Merge branch '1.x' of github.com:open-mmlab/mmgeneration into plyfager/restart-publish

Author: plyfager

.circleci/config.yml

@@ -26,7 +26,7 @@ workflows:
             tools/.* lint_only false
             configs/.* lint_only false
             .circleci/.* lint_only false
-          base-revision: dev-1.x
+          base-revision: 1.x
           # this is the path of the configuration we should trigger once
           # path filtering and pipeline parameter value updates are
           # complete. In this case, we are using the parent dynamic

.circleci/test.yml

@@ -145,15 +145,15 @@ workflows:
           torch: 1.6.0
           torchvision: 0.7.0
           python: 3.6.9  # The lowest python 3.6.x version available on CircleCI images
-          mmcv: https://download.openmmlab.com/mmcv/dev-2.x/cpu/torch1.6.0/mmcv_full-2.0.0rc0-cp36-cp36m-manylinux1_x86_64.whl
+          mmcv: https://download.openmmlab.com/mmcv/dist/cpu/torch1.6.0/mmcv-2.0.0rc1-cp36-cp36m-manylinux1_x86_64.whl
           requires:
             - lint
       - build_cpu:
           name: maximum_version_cpu
           torch: 1.9.0
           torchvision: 0.10.0
           python: 3.9.0
-          mmcv: https://download.openmmlab.com/mmcv/dev-2.x/cpu/torch1.9.0/mmcv_full-2.0.0rc0-cp39-cp39-manylinux1_x86_64.whl
+          mmcv: https://download.openmmlab.com/mmcv/dist/cpu/torch1.9.0/mmcv-2.0.0rc1-cp39-cp39-manylinux1_x86_64.whl
           requires:
             - minimum_version_cpu
       - hold:
@@ -166,7 +166,7 @@ workflows:
           # Use double quotation mark to explicitly specify its type
           # as string instead of number
           cuda: "10.2"
-          mmcv: https://download.openmmlab.com/mmcv/dev-2.x/cu102/torch1.8.0/mmcv_full-2.0.0rc0-cp37-cp37m-manylinux1_x86_64.whl
+          mmcv: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/mmcv-2.0.0rc1-cp37-cp37m-manylinux1_x86_64.whl
           requires:
             - hold
   merge_stage_test:
@@ -179,7 +179,7 @@ workflows:
           torch: 1.6.0
           # Use double quotation mark to explicitly specify its type
           # as string instead of number
-          mmcv: https://download.openmmlab.com/mmcv/dev-2.x/cu101/torch1.6.0/mmcv_full-2.0.0rc0-cp37-cp37m-manylinux1_x86_64.whl
+          mmcv: https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/mmcv-2.0.0rc1-cp37-cp37m-manylinux1_x86_64.whl
           cuda: "10.1"
           filters:
             branches:

LICENSES.md

@@ -2,7 +2,7 @@
 
 In this file, we list the operations with other licenses instead of Apache 2.0. Users should be careful about adopting these operations in any commercial matters.
 
-|      Operation       |                                                                 Files                                                                 |    License     |
-| :------------------: | :-----------------------------------------------------------------------------------------------------------------------------------: | :------------: |
-|    conv2d_gradfix    |           [mmgen/ops/conv2d_gradfix.py](https://github.com/open-mmlab/mmgeneration/blob/master/mmgen/ops/conv2d_gradfix.py)           | NVIDIA License |
-| compute_pr_distances | [mmgen/core/evaluation/metric_utils.py](https://github.com/open-mmlab/mmgeneration/blob/master/mmgen/core/evaluation/metric_utils.py) | NVIDIA License |
+|      Operation       |                                                               Files                                                                |    License     |
+| :------------------: | :--------------------------------------------------------------------------------------------------------------------------------: | :------------: |
+|    conv2d_gradfix    |           [mmgen/ops/conv2d_gradfix.py](https://github.com/open-mmlab/mmgeneration/blob/1.x/mmgen/ops/conv2d_gradfix.py)           | NVIDIA License |
+| compute_pr_distances | [mmgen/core/evaluation/metric_utils.py](https://github.com/open-mmlab/mmgeneration/blob/1.x/mmgen/core/evaluation/metric_utils.py) | NVIDIA License |

