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[Feature] use mdformat (open-mmlab#888)
* add pre-commit hook * update all mds * fix
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‎.pre-commit-config.yaml

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repos:
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- repo: https://gitlab.com/pycqa/flake8.git
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- repo: https://github.com/PyCQA/flake8
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rev: 3.7.9
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hooks:
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- id: flake8
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args: ["--remove"]
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- id: mixed-line-ending
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args: ["--fix=lf"]
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- repo: https://github.com/markdownlint/markdownlint
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rev: v0.11.0
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hooks:
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- id: markdownlint
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args: ["-r", "~MD002,~MD013,~MD029,~MD033,~MD034",
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"-t", "allow_different_nesting"]
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- repo: https://github.com/codespell-project/codespell
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rev: v2.1.0
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hooks:
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- id: codespell
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args: ["--skip", "*.ipynb,tools/data/hvu/label_map.json,configs/restorers/srresnet_srgan/*.md", "-L", "formating,te,nd,thre,Gool,gool"]
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args: ["--skip", "*.ipynb", "-L", "formating,theis"]
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- repo: https://github.com/executablebooks/mdformat
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rev: 0.7.14
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hooks:
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- id: mdformat
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args: ["--number"]
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additional_dependencies:
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- mdformat-gfm
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- mdformat_frontmatter
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- linkify-it-py
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- repo: local
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hooks:
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- id: update-model-index

‎README.md

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https://user-images.githubusercontent.com/12756472/158972813-d8d0f19c-f49c-4618-9967-52652726ef19.mp4
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### Major features
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- **Modular design**
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## News
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- [2022-04-01] v0.14.0 was released.
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- \[2022-04-01\] v0.14.0 was released.
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- Support TOFlow in video frame interpolation
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- [2022-03-01] v0.13.0 was released.
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- \[2022-03-01\] v0.13.0 was released.
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- Support CAIN
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- Support EDVR-L
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- Support running in Windows
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- [2022-02-11] Switch to **PyTorch 1.5+**. The compatibility to earlier versions of PyTorch will no longer be guaranteed.
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- \[2022-02-11\] Switch to **PyTorch 1.5+**. The compatibility to earlier versions of PyTorch will no longer be guaranteed.
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Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
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‎README_zh-CN.md

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https://user-images.githubusercontent.com/12756472/158972813-d8d0f19c-f49c-4618-9967-52652726ef19.mp4
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### 主要特性
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- **模块化设计**
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## 最新消息
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- [2022-04-01] v0.14.0 版本发布
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- \[2022-04-01\] v0.14.0 版本发布
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- 支持视频插帧算法 TOFlow
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- [2022-03-01] v0.13.0 版本发布
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- \[2022-03-01\] v0.13.0 版本发布
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- 支持 CAIN
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- 支持 EDVR-L
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- 支持在 Windows 系统中运行
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- [2022-02-11] 切换到 **PyTorch 1.5+**. 将不再保证与早期版本的 PyTorch 的兼容性
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- \[2022-02-11\] 切换到 **PyTorch 1.5+**. 将不再保证与早期版本的 PyTorch 的兼容性
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请查看 [changelog.md](docs/en/changelog.md) 以获取更多细节与发版记录
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‎configs/inpainting/deepfillv1/README.md

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Recent deep learning based approaches have shown promising results for the challenging task of inpainting large missing regions in an image. These methods can generate visually plausible image structures and textures, but often create distorted structures or blurry textures inconsistent with surrounding areas. This is mainly due to ineffectiveness of convolutional neural networks in explicitly borrowing or copying information from distant spatial locations. On the other hand, traditional texture and patch synthesis approaches are particularly suitable when it needs to borrow textures from the surrounding regions. Motivated by these observations, we propose a new deep generative model-based approach which can not only synthesize novel image structures but also explicitly utilize surrounding image features as references during network training to make better predictions. The model is a feed-forward, fully convolutional neural network which can process images with multiple holes at arbitrary locations and with variable sizes during the test time. Experiments on multiple datasets including faces (CelebA, CelebA-HQ), textures (DTD) and natural images (ImageNet, Places2) demonstrate that our proposed approach generates higher-quality inpainting results than existing ones.
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<!-- [IMAGE] -->
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<div align=center >
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<img src="https://user-images.githubusercontent.com/12726765/144174665-9675931f-e448-4475-a659-99b65e7d4a64.png" width="400"/>
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</div >
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**Places365-Challenge**
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| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | Download |
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| :--------------------------------------------------------------------------: | :---------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | Download |
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| :---------------------------------------------------------------------------: | :---------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| [DeepFillv1](/configs/inpainting/deepfillv1/deepfillv1_256x256_8x2_places.py) | square bbox | 256x256 | 3500k | Places365-val | 11.019 | 23.429 | 0.862 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.log.json) |
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**CelebA-HQ**
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| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | Download |
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| :--------------------------------------------------------------------------: | :---------: | :--------: | :---------: | :--------: | :------: | :----: | :---: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | Download |
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| :---------------------------------------------------------------------------: | :---------: | :--------: | :---------: | :--------: | :------: | :----: | :---: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| [DeepFillv1](/configs/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba.py) | square bbox | 256x256 | 1500k | CelebA-val | 6.677 | 26.878 | 0.911 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.log.json) |
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## Citation
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```bibtex

‎configs/inpainting/deepfillv1/README_zh-CN.md

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**Places365-Challenge**
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| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | 下载 |
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| :--------------------------------------------------------------------------: | :---------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| [DeepFillv1](/configs/inpainting/deepfillv1/deepfillv1_256x256_8x2_places.py) | square bbox | 256x256 | 3500k | Places365-val | 11.019 | 23.429 | 0.862 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.log.json) |
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| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | 下载 |
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| :---------------------------------------------------------------------------: | :---------: | :-----: | :---: | :-----------: | :----: | :----: | :---: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| [DeepFillv1](/configs/inpainting/deepfillv1/deepfillv1_256x256_8x2_places.py) | square bbox | 256x256 | 3500k | Places365-val | 11.019 | 23.429 | 0.862 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.log.json) |
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**CelebA-HQ**
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| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | 下载 |
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| :--------------------------------------------------------------------------: | :---------: | :--------: | :---------: | :--------: | :------: | :----: | :---: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| [DeepFillv1](/configs/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba.py) | square bbox | 256x256 | 1500k | CelebA-val | 6.677 | 26.878 | 0.911 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.log.json) |
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| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | 下载 |
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| :---------------------------------------------------------------------------: | :---------: | :-----: | :---: | :--------: | :---: | :----: | :---: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| [DeepFillv1](/configs/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba.py) | square bbox | 256x256 | 1500k | CelebA-val | 6.677 | 26.878 | 0.911 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.log.json) |

‎configs/inpainting/deepfillv2/README.md

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We present a generative image inpainting system to complete images with free-form mask and guidance. The system is based on gated convolutions learned from millions of images without additional labelling efforts. The proposed gated convolution solves the issue of vanilla convolution that treats all input pixels as valid ones, generalizes partial convolution by providing a learnable dynamic feature selection mechanism for each channel at each spatial location across all layers. Moreover, as free-form masks may appear anywhere in images with any shape, global and local GANs designed for a single rectangular mask are not applicable. Thus, we also present a patch-based GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminator on dense image patches. SN-PatchGAN is simple in formulation, fast and stable in training. Results on automatic image inpainting and user-guided extension demonstrate that our system generates higher-quality and more flexible results than previous methods. Our system helps user quickly remove distracting objects, modify image layouts, clear watermarks and edit faces.
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<img src="https://user-images.githubusercontent.com/12726765/144175160-75473789-924f-490b-ab25-4c4f252fa55f.png" width="400"/>
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**Places365-Challenge**
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| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | Download |
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| :--------------------------------------------------------------------------: | :-------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | Download |
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| :---------------------------------------------------------------------------: | :-------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| [DeepFillv2](/configs/inpainting/deepfillv2/deepfillv2_256x256_8x2_places.py) | free-form | 256x256 | 100k | Places365-val | 8.635 | 22.398 | 0.815 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.log.json) |
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| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | Download |
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| :--------------------------------------------------------------------------: | :-------: | :--------: | :---------: | :--------: | :------: | :----: | :---: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | Download |
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| :---------------------------------------------------------------------------: | :-------: | :--------: | :---------: | :--------: | :------: | :----: | :---: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| [DeepFillv2](/configs/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba.py) | free-form | 256x256 | 20k | CelebA-val | 5.411 | 25.721 | 0.871 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.log.json) |
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## Citation
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```bibtex

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