects in OpenMMLab

  • MMCV: OpenMMLab foundational library for computer vision.
  • MMClassification: OpenMMLab image classification toolbox and benchmark.
  • MMDetection: OpenMMLab detection toolbox and benchmark.
  • MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
  • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
  • MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
  • MMTracking: OpenMMLab video perception toolbox and benchmark.
  • MMPose: OpenMMLab pose estimation toolbox and benchmark.
  • MMEditing: OpenMMLab image and video editing toolbox.
  • MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
  • MMGeneration: OpenMMLab's next-generation toolbox for generative models.
Comments
  • Fix s3 readme of 1.x branch

    Fix s3 readme of 1.x branch

    Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.

    Motivation

    Please describe the motivation of this PR and the goal you want to achieve through this PR.

    Modification

    Please briefly describe what modification is made in this PR.

    Who can help? @ them here!

    BC-breaking (Optional)

    Does the modification introduce changes that break the backward-compatibility of the downstream repositories? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.

    Use cases (Optional)

    If this PR introduces a new feature, it is better to list some use cases here, and update the documentation.

    Checklist

    Before PR:

    • [ ] I have read and followed the workflow indicated in the CONTRIBUTING.md to create this PR.
    • [ ] Pre-commit or linting tools indicated in CONTRIBUTING.md are used to fix the potential lint issues.
    • [ ] Bug fixes are covered by unit tests, the case that causes the bug should be added in the unit tests.
    • [ ] New functionalities are covered by complete unit tests. If not, please add more unit test to ensure the correctness.
    • [ ] The documentation has been modified accordingly, including docstring or example tutorials.

    After PR:

    • [ ] If the modification has potential influence on downstream or other related projects, this PR should be tested with some of those projects.
    • [ ] CLA has been signed and all committers have signed the CLA in this PR.
    opened by plyfager 0
  • Fix s3 readme dev-1.x

    Fix s3 readme dev-1.x

    Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.

    Motivation

    Please describe the motivation of this PR and the goal you want to achieve through this PR.

    Modification

    Please briefly describe what modification is made in this PR.

    Who can help? @ them here!

    BC-breaking (Optional)

    Does the modification introduce changes that break the backward-compatibility of the downstream repositories? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.

    Use cases (Optional)

    If this PR introduces a new feature, it is better to list some use cases here, and update the documentation.

    Checklist

    Before PR:

    • [ ] I have read and followed the workflow indicated in the CONTRIBUTING.md to create this PR.
    • [ ] Pre-commit or linting tools indicated in CONTRIBUTING.md are used to fix the potential lint issues.
    • [ ] Bug fixes are covered by unit tests, the case that causes the bug should be added in the unit tests.
    • [ ] New functionalities are covered by complete unit tests. If not, please add more unit test to ensure the correctness.
    • [ ] The documentation has been modified accordingly, including docstring or example tutorials.

    After PR:

    • [ ] If the modification has potential influence on downstream or other related projects, this PR should be tested with some of those projects.
    • [ ] CLA has been signed and all committers have signed the CLA in this PR.
    opened by plyfager 0
  • Fix s3 readme with refined metrics

    Fix s3 readme with refined metrics

    Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.

    Motivation

    Please describe the motivation of this PR and the goal you want to achieve through this PR.

    Modification

    Please briefly describe what modification is made in this PR.

    Who can help? @ them here!

    BC-breaking (Optional)

    Does the modification introduce changes that break the backward-compatibility of the downstream repositories? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.

    Use cases (Optional)

    If this PR introduces a new feature, it is better to list some use cases here, and update the documentation.

    Checklist

    Before PR:

    • [ ] I have read and followed the workflow indicated in the CONTRIBUTING.md to create this PR.
    • [ ] Pre-commit or linting tools indicated in CONTRIBUTING.md are used to fix the potential lint issues.
    • [ ] Bug fixes are covered by unit tests, the case that causes the bug should be added in the unit tests.
    • [ ] New functionalities are covered by complete unit tests. If not, please add more unit test to ensure the correctness.
    • [ ] The documentation has been modified accordingly, including docstring or example tutorials.

    After PR:

    • [ ] If the modification has potential influence on downstream or other related projects, this PR should be tested with some of those projects.
    • [ ] CLA has been signed and all committers have signed the CLA in this PR.
    opened by plyfager 0
  • Latent-Diffusion Models

    Latent-Diffusion Models

    Model/Dataset/Scheduler description

    Recent trends show that diffusion models, especially latent diffusion models, have been very successful for image/video generation tasks. While the current codebase supports IDDPM, I believe supporting LDM for tasks like unconditional image generation or even text-to-image generation would be very helpful for the openmmlab family.

