Movatterモバイル変換

tensorlayer/SRGANPublic

NotificationsYou must be signed in to change notification settings
Fork815
Star3.4k

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

github.com/tensorlayer/tensorlayerx

3.4k stars 815 forks Branches Tags Activity

Star

Notifications

You must be signed in to change notification settings

Branches Tags

Folders and files

Name		Name	Last commit message	Last commit date
Latest commit History 133 Commits
img		img
.gitignore		.gitignore
.style.yapf		.style.yapf
README.md		README.md
config.py		config.py
requirements.txt		requirements.txt
srgan.py		srgan.py
train.py		train.py
vgg.py		vgg.py

Repository files navigation

Super Resolution Examples

Implementation of"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
For earlier version, please checksrgan release andtensorlayer.
For more computer vision applications, checkTLXCV

SRGAN Architecture

Prepare Data and Pre-trained VGG

1. You need to download the pretrained VGG19 model weights inhere.
1. You need to have the high resolution images for training.
- In this experiment, I used images fromDIV2K - bicubic downscaling x4 competition, so the hyper-paremeters inconfig.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 useYahoo MirFlickr25k, just simply download it usingtrain_hr_imgs = tl.files.load_flickr25k_dataset(tag=None) inmain.py.
- If you want to use your own images, you can set the path to your image folder viaconfig.TRAIN.hr_img_path inconfig.py.

Run

🔥🔥🔥🔥🔥🔥 You need installTensorLayerX at first!

🔥🔥🔥🔥🔥🔥 Please install TensorLayerX via source

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

Train

Set your image folder inconfig.py, if you downloadDIV2K - bicubic downscaling x4 competition dataset, you don't need to change it.
Other links for DIV2K, in case you can't find it :test_LR_bicubic_X4,train_HR,train_LR_bicubic_X4,valid_HR,valid_LR_bicubic_X4.

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.

python train.py

🔥Modify a line of code intrain.py, easily switch to any framework!

importosos.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,Googledrive	Baidu,Googledrive
PaddlePaddle	Baidu,Googledrive	Baