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SwinT-ChARM

★ 47 Open GitHub ↗

TensorFlow implementation of SwinT-ChARM (Transformer-Based Transform Coding, ICLR 2022)

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# SwinT-ChARM (TensorFlow 2)

[<img src="https://colab.research.google.com/assets/colab-badge.svg" align="center">](https://colab.research.google.com/drive/1w42soP9daxok4f6jvh3nen1DX0z4KSRb?usp=sharing)

This repository provides a TensorFlow implementation of SwinT-ChARM based on:

- [Transformer-Based Transform Coding (ICLR 2022)](https://openreview.net/pdf?id=IDwN6xjHnK8),
- [Channel-wise Autoregressive Entropy Models For Learned Image Compression (ICIP 2020)](https://arxiv.org/pdf/2007.08739.pdf).

![SwinT-ChARM net arch](https://github.com/Nikolai10/SwinT-ChARM/blob/master/res/doc/figures/teaser.png)
<sup>
[Source](https://openreview.net/pdf?id=IDwN6xjHnK8)
</sup>

## Updates

***10/06/2023***
1. LIC-TCM (TensorFlow 2) is now available: https://github.com/Nikolai10/LIC-TCM (Liu et al. CVPR 2023 Highlight).

***09/06/2023***
1. The high quality of this reimplementation has been confirmed in [EGIC, Section A.8.](https://arxiv.org/pdf/2309.03244v1.pdf).

***10/09/2022***

1. The number of model parameters now corresponds exactly to the reported number (32.6 million). We thank the authors for providing us with the official DeepSpeed log files.
2. SwinT-ChARM now supports compression at different input resolutions (multiples of 256).
3. We release a pre-trained model as proof of functional correctness.

***08/17/2022***

1. Initial release of this project (see branch *release_08/17/2022*)

## Acknowledgment
This project is based on:

- [TensorFlow Compression (TFC)](https://github.com/tensorflow/compression), a TF library dedicated to data compression.
- [swin-transformers-tf](https://github.com/sayakpaul/swin-transformers-tf), an unofficial implementation of [Swin-Transformer](https://github.com/microsoft/Swin-Transformer). Functional correctness has been [proven](https://github.com/microsoft/Swin-Transformer/pull/206).

Note that this repository builds upon the official TF implementation of [Minnen et al.](https://github.com/tensorflow/compression/blob/master/models/ms2020.py), while Zhu et al. base their work on an
unknown (possibly not publicly available) PyTorch reimplementation.

## Examples

The samples below are taken from the [Kodak dataset](http://r0k.us/graphics/kodak/), external to the training set:

Original | SwinT-ChARM (β = 0.0003)
:-------------------------:|:-------------------------:
![kodim22.png](https://github.com/Nikolai10/SwinT-ChARM/blob/master/res/eval/kodim22.png) | ![kodim22_hat.png](https://github.com/Nikolai10/SwinT-ChARM/blob/master/res/eval/kodim22_hat.png)

```python
Mean squared error: 13.7772
PSNR (dB): 36.74
Multiscale SSIM: 0.9871
Multiscale SSIM (dB): 18.88
Bits per pixel: 0.9890
```

Original | SwinT-ChARM (β = 0.0003)
:-------------------------:|:-------------------------:
![kodim23.png](https://github.com/Nikolai10/SwinT-ChARM/blob/master/res/eval/kodim23.png) | ![kodim23_hat.png](https://github.com/Nikolai10/SwinT-ChARM/blob/master/res/eval/kodim23_hat.png)

```python
Mean squared error: 7.1963
PSNR (dB): 39.56
Multiscale SSIM: 0.9903
Multiscale SSIM (dB): 20.13
Bits per pixel: 0.3953
```

Original | SwinT-ChARM (β = 0.0003)
:-------------------------:|:-------------------------:
![kodim15.png](https://github.com/Nikolai10/SwinT-ChARM/blob/master/res/eval/kodim15.png) | ![kodim15_hat.png](https://github.com/Nikolai10/SwinT-ChARM/blob/master/res/eval/kodim15_hat.png)

```python
Mean squared error: 10.1494
PSNR (dB): 38.07
Multiscale SSIM: 0.9888
Multiscale SSIM (dB): 19.49
Bits per pixel: 0.6525
```
More examples can be found [here](https://github.com/Nikolai10/SwinT-ChARM/blob/master/res/eval).

## Pretrained Models/ Performance (TFC 2.8)

Our pre-trained model (β = 0.0003) achieves a [PSNR of 37.59 (db) using an average of 0.93 bpp](https://github.com/Nikolai10/SwinT-ChARM/blob/master/res/eval/kodak_results.txt) on the [Kodak dataset](http://r0k.us/graphics/kodak/), which is very close to the reported numbers (see [paper](https://openreview.net/pdf?id=IDwN6xjHnK8), Figure 3). Worth mentioning: we achieve this result despite training our model from scratch and using less than one-third of the computational resources (1M optimization steps).

| Lagrangian multiplier (β) | SavedModel | Training Instructions |
| ----------- | -------------------------------- | ---------------------- |
| 0.0003 | [download](https://drive.google.com/drive/folders/1bUWowLEgU8ukYejvVPbhU38_7japhp7C?usp=sharing) | <pre lang=bash>`!python SwinT-ChARM/zyc2022.py -V --model_path <...> train --max_support_slices 10 --lambda 0.0003 --epochs 1000 --batchsize 16 --train_path <...>`</pre> |

## File Structure

res
         ├── doc/                                       # addtional resources
         ├── eval/                                      # sample images + reconstructions
         ├── train_zyc2022/                             # model checkpoints + tf.summaries
         ├── zyc2022/                                   # saved model
     swin-transformers-tf/                              # extended swin-transformers-tf implementation 
         ├── changelog.txt                              # summary of changes made to the original work
         ├── ...  
     config.py                                          # model-dependent configurations
     zyc2022.py                                         # core of this repo

## License
[Apache License 2.0](LICENSE)

SwinT-ChARM

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