    Open source status

    • [ ] The model implementation is available
    • [ ] The model weights are available.

    Provide useful links for the implementation

    https://github.com/CompVis/latent-diffusion

    opened by Adamdad 5
  • add colab page

    add colab page

    Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.

    Motivation

    Please describe the motivation of this PR and the goal you want to achieve through this PR.

    Modification

    Please briefly describe what modification is made in this PR.

    Who can help? @ them here!

    BC-breaking (Optional)

    Does the modification introduce changes that break the backward-compatibility of the downstream repositories? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.

    Use cases (Optional)

    If this PR introduces a new feature, it is better to list some use cases here, and update the documentation.

    Checklist

    Before PR:

    • [ ] I have read and followed the workflow indicated in the CONTRIBUTING.md to create this PR.
    • [ ] Pre-commit or linting tools indicated in CONTRIBUTING.md are used to fix the potential lint issues.
    • [ ] Bug fixes are covered by unit tests, the case that causes the bug should be added in the unit tests.
    • [ ] New functionalities are covered by complete unit tests. If not, please add more unit test to ensure the correctness.
    • [ ] The documentation has been modified accordingly, including docstring or example tutorials.

    After PR:

    • [ ] If the modification has potential influence on downstream or other related projects, this PR should be tested with some of those projects.
    • [ ] CLA has been signed and all committers have signed the CLA in this PR.
    opened by plyfager 0
Releases(v0.7.2)

  • v0.7.2(Sep 14, 2022)

    Improvements

    • [Docs] Fix typos in docs. by @RangeKing in https://github.com/open-mmlab/mmgeneration/pull/425
    • [Docs] Add doc for StyleGAN-Ada by @plyfager in https://github.com/open-mmlab/mmgeneration/pull/391
    • [Fix] Update MMCV_MAX to 1.7 by @LeoXing1996 in https://github.com/open-mmlab/mmgeneration/pull/436
    • [CI] update github workflow, circleci and github templates by @LeoXing1996 in https://github.com/open-mmlab/mmgeneration/pull/431

    New Contributors

    • @RangeKing made their first contribution in https://github.com/open-mmlab/mmgeneration/pull/425
    Source code(tar.gz)
    Source code(zip)

  • v1.0.0rc0(Sep 1, 2022)

    MMGeneration 1.0.0rc0 is the first version of MMGeneration 1.x, a part of the OpenMMLab 2.0 projects.

    Built upon the new training engine, MMGeneration 1.x unifies the interfaces of dataset, models, evaluation, and visualization.

    And there are some BC-breaking changes. Please check the migration tutorial for more details.

    Source code(tar.gz)
    Source code(zip)

  • v0.7.1(Apr 30, 2022)

    Fix bugs and Improvements

    • Support train_dataloader, val_dataloader and test_dataloader settings (#281)
    • Fix ada typo (#283)
    • Add chinese application tutorial (#284)
    • Add chinese document of ddp training (#286)

    Contributors

    @plyfager @LeoXing1996

    Source code(tar.gz)
    Source code(zip)

  • v0.7.0(Apr 2, 2022)

    Highlights

    • Support training of StyleGANv3 (#275, #277)
    • Support adaptive discriminator augmentation (#276)

    New Features

    • Support passing training arguments in static unconditional gan (#275)
    • Support dynamic EMA, now you can define momentum updating policy (#261)
    • Add multi machine distribute train (#267)

    Fix bugs and Improvements

    • Add brief installation steps in README (#270)
    • Support random seed for distributed sampler (#271)
    • Use hyphen for command line args in apps (#273)

    Contributors

    @plyfager @LeoXing1996

    Source code(tar.gz)
    Source code(zip)

  • v0.6.0(Mar 7, 2022)

    Highlights

    • Support StyleGANv3 (#247, #253, #258)
    • Support StyleCLIP (#236)

    New Features

    • Support training on CPU (#238)
    • Speed up training (#231)

    Fix bugs and Improvements

    • Fix bug in non-distributed training/testing (#239)
    • Fix typos and invalid links (#221, #226, #228, #244, #249)
    • Add part of Chinese documentation (#250, #257)