README.md

@@ -20,36 +20,36 @@
 </div>
 
 [![PyPI](https://img.shields.io/pypi/v/mmgen)](https://pypi.org/project/mmgen)
-[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmgeneration.readthedocs.io/en/latest/)
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmgeneration.readthedocs.io/en/1.x/)
 [![badge](https://github.com/open-mmlab/mmgeneration/workflows/build/badge.svg)](https://github.com/open-mmlab/mmgeneration/actions)
-[![codecov](https://codecov.io/gh/open-mmlab/mmgeneration/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmgeneration)
-[![license](https://img.shields.io/github/license/open-mmlab/mmgeneration.svg)](https://github.com/open-mmlab/mmgeneration/blob/master/LICENSE)
+[![codecov](https://codecov.io/gh/open-mmlab/mmgeneration/branch/1.x/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmgeneration)
+[![license](https://img.shields.io/github/license/open-mmlab/mmgeneration.svg)](https://github.com/open-mmlab/mmgeneration/blob/1.x/LICENSE)
 [![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmgeneration.svg)](https://github.com/open-mmlab/mmgeneration/issues)
 [![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmgeneration.svg)](https://github.com/open-mmlab/mmgeneration/issues)
 
-[📘Documentation](https://mmgeneration.readthedocs.io/en/latest/) |
-[🛠️Installation](https://mmgeneration.readthedocs.io/en/latest/get_started.html#installation) |
-[👀Model Zoo](https://mmgeneration.readthedocs.io/en/latest/modelzoo_statistics.html) |
-[🆕Update News](https://github.com/open-mmlab/mmgeneration/blob/master/docs/en/changelog.md) |
+[📘Documentation](https://mmgeneration.readthedocs.io/en/1.x/) |
+[🛠️Installation](https://mmgeneration.readthedocs.io/en/1.x/get_started.html#installation) |
+[👀Model Zoo](https://mmgeneration.readthedocs.io/en/1.x/modelzoo_statistics.html) |
+[🆕Update News](https://github.com/open-mmlab/mmgeneration/blob/1.x/docs/en/notes/changelog.md) |
 [🚀Ongoing Projects](https://github.com/open-mmlab/mmgeneration/projects) |
 [🤔Reporting Issues](https://github.com/open-mmlab/mmgeneration/issues)
 
 English | [简体中文](README_zh-CN.md)
 
 ## Introduction
 
-MMGeneration is a powerful toolkit for generative models, especially for GANs now. It is based on PyTorch and [MMCV](https://github.com/open-mmlab/mmcv). The master branch works with **PyTorch 1.5+**.
+MMGeneration is a powerful toolkit for generative models, especially for GANs now. It is based on PyTorch and [MMCV](https://github.com/open-mmlab/mmcv/tree/2.x). The master branch works with **PyTorch 1.5+**.
 
 <div align="center">
     <img src="https://user-images.githubusercontent.com/12726765/114534478-9a65a900-9c81-11eb-8087-de8b6816eed8.png" width="800"/>
 </div>
 
 ## 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](docs/en/tutorials/applications.md))
-- **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](docs/en/tutorials/ddp_train_gans.md))
-- **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](docs/en/tutorials/customize_losses.md))
+- **High-quality Training Performance:** MMGeneration currently support training on Unconditional GANs, Conditional GANs, Internal GANs, Image Translation Models, and Diffusion Models.
+- **Powerful Application Toolkit:** A toolkit that provides plentiful applications to users. MMGeneration supports GAN interpolation, GAN projection, GAN manipulations and many other popular GAN's applications. It's time to play with your GANs! ([Tutorial for applications](docs/en/advanced_guides/applications.md))
+- **Efficient Distributed Training for Generative Models:** With support of [MMSeparateDistributedDataParallel](https://github.com/open-mmlab/mmengine/blob/main/mmengine/model/wrappers/seperate_distributed.py), distributed training for dynamic architectures can be easily implemented.
+- **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 [losses](docs/en/advanced_guides/losses.md))
 
 <table>
 <thead>
@@ -88,13 +88,22 @@ MMGeneration is a powerful toolkit for generative models, especially for GANs no
 - Conditional GANs have been supported in our toolkit. More methods and pre-trained weights will come soon.
 - Mixed-precision training (FP16) for StyleGAN2 has been supported. Please check [the comparison](configs/styleganv2/README.md) between different implementations.
 
-## Changelog
+## What's new
 
-v0.7.1 was released on 30/04/2022. Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
+v1.0.0rc0 was released in 31/8/2022.
+
+This release introduced a brand new and flexible training & test engine, but it's still in progress. Welcome
+to try according to [the documentation](https://mmgeneration.readthedocs.io/en/1.x/).
+
+And there are some BC-breaking changes. Please check [the migration tutorial](https://mmgeneration.readthedocs.io/en/1.x/migration.html).
+
+The release candidate will last until the end of 2022, and during the release candidate, we will develop on the `1.x` branch. And we will still maintain 0.x version still at least the end of 2023.
+
+Please refer to [changelog.md](https://mmgeneration.readthedocs.io/en/1.x/notes/changelog.html) for more details and other release history.
 
 ## Installation
 
-MMGeneration depends on [PyTorch](https://pytorch.org/) and [MMCV](https://github.com/open-mmlab/mmcv).
+MMGeneration depends on [PyTorch](https://pytorch.org/) and [MMCV](https://github.com/open-mmlab/mmcv/tree/2.x).
 Below are quick steps for installation.
 
 **Step 1.**
@@ -109,15 +118,16 @@ pip3 install torch torchvision
 Install MMCV with [MIM](https://github.com/open-mmlab/mim).
 
 ```
-pip3 install openmim
-mim install mmcv-full
+pip install -U openmim
+# wait for more pre-compiled pkgs to release
+mim install 'mmcv>=2.0.0rc1'
 ```
 
 **Step 3.**
 Install MMGeneration from source.
 
 ```
-git clone https://github.com/open-mmlab/mmgeneration.git
+git clone -b 1.x https://github.com/open-mmlab/mmgeneration.git
 cd mmgeneration
 pip3 install -e .[all]
 ```
@@ -126,7 +136,7 @@ Please refer to [get_started.md](docs/en/get_started.md) for more detailed instr
 
 ## Getting Started
 
-Please see [get_started.md](docs/en/get_started.md) for the basic usage of MMGeneration. [docs/en/quick_run.md](docs/en/quick_run.md) can offer full guidance for quick run. For other details and tutorials, please go to our [documentation](https://mmgeneration.readthedocs.io/).
+Please see [get_started.md](docs/en/get_started.md) for the basic usage of MMGeneration. For other details and tutorials, please go to our [documentation](https://mmgeneration.readthedocs.io/en/1.x/).
 
 ## ModelZoo
 
@@ -185,7 +195,7 @@ These methods have been carefully studied and supported in our frameworks:
 
 ## Contributing
 
-We appreciate all contributions to improve MMGeneration. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/CONTRIBUTING.md) in MMCV for more details about the contributing guideline.
+We appreciate all contributions to improve MMGeneration. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/tree/2.x/CONTRIBUTING.md) in MMCV and \[https://github.com/open-mmlab/mmengine/blob/main/CONTRIBUTING.md\] in MMEngine for more details about the contributing guideline.
 
 ## Citation
 
@@ -204,24 +214,25 @@ If you find this project useful in your research, please consider cite:
 
 This project is released under the [Apache 2.0 license](LICENSE). Some operations in `MMGeneration` are with other licenses instead of Apache2.0. Please refer to [LICENSES.md](LICENSES.md) for the careful check, if you are using our code for commercial matters.
 
-## Projects in OpenMMLab
+## Projects in OpenMMLab 2.0
 
-- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
+- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
+- [MMCV](https://github.com/open-mmlab/mmcv/tree/2.x): OpenMMLab foundational library for computer vision.
 - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
-- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
-- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
-- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
-- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
-- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
-- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
-- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
-- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
-- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
-- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
-- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
-- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
-- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
-- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
-- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
-- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
+- [MMClassification](https://github.com/open-mmlab/mmclassification/tree/1.x): OpenMMLab image classification toolbox and benchmark.
+- [MMDetection](https://github.com/open-mmlab/mmdetection/tree/3.x): OpenMMLab detection toolbox and benchmark.
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d/tree/1.x): OpenMMLab's next-generation platform for general 3D object detection.
+- [MMRotate](https://github.com/open-mmlab/mmrotate/tree/1.x): OpenMMLab rotated object detection toolbox and benchmark.
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation/tree/1.x): OpenMMLab semantic segmentation toolbox and benchmark.
+- [MMOCR](https://github.com/open-mmlab/mmocr/tree/1.x): OpenMMLab text detection, recognition, and understanding toolbox.
+- [MMPose](https://github.com/open-mmlab/mmpose/tree/1.x): OpenMMLab pose estimation toolbox and benchmark.
+- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d/tree/1.x): OpenMMLab 3D human parametric model toolbox and benchmark.
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup/tree/1.x): OpenMMLab self-supervised learning toolbox and benchmark.
+- [MMRazor](https://github.com/open-mmlab/mmrazor/tree/1.x): OpenMMLab model compression toolbox and benchmark.
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot/tree/1.x): OpenMMLab fewshot learning toolbox and benchmark.
+- [MMAction2](https://github.com/open-mmlab/mmaction2/tree/1.x): OpenMMLab's next-generation action understanding toolbox and benchmark.
+- [MMTracking](https://github.com/open-mmlab/mmtracking/tree/1.x): OpenMMLab video perception toolbox and benchmark.
+- [MMFlow](https://github.com/open-mmlab/mmflow/tree/1.x): OpenMMLab optical flow toolbox and benchmark.
+- [MMEditing](https://github.com/open-mmlab/mmediting/tree/1.x): OpenMMLab image and video editing toolbox.
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration/tree/1.x): OpenMMLab image and video generative models toolbox.
 - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.