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Add Swin2SR (#19784)

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* First draft

* Add more improvements

* Improve forward pass

* Fix layernorm

* Add upscaler

* More improvements

* More improvements

* More improvements

* Improve conversion script

* Add preprocessing

* Make output match original implementation

* Add additional attributes

* Add support for more models

* Support more models

* Add support for real world sr

* Add initial Swin2SRFeatureExtractor

* Add ImageSuperResolutionOutput

* Make more tests pass

* Use BaseModelOutput

* Fix one more test

* Fix more tests

* Fix another test

* Fix all tests

* Rename to Swin2SRImageProcessor

* Fix toctree

* Fix toctree

* Fix rebase

* Improve Swin2SRImageProcessor

* Remove feature extractor file

* Improve model

* Improve conversion script

* Fix integration test

* Fix init

* Fix conversion script

* Address comments

* Improve upsampler

* Add NearestConvUpsampler

* Improve pixel shuffle upsampler

* Improve auxiliary upsampler

* Improve conversion script

* Rename conv_last to final_convolution

* Fix rebase

* Improve upsample module

* Add padding to image processor

* Fix bug

* Update padding

* Remove print statement and fix integration test

* Improve docs

* Add image processor tests

* Convert all checkpoints, fix testsé

* Remove print statements

* Fix import

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
This commit is contained in:
NielsRogge
2022-12-16 16:24:01 +01:00
committed by GitHub
parent 7f99861218
commit 26dd041c6e
28 changed files with 2570 additions and 10 deletions
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docs/source/en/_toctree.yml
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@@ -438,6 +438,8 @@
title: Swin Transformer
- local: model_doc/swinv2
title: Swin Transformer V2
- local: model_doc/swin2sr
title: Swin2SR
- local: model_doc/table-transformer
title: Table Transformer
- local: model_doc/timesformer
@@ -564,4 +566,4 @@
- local: internal/file_utils
title: General Utilities
title: Internal Helpers
title: API
title: API

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@@ -175,6 +175,7 @@ The documentation is organized into five sections:
1. **[SqueezeBERT](model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
1. **[Swin Transformer](model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
1. **[Swin Transformer V2](model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
1. **[Swin2SR](model_doc/swin2sr)** (from University of Würzburg) released with the paper [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.
1. **[SwitchTransformers](model_doc/switch_transformers)** (from Google) released with the paper [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://arxiv.org/abs/2101.03961) by William Fedus, Barret Zoph, Noam Shazeer.
1. **[T5](model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
1. **[T5v1.1](model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
@@ -342,6 +343,7 @@ Flax), PyTorch, and/or TensorFlow.
| SqueezeBERT | ✅ | ✅ | ✅ | ❌ | ❌ |
| Swin Transformer | ❌ | ❌ | ✅ | ✅ | ❌ |
| Swin Transformer V2 | ❌ | ❌ | ✅ | ❌ | ❌ |
| Swin2SR | ❌ | ❌ | ✅ | ❌ | ❌ |
| SwitchTransformers | ❌ | ❌ | ✅ | ❌ | ❌ |
| T5 | ✅ | ✅ | ✅ | ✅ | ✅ |
| Table Transformer | ❌ | ❌ | ✅ | ❌ | ❌ |

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<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Swin2SR
## Overview
The Swin2SR model was proposed in [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.
Swin2R improves the [SwinIR](https://github.com/JingyunLiang/SwinIR/) model by incorporating [Swin Transformer v2](swinv2) layers which mitigates issues such as training instability, resolution gaps between pre-training
and fine-tuning, and hunger on data.
The abstract from the paper is the following:
*Compression plays an important role on the efficient transmission and storage of images and videos through band-limited systems such as streaming services, virtual reality or videogames. However, compression unavoidably leads to artifacts and the loss of the original information, which may severely degrade the visual quality. For these reasons, quality enhancement of compressed images has become a popular research topic. While most state-of-the-art image restoration methods are based on convolutional neural networks, other transformers-based methods such as SwinIR, show impressive performance on these tasks.
In this paper, we explore the novel Swin Transformer V2, to improve SwinIR for image super-resolution, and in particular, the compressed input scenario. Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data. We conduct experiments on three representative tasks: JPEG compression artifacts removal, image super-resolution (classical and lightweight), and compressed image super-resolution. Experimental results demonstrate that our method, Swin2SR, can improve the training convergence and performance of SwinIR, and is a top-5 solution at the "AIM 2022 Challenge on Super-Resolution of Compressed Image and Video".*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/swin2sr_architecture.png"
alt="drawing" width="600"/>
<small> Swin2SR architecture. Taken from the <a href="https://arxiv.org/abs/2209.11345">original paper.</a> </small>
This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/mv-lab/swin2sr).
## Resources
Demo notebooks for Swin2SR can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Swin2SR).
A demo Space for image super-resolution with SwinSR can be found [here](https://huggingface.co/spaces/jjourney1125/swin2sr).
## Swin2SRImageProcessor
[[autodoc]] Swin2SRImageProcessor
- preprocess
## Swin2SRConfig
[[autodoc]] Swin2SRConfig
## Swin2SRModel
[[autodoc]] Swin2SRModel
- forward
## Swin2SRForImageSuperResolution
[[autodoc]] Swin2SRForImageSuperResolution
- forward