    Contributors

    @plyfager @LeoXing1996 @gvalvano @JimHeo @plutoyuxie

    Source code(tar.gz)
    Source code(zip)

  • v0.5.0(Jan 12, 2022)

    Highlights

    • Support BigGAN style's Spectral Norm and update BigGAN with best FID and IS (#159)
    • Support import projected latent and export video in interpolation (#167)
    • Support Improved-DDPM model (#205)

    New Features

    • Support evaluation in distributed mode (#151)
    • Support presistent_work in validation dataloader (#179)
    • Support dockerfile (#200)
    • Support mim (#176)

    Fix bugs and Improvements

    • Fix bug in SinGAN dataset (#192)
    • Fix SAGAN, SNGAN and BigGAN's default sn_style (#199, #213, #215, #217)

    Contributors

    @plyfager @LeoXing1996 @nbei @TommyZihao @JiangongWang

    Source code(tar.gz)
    Source code(zip)

  • v0.4.0(Nov 3, 2021)

    Highlights

    • Add more experiments for conditional GANs: SNGAN, SAGAN, and BigGAN
    • Refact Translation Model (#88, #126, #127, #145)

    New Features

    • Use PyTorch Sphinx theme #123
    • Support torchserve for unconditional models #131

    Fix bugs and Improvements

    • Add CI for python3.9 #110
    • Add support for PyTorch1.9 #115
    • Add pre-commit hook for spell checking #135

    Contributors

    @plyfager , @LeoXing1996 , @ckkelvinchan , @nbei

    Source code(tar.gz)
    Source code(zip)

  • v0.3.0(Aug 2, 2021)

    Highlights

    • Support conditional GANs: Projection GAN, SNGAN, SAGAN, and BigGAN

    New Features

    • Add support for persistent_workers in PyTorch >= 1.7.0 #71
    • Support warm-up for EMA #55

    Fix bugs and Improvements

    • Fix failing to build docs #64
    • Revise the logic of num_classes in basic conditional gan #69
    • Support dynamic eval internal in eval hook #73
    Source code(tar.gz)
    Source code(zip)

  • v0.2.0(May 30, 2021)

    Highlights

    • Support new methods: LSGAN, GGAN.
    • Support mixed-precision training (FP16): official PyTorch Implementation and APEX (#11, #20)

    New Features

    • Add the experiment of MNIST in DCGAN (#24)
    • Add support for uploading checkpoints to Ceph system (cloud server) (#27)
    • Add the functionality of saving the best checkpoint in GenerativeEvalHook (#21)

    Fix bugs and Improvements

    • Fix loss of sample-cfg argument (#13)
    • Add pbar to offline eval and fix bug in grayscale image evaluation/saving (#23)
    • Fix error when data_root option in val_cfg or test_cfg are set as None (#28)
    • Change latex in quick_run.md to svg url and fix number of checkpoints in modelzoo_statistics.md (#34)
    Source code(tar.gz)
    Source code(zip)

  • v0.1.0(Apr 23, 2021)

    Highlights

    • MMGeneration v0.1.0 is released.

    Main Features

    • High-quality Training Performance: We currently support training on Unconditional GANs(DCGAN, WGAN-GP,PGGAN, StyleGANV1, StyleGANV2, Positional Encoding in GANs), Internal GANs(SinGAN), and Image Translation Models(Pix2Pix, CycleGAN). Support for conditional models will come soon.
    • Powerful Application Toolkit: A plentiful toolkit containing multiple applications in GANs is provided to users. GAN interpolation, GAN projection, and GAN manipulations are integrated into our framework. It's time to play with your GANs!
    • Efficient Distributed Training for Generative Models: For the highly dynamic training in generative models, we adopt a new way to train dynamic models with MMDDP.
    • New Modular Design for Flexible Combination: A new design for complex loss modules is proposed for customizing the links between modules, which can achieve flexible combinations among different modules.
    Source code(tar.gz)
    Source code(zip)
Owner
OpenMMLab
OpenMMLab
GitHub Repository
RoMa: A lightweight library to deal with 3D rotations in PyTorch.

RoMa: A lightweight library to deal with 3D rotations in PyTorch. RoMa (which stands for Rotation Manipulation) provides differentiable mappings betwe

NAVER 90 Dec 27, 2022
code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning Overview This code is for paper: Not All Unlabeled Data are Equa

Jason Ren 22 Nov 23, 2022
The code is the training example of AAAI2022 Security AI Challenger Program Phase 8: Data Centric Robot Learning on ML models.

Example code of [Tianchi AAAI2022 Security AI Challenger Program Phase 8]

22 Oct 14, 2022
You can draw the corresponding bounding box into the image and save it according to the result file (txt format) run by the tracker.

You can draw the corresponding bounding box into the image and save it according to the result file (txt format) run by the tracker.

Huiyiqianli 42 Dec 06, 2022
The story of Chicken for Club Bing

Chicken Story tl;dr: The time when Microsoft banned my entire country for cheating at Club Bing. (A lot of the details are from memory so I've recreat

Eyal 142 May 16, 2022
Implementation of MeMOT - Multi-Object Tracking with Memory - in Pytorch

MeMOT - Pytorch (wip) Implementation of MeMOT - Multi-Object Tracking with Memory - in Pytorch. This paper is just one in a line of work, but importan

Phil Wang 15 May 09, 2022
Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree

This is a Python implementation of cover trees, a data structure for finding nearest neighbors in a general metric space (e.g., a 3D box with periodic

Patrick Varilly 28 Nov 25, 2022
SVG Icon processing tool for C++

BAWR This is a tool to automate the icons generation from sets of svg files into fonts and atlases. The main purpose of this tool is to add it to the

Frank David Martínez M 66 Dec 14, 2022
A C implementation for creating 2D voronoi diagrams

Branch OSX/Linux Windows master dev jc_voronoi A fast C/C++ header only implementation for creating 2D Voronoi diagrams from a point set Uses Fortune'

Mathias Westerdahl 481 Dec 29, 2022
Code and data form the paper BERT Got a Date: Introducing Transformers to Temporal Tagging

BERT Got a Date: Introducing Transformers to Temporal Tagging Satya Almasian*, Dennis Aumiller*, and Michael Gertz Heidelberg University Contact us vi

54 Dec 04, 2022
Multi-Stage Episodic Control for Strategic Exploration in Text Games

XTX: eXploit - Then - eXplore Requirements First clone this repo using git clone https://github.com/princeton-nlp/XTX.git Please create two conda envi

Princeton Natural Language Processing 9 May 24, 2022
This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

Graphormer By Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng*, Guolin Ke, Di He*, Yanming Shen and Tie-Yan Liu. This repo is the official impl

Microsoft 1.3k Dec 29, 2022
PyTorch implementation of Deep HDR Imaging via A Non-Local Network (TIP 2020).

NHDRRNet-PyTorch This is the PyTorch implementation of Deep HDR Imaging via A Non-Local Network (TIP 2020). 0. Differences between Original Paper and

Yutong Zhang 1 Mar 01, 2022
Official code for UnICORNN (ICML 2021)

UnICORNN (Undamped Independent Controlled Oscillatory RNN) [ICML 2021] This repository contains the implementation to reproduce the numerical experime

Konstantin Rusch 21 Dec 22, 2022
This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset.

DeepLab-ResNet-TensorFlow This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset. Up

19 Jan 16, 2022
KSAI Lite is a deep learning inference framework of kingsoft, based on tensorflow lite

KSAI Lite is a deep learning inference framework of kingsoft, based on tensorflow lite

80 Dec 27, 2022
KIDA: Knowledge Inheritance in Data Aggregation

KIDA: Knowledge Inheritance in Data Aggregation This project releases our 1st place solution on NeurIPS2021 ML4CO Dual Task. Slide and model weights a

24 Sep 08, 2022
This project aims to explore the deployment of Swin-Transformer based on TensorRT, including the test results of FP16 and INT8.

Swin Transformer This project aims to explore the deployment of SwinTransformer based on TensorRT, including the test results of FP16 and INT8. Introd

maggiez 87 Dec 21, 2022
This repository contains the source codes for the paper AtlasNet V2 - Learning Elementary Structures.

AtlasNet V2 - Learning Elementary Structures This work was build upon Thibault Groueix's AtlasNet and 3D-CODED projects. (you might want to have a loo

Théo Deprelle 123 Nov 11, 2022
[ICCV21] Official implementation of the "Social NCE: Contrastive Learning of Socially-aware Motion Representations" in PyTorch.

Social-NCE + CrowdNav Website | Paper | Video | Social NCE + Trajectron | Social NCE + STGCNN This is an official implementation for Social NCE: Contr

VITA lab at EPFL 125 Dec 23, 2022