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https://github.com/tensorlayer/SRGAN

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/tensorlayer/SRGAN

cnn gan srgan super-resolution tensorflow tensorlayer vgg vgg16 vgg19

Last synced: 13 days ago
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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Host: GitHub
URL: https://github.com/tensorlayer/SRGAN
Owner: tensorlayer
Created: 2017-04-20T17:08:27.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2024-02-22T02:05:20.000Z (9 months ago)
Last Synced: 2024-10-29T15:34:58.662Z (14 days ago)
Topics: cnn, gan, srgan, super-resolution, tensorflow, tensorlayer, vgg, vgg16, vgg19
Language: Python
Homepage: https://github.com/tensorlayer/tensorlayerx
Size: 145 MB
Stars: 3,314
Watchers: 98
Forks: 812
Open Issues: 149
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

        ## Super Resolution Examples

- Implementation of ["Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"](https://arxiv.org/abs/1609.04802)

- For earlier version, please check [srgan release](https://github.com/tensorlayer/srgan/releases) and [tensorlayer](https://github.com/tensorlayer/TensorLayer).

- For more computer vision applications, check [TLXCV](https://github.com/tensorlayer/TLXCV)

### SRGAN Architecture





	









	





### Prepare Data and Pre-trained VGG

- 1. You need to download the pretrained VGG19 model weights in [here](https://drive.google.com/file/d/1CLw6Cn3yNI1N15HyX99_Zy9QnDcgP3q7/view?usp=sharing).

- 2. You need to have the high resolution images for training.

  -  In this experiment, I used images from [DIV2K - bicubic downscaling x4 competition](http://www.vision.ee.ethz.ch/ntire17/), so the hyper-paremeters in `config.py` (like number of epochs) are seleted basic on that dataset, if you change a larger dataset you can reduce the number of epochs. 

  -  If you dont want to use DIV2K dataset, you can also use [Yahoo MirFlickr25k](http://press.liacs.nl/mirflickr/mirdownload.html), just simply download it using `train_hr_imgs = tl.files.load_flickr25k_dataset(tag=None)` in `main.py`. 

  -  If you want to use your own images, you can set the path to your image folder via `config.TRAIN.hr_img_path` in `config.py`.

### Run

🔥🔥🔥🔥🔥🔥 You need install [TensorLayerX](https://github.com/tensorlayer/TensorLayerX#installation) at first!

🔥🔥🔥🔥🔥🔥 Please install TensorLayerX via source

```bash

pip install git+https://github.com/tensorlayer/tensorlayerx.git 

```

#### Train

- Set your image folder in `config.py`, if you download [DIV2K - bicubic downscaling x4 competition](http://www.vision.ee.ethz.ch/ntire17/) dataset, you don't need to change it. 

- Other links for DIV2K, in case you can't find it : [test\_LR\_bicubic_X4](https://data.vision.ee.ethz.ch/cvl/DIV2K/validation_release/DIV2K_test_LR_bicubic_X4.zip), [train_HR](https://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip), [train\_LR\_bicubic_X4](https://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_LR_bicubic_X4.zip), [valid_HR](https://data.vision.ee.ethz.ch/cvl/DIV2K/validation_release/DIV2K_valid_HR.zip), [valid\_LR\_bicubic_X4](https://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_LR_bicubic_X4.zip).

```python

config.TRAIN.img_path = "your_image_folder/"

```

Your directory structure should look like this:

```

srgan/

    └── config.py

    └── srgan.py

    └── train.py

    └── vgg.py

    └── model

          └── vgg19.npy

    └── DIV2K

          └── DIV2K_train_HR

          ├── DIV2K_train_LR_bicubic

          ├── DIV2K_valid_HR

          └── DIV2K_valid_LR_bicubic

```

- Start training.

```bash

python train.py

```

🔥Modify a line of code in **train.py**, easily switch to any framework!

```python

import os

os.environ['TL_BACKEND'] = 'tensorflow'

# os.environ['TL_BACKEND'] = 'mindspore'

# os.environ['TL_BACKEND'] = 'paddle'

# os.environ['TL_BACKEND'] = 'pytorch'

```

🚧 We will support PyTorch as Backend soon.

