mirror of
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[Fix] README for mmseg 1.x (#2009)
* [Fix] README for mmseg 1.x * typo * link and refine
This commit is contained in:
Miao Zheng
GitHub
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.github/CONTRIBUTING.md
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.github/CONTRIBUTING.md
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@ -1,23 +1,21 @@
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# Contributing to mmsegmentation
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# Contributing to MMSegmentation 1.x
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All kinds of contributions are welcome, including but not limited to the following.
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- Fixes (typo, bugs)
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- New features and components
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- Fix typo or bugs
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- Add documentation or translate the documentation into other languages
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- Add new features and components
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## Workflow
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1. fork and pull the latest mmsegmentation
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2. checkout a new branch (do not use master branch for PRs)
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1. fork and pull the latest MMSegmentation repository
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2. checkout a new branch from 'dev-1.x' (do not use master branch for PRs)
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3. commit your changes
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4. create a PR
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:::{note}
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- If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first.
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- If you are the author of some papers and would like to include your method to mmsegmentation,
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please contact Kai Chen (chenkaidev\[at\]gmail\[dot\]com). We will much appreciate your contribution.
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:::
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```{note}
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If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first.
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```
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## Code style
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@ -27,15 +25,18 @@ We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code
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We use the following tools for linting and formatting:
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- [flake8](http://flake8.pycqa.org/en/latest/): linter
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- [yapf](https://github.com/google/yapf): formatter
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- [isort](https://github.com/timothycrosley/isort): sort imports
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- [flake8](https://github.com/PyCQA/flake8): A wrapper around some linter tools.
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- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.
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- [yapf](https://github.com/google/yapf): A formatter for Python files.
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- [codespell](https://github.com/codespell-project/codespell): A Python utility to fix common misspellings in text files.
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- [mdformat](https://github.com/executablebooks/mdformat): Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files.
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- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring.
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Style configurations of yapf and isort can be found in [setup.cfg](../setup.cfg) and [.isort.cfg](../.isort.cfg).
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Style configurations of yapf and isort can be found in [setup.cfg](./setup.cfg).
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We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`,
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fixes `end-of-files`, sorts `requirments.txt` automatically on every commit.
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The config for a pre-commit hook is stored in [.pre-commit-config](../.pre-commit-config.yaml).
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We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`,
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fixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit.
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The config for a pre-commit hook is stored in [.pre-commit-config](./.pre-commit-config.yaml).
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After you clone the repository, you will need to install initialize pre-commit hook.
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94
README.md
94
README.md
@ -22,14 +22,14 @@
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[](https://pypi.org/project/mmsegmentation/)
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[](https://pypi.org/project/mmsegmentation)
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[](https://mmsegmentation.readthedocs.io/en/latest/)
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[](https://mmsegmentation.readthedocs.io/en/1.x/)
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[](https://github.com/open-mmlab/mmsegmentation/actions)
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[](https://codecov.io/gh/open-mmlab/mmsegmentation)
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[](https://github.com/open-mmlab/mmsegmentation/blob/master/LICENSE)
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[](https://github.com/open-mmlab/mmsegmentation/blob/1.x/LICENSE)
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[](https://github.com/open-mmlab/mmsegmentation/issues)
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[](https://github.com/open-mmlab/mmsegmentation/issues)
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Documentation: https://mmsegmentation.readthedocs.io/
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Documentation: https://mmsegmentation.readthedocs.io/en/1.x/
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English | [简体中文](README_zh-CN.md)
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@ -38,7 +38,7 @@ English | [简体中文](README_zh-CN.md)
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MMSegmentation is an open source semantic segmentation toolbox based on PyTorch.
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It is a part of the OpenMMLab project.
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The master branch works with **PyTorch 1.5+**.
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The 1.x branch works with **PyTorch 1.6+**.
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@ -60,14 +60,29 @@ The master branch works with **PyTorch 1.5+**.
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The training speed is faster than or comparable to other codebases.
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## License
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## What's New
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This project is released under the [Apache 2.0 license](LICENSE).
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v1.0.0rc0 was released in 31/8/2022.
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Please refer to [changelog.md](docs/en/notes/changelog.md) for details and release history.
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## Changelog
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- Unifies interfaces of all components based on MMEngine.
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- Faster training and testing speed with complete support of mixed precision training.
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- Refactored and more flexible architecture.
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v0.24.1 was released in 5/1/2022.
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Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
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## Installation
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Please refer to [get_started.md](docs/en/get_started.md#installation) for installation and [dataset_prepare.md](docs/en/user_guides/dataset_prepare.md#prepare-datasets) for dataset preparation.
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## Get Started
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Please see [Overview](docs/en/overview.md) for the general introduction of MMSegmentation.
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Please see [user guides](https://mmsegmentation.readthedocs.io/en/1.x/user_guides/index.html#) for the basic usage of MMSegmentation.
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There are also [advanced tutorials](https://mmsegmentation.readthedocs.io/en/dev-1.x/advanced_guides/index.html) for in-depth understanding of mmseg design and implementation .
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A Colab tutorial is also provided. You may preview the notebook [here](demo/MMSegmentation_Tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/1.x/demo/MMSegmentation_Tutorial.ipynb) on Colab.
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To migrate from MMSegmentation 1.x, please refer to [migration](docs/en/migration.md).
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## Benchmark and model zoo
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@ -127,36 +142,35 @@ Supported methods:
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Supported datasets:
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- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#cityscapes)
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- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-voc)
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- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#ade20k)
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- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-context)
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- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#coco-stuff-10k)
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- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#coco-stuff-164k)
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- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#chase-db1)
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- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#drive)
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- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#hrf)
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- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#stare)
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- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#dark-zurich)
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- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#nighttime-driving)
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- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#loveda)
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- [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isprs-potsdam)
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- [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isprs-vaihingen)
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- [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isaid)
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- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#cityscapes)
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- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#pascal-voc)
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- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#ade20k)
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- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#pascal-context)
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- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#coco-stuff-10k)
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- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#coco-stuff-164k)
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- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#chase-db1)
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- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#drive)
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- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#hrf)
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- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#stare)
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- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#dark-zurich)
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- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#nighttime-driving)
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- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#loveda)
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- [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#isprs-potsdam)
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- [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#isprs-vaihingen)
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- [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#isaid)
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## Installation
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Please refer to [FAQ](docs/en/notes/faq.md) for frequently asked questions.
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Please refer to [get_started.md](docs/en/get_started.md#installation) for installation and [dataset_prepare.md](docs/en/dataset_prepare.md#prepare-datasets) for dataset preparation.
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## Contributing
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## Get Started
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We appreciate all contributions to improve MMSegmentation. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
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Please see [train.md](docs/en/train.md) and [inference.md](docs/en/inference.md) for the basic usage of MMSegmentation.
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There are also tutorials for [customizing dataset](docs/en/tutorials/customize_datasets.md), [designing data pipeline](docs/en/tutorials/data_pipeline.md), [customizing modules](docs/en/tutorials/customize_models.md), and [customizing runtime](docs/en/tutorials/customize_runtime.md).
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We also provide many [training tricks](docs/en/tutorials/training_tricks.md) for better training and [useful tools](docs/en/useful_tools.md) for deployment.
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## Acknowledgement
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A Colab tutorial is also provided. You may preview the notebook [here](demo/MMSegmentation_Tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb) on Colab.
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Please refer to [FAQ](docs/en/faq.md) for frequently asked questions.
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MMSegmentation is an open source project that welcome any contribution and feedback.
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We wish that the toolbox and benchmark could serve the growing research
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community by providing a flexible as well as standardized toolkit to reimplement existing methods
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and develop their own new semantic segmentation methods.
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## Citation
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@ -171,19 +185,13 @@ If you find this project useful in your research, please consider cite:
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}
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```
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## Contributing
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## License
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We appreciate all contributions to improve MMSegmentation. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
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## Acknowledgement
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MMSegmentation is an open source project that welcome any contribution and feedback.
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We wish that the toolbox and benchmark could serve the growing research
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community by providing a flexible as well as standardized toolkit to reimplement existing methods
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and develop their own new semantic segmentation methods.
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This project is released under the [Apache 2.0 license](LICENSE).
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## Projects in OpenMMLab
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- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models
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- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
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- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
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- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
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@ -22,10 +22,10 @@
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[](https://pypi.org/project/mmsegmentation/)
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[](https://pypi.org/project/mmsegmentation)
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[](https://mmsegmentation.readthedocs.io/zh_CN/latest/)
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[](https://mmsegmentation.readthedocs.io/zh_CN/1.x/)
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[](https://github.com/open-mmlab/mmsegmentation/actions)
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[](https://codecov.io/gh/open-mmlab/mmsegmentation)
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[](https://github.com/open-mmlab/mmsegmentation/blob/master/LICENSE)
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[](https://github.com/open-mmlab/mmsegmentation/blob/1.x/LICENSE)
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[](https://github.com/open-mmlab/mmsegmentation/issues)
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[](https://github.com/open-mmlab/mmsegmentation/issues)
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@ -37,7 +37,7 @@
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MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 OpenMMLab 项目的一部分。
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主分支代码目前支持 PyTorch 1.5 以上的版本。
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1.x 分支代码目前支持 PyTorch 1.6 以上的版本。
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@ -59,14 +59,24 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
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训练速度比其他语义分割代码库更快或者相当。
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## 开源许可证
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该项目采用 [Apache 2.0 开源许可证](LICENSE)。
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## 更新日志
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最新版本 v0.24.1 在 2022.5.1 发布。
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如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/en/changelog.md)。
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最新版本 v1.0.0rc0 在 2022.8.31 发布。
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如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/en/notes/changelog.md)。
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## 安装
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请参考[快速入门文档](docs/zh_cn/get_started.md#installation)进行安装，参考[数据集准备](docs/zh_cn/user_guides/2_dataset_prepare.md)处理数据。
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## 快速入门
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请参考[概述](docs/zh_cn/overview.md)对 MMSegmetation 进行初步了解
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请参考[用户指南](https://mmsegmentation.readthedocs.io/zh_CN/1.x/user_guides/index.html)了解 mmseg 的基本使用，以及[进阶指南](https://mmsegmentation.readthedocs.io/zh_CN/1.x/advanced_guides/index.html)深入了解 mmseg 设计和代码实现。
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同时，我们提供了 Colab 教程。你可以在[这里](demo/MMSegmentation_Tutorial.ipynb)浏览教程，或者直接在 Colab 上[运行](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/1.x/demo/MMSegmentation_Tutorial.ipynb)。
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若需要将0.x版本的代码迁移至新版，请参考[迁移文档](docs/zh_cn/migration.md)。
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## 基准测试和模型库
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@ -126,36 +136,32 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
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已支持的数据集：
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- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#cityscapes)
|
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- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#pascal-voc)
|
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- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#ade20k)
|
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- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#pascal-context)
|
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- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#coco-stuff-10k)
|
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- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#coco-stuff-164k)
|
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- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#chase-db1)
|
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- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#drive)
|
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- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#hrf)
|
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- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#stare)
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- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#dark-zurich)
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- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#nighttime-driving)
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- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#loveda)
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- [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isprs-potsdam)
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- [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isprs-vaihingen)
|
||||
- [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isaid)
|
||||
- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#cityscapes)
|
||||
- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#pascal-voc)
|
||||
- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#ade20k)
|
||||
- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#pascal-context)
|
||||
- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#coco-stuff-10k)
|
||||
- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#coco-stuff-164k)
|
||||
- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#chase-db1)
|
||||
- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#drive)
|
||||
- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#hrf)
|
||||
- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#stare)
|
||||
- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#dark-zurich)
|
||||
- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#nighttime-driving)
|
||||
- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#loveda)
|
||||
- [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#isprs-potsdam)
|
||||
- [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#isprs-vaihingen)
|
||||
- [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#isaid)
|
||||
|
||||
## 安装
|
||||
如果遇到问题，请参考 [常见问题解答](docs/zh_cn/notes/faq.md)。
|
||||
|
||||
请参考[快速入门文档](docs/zh_cn/get_started.md#installation)进行安装，参考[数据集准备](docs/zh_cn/dataset_prepare.md)处理数据。
|
||||
## 贡献指南
|
||||
|
||||
## 快速入门
|
||||
我们感谢所有的贡献者为改进和提升 MMSegmentation 所作出的努力。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
|
||||
|
||||
请参考[训练教程](docs/zh_cn/train.md)和[测试教程](docs/zh_cn/inference.md)学习 MMSegmentation 的基本使用。
|
||||
我们也提供了一些进阶教程，内容覆盖了[增加自定义数据集](docs/zh_cn/tutorials/customize_datasets.md)，[设计新的数据预处理流程](docs/zh_cn/tutorials/data_pipeline.md)，[增加自定义模型](docs/zh_cn/tutorials/customize_models.md)，[增加自定义的运行时配置](docs/zh_cn/tutorials/customize_runtime.md)。
|
||||
除此之外，我们也提供了很多实用的[训练技巧说明](docs/zh_cn/tutorials/training_tricks.md)和模型部署相关的[有用的工具](docs/zh_cn/useful_tools.md)。
|
||||
## 致谢
|
||||
|
||||
同时，我们提供了 Colab 教程。你可以在[这里](demo/MMSegmentation_Tutorial.ipynb)浏览教程，或者直接在 Colab 上[运行](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb)。
|
||||
|
||||
如果遇到问题，请参考 [常见问题解答](docs/zh_cn/faq.md)。
|
||||
MMSegmentation 是一个由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。 我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现已有算法并开发自己的新模型，从而不断为开源社区提供贡献。
|
||||
|
||||
## 引用
|
||||
|
||||
@ -170,16 +176,13 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
|
||||
}
|
||||
```
|
||||
|
||||
## 贡献指南
|
||||
## 开源许可证
|
||||
|
||||
我们感谢所有的贡献者为改进和提升 MMSegmentation 所作出的努力。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
|
||||
|
||||
## 致谢
|
||||
|
||||
MMSegmentation 是一个由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。 我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现已有算法并开发自己的新模型，从而不断为开源社区提供贡献。
|
||||
该项目采用 [Apache 2.0 开源许可证](LICENSE)。
|
||||
|
||||
## OpenMMLab 的其他项目
|
||||
|
||||
- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab 深度学习模型训练库
|
||||
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
|
||||
- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
|
||||
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
|
||||
|
||||
@ -118,7 +118,7 @@ test_dataloader = dict(dataset=dataset_A_test)
|
||||
|
||||
```
|
||||
|
||||
You can refer base dataset [tutorial](TODO) from mmengine for more details
|
||||
You can refer base dataset [tutorial](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/basedataset.html) from mmengine for more details
|
||||
|
||||
### Multi-image Mix Dataset
|
||||
|
||||
|
||||
@ -5,6 +5,7 @@ Welcome to MMSegmentation's documentation!
|
||||
:maxdepth: 1
|
||||
:caption: Get Started
|
||||
|
||||
overview.md
|
||||
get_started.md
|
||||
|
||||
.. toctree::
|
||||
@ -43,7 +44,6 @@ Welcome to MMSegmentation's documentation!
|
||||
|
||||
notes/changelog.md
|
||||
notes/faq.md
|
||||
notes/CONTRIBUTING.md
|
||||
|
||||
.. toctree::
|
||||
:caption: Switch Language
|
||||
|
||||
@ -1,59 +0,0 @@
|
||||
# Contributing to MMSegmentation 1.x
|
||||
|
||||
All kinds of contributions are welcome, including but not limited to the following.
|
||||
|
||||
- Fix typo or bugs
|
||||
- Add documentation or translate the documentation into other languages
|
||||
- Add new features and components
|
||||
|
||||
## Workflow
|
||||
|
||||
1. fork and pull the latest MMSegmentation repository
|
||||
2. checkout a new branch from 'dev-1.x' (do not use master branch for PRs)
|
||||
3. commit your changes
|
||||
4. create a PR
|
||||
|
||||
```{note}
|
||||
If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first.
|
||||
```
|
||||
|
||||
## Code style
|
||||
|
||||
### Python
|
||||
|
||||
We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
|
||||
|
||||
We use the following tools for linting and formatting:
|
||||
|
||||
- [flake8](https://github.com/PyCQA/flake8): A wrapper around some linter tools.
|
||||
- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.
|
||||
- [yapf](https://github.com/google/yapf): A formatter for Python files.
|
||||
- [codespell](https://github.com/codespell-project/codespell): A Python utility to fix common misspellings in text files.
|
||||
- [mdformat](https://github.com/executablebooks/mdformat): Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files.
|
||||
- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring.
|
||||
|
||||
Style configurations of yapf and isort can be found in [setup.cfg](./setup.cfg).
|
||||
|
||||
We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`,
|
||||
fixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit.
|
||||
The config for a pre-commit hook is stored in [.pre-commit-config](./.pre-commit-config.yaml).
|
||||
|
||||
After you clone the repository, you will need to install initialize pre-commit hook.
|
||||
|
||||
```shell
|
||||
pip install -U pre-commit
|
||||
```
|
||||
|
||||
From the repository folder
|
||||
|
||||
```shell
|
||||
pre-commit install
|
||||
```
|
||||
|
||||
After this on every commit check code linters and formatter will be enforced.
|
||||
|
||||
> Before you create a PR, make sure that your code lints and is formatted by yapf.
|
||||
|
||||
### C++ and CUDA
|
||||
|
||||
We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).
|
||||
1
docs/en/overview.md
Normal file
1
docs/en/overview.md
Normal file
@ -0,0 +1 @@
|
||||
# Overview
|
||||
@ -17,7 +17,7 @@ For example, if some modification is made base on DeepLabV3, user may first inhe
|
||||
|
||||
If you are building an entirely new method that does not share the structure with any of the existing methods, you may create a folder `xxxnet` under `configs`,
|
||||
|
||||
Please refer to [mmengine](TODO) for detailed documentation.
|
||||
Please refer to [mmengine](https://mmengine.readthedocs.io/en/latest/tutorials/config.html) for detailed documentation.
|
||||
|
||||
## Config Name Style
|
||||
|
||||
@ -231,7 +231,7 @@ resume = False # Whether to resume from existed model.
|
||||
### Ignore some fields in the base configs
|
||||
|
||||
Sometimes, you may set `_delete_=True` to ignore some of the fields in base configs.
|
||||
You may refer to [mmengine](TODO) for simple illustration.
|
||||
You may refer to [mmengine](https://mmengine.readthedocs.io/en/latest/tutorials/config.html) for simple illustration.
|
||||
|
||||
In MMSegmentation, for example, to change the backbone of PSPNet with the following config.
|
||||
|
||||
|
||||
@ -38,7 +38,7 @@ Find the `vis_data` path of `work_dir` after starting training, for example, the
|
||||
work_dirs/test_visual/20220810_115248/vis_data
|
||||
```
|
||||
|
||||
The scalar file in vis_data path includes learning rate, losses and data_time etc, also record metrics results and you can refer [logging tutorial](TODO) in mmengine to log custom data. The tensorboard visualization results are executed with the following command:
|
||||
The scalar file in vis_data path includes learning rate, losses and data_time etc, also record metrics results and you can refer [logging tutorial](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/logging.html) in mmengine to log custom data. The tensorboard visualization results are executed with the following command:
|
||||
|
||||
```shell
|
||||
tensorboard --logdir work_dirs/test_visual/20220810_115248/vis_data
|
||||
@ -76,4 +76,4 @@ we can also run the following command to view them in TensorBoard:
|
||||
tensorboard --logdir work_dirs/test_visual/20220810_115248/vis_data
|
||||
```
|
||||
|
||||
If you would like to know more visualization usage, you can refer to [visualization tutorial](todo) in mmengie.
|
||||
If you would like to know more visualization usage, you can refer to [visualization tutorial](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/visualization.html) in mmengie.
|
||||
|
||||
@ -9,13 +9,13 @@
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: 用户文档
|
||||
:caption: 用户指南
|
||||
|
||||
user_guides/index.rst
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: 进阶文档
|
||||
:caption: 进阶指南
|
||||
|
||||
advanced_guides/index.rst
|
||||
|
||||
|
||||
1
docs/zh_cn/migration.md
Normal file
1
docs/zh_cn/migration.md
Normal file
@ -0,0 +1 @@
|
||||
# 迁移文档
|
||||
1
docs/zh_cn/overview.md
Normal file
1
docs/zh_cn/overview.md
Normal file
@ -0,0 +1 @@
|
||||
# 概述
|
||||
Loading…
x
Reference in New Issue
Block a user