#### Evaluation.

🔥 We have trained SRGAN on DIV2K dataset.

🔥 Download model weights as follows.

|              | SRGAN_g | SRGAN_d | 

|------------- |---------|---------|

| TensorFlow   | [Baidu](https://pan.baidu.com/s/118uUg3oce_3NZQCIWHVjmA?pwd=p9li), [Googledrive](https://drive.google.com/file/d/1GlU9At-5XEDilgnt326fyClvZB_fsaFZ/view?usp=sharing) |[Baidu](https://pan.baidu.com/s/1DOpGzDJY5PyusKzaKqbLOg?pwd=g2iy), [Googledrive](https://drive.google.com/file/d/1RpOtVcVK-yxnVhNH4KSjnXHDvuU_pq3j/view?usp=sharing)   |        

| PaddlePaddle | [Baidu](https://pan.baidu.com/s/1ngBpleV5vQZQqNE_8djDIg?pwd=s8wc), [Googledrive](https://drive.google.com/file/d/1GRNt_ZsgorB19qvwN5gE6W9a_bIPLkg1/view?usp=sharing)  | [Baidu](https://pan.baidu.com/s/1nSefLNRanFImf1DskSVpCg?pwd=befc), [Googledrive](https://drive.google.com/file/d/1Jf6W1ZPdgtmUSfrQ5mMZDB_hOCVU-zFo/view?usp=sharing)   |         

| MindSpore    | 🚧Coming soon!    | 🚧Coming soon!     |         

| PyTorch      | 🚧Coming soon!    | 🚧Coming soon!     |

Download weights file and put weights under the folder srgan/models/.

Your directory structure should look like this:

```

srgan/

    └── config.py

    └── srgan.py

    └── train.py

    └── vgg.py

    └── model

          └── vgg19.npy

    └── DIV2K

          ├── DIV2K_train_HR

          ├── DIV2K_train_LR_bicubic

          ├── DIV2K_valid_HR

          └── DIV2K_valid_LR_bicubic

    └── models

          ├── g.npz  # You should rename the weigths file. 

          └── d.npz  # If you set os.environ['TL_BACKEND'] = 'tensorflow',you should rename srgan-g-tensorflow.npz to g.npz .

```

- Start evaluation.

```bash

python train.py --mode=eval

```

Results will be saved under the folder srgan/samples/. 

### Results





	





### Reference

* [1] [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802)

* [2] [Is the deconvolution layer the same as a convolutional layer ?](https://arxiv.org/abs/1609.07009)

### Citation

If you find this project useful, we would be grateful if you cite the TensorLayer paper：

```

@article{tensorlayer2017,

author = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},

journal = {ACM Multimedia},

title = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},

url = {http://tensorlayer.org},

year = {2017}

}

@inproceedings{tensorlayer2021,

  title={TensorLayer 3.0: A Deep Learning Library Compatible With Multiple Backends},

  author={Lai, Cheng and Han, Jiarong and Dong, Hao},

  booktitle={2021 IEEE International Conference on Multimedia \& Expo Workshops (ICMEW)},

  pages={1--3},

  year={2021},

  organization={IEEE}

}

```

### Other Projects

- [Style Transfer](https://github.com/tensorlayer/adaptive-style-transfer)

- [Pose Estimation](https://github.com/tensorlayer/openpose)

### Discussion

- [TensorLayer Slack](https://join.slack.com/t/tensorlayer/shared_invite/enQtMjUyMjczMzU2Njg4LWI0MWU0MDFkOWY2YjQ4YjVhMzI5M2VlZmE4YTNhNGY1NjZhMzUwMmQ2MTc0YWRjMjQzMjdjMTg2MWQ2ZWJhYzc)

- [TensorLayer WeChat](https://github.com/tensorlayer/tensorlayer-chinese/blob/master/docs/wechat_group.md)

### License

- For academic and non-commercial use only.

- For commercial use, please contact [email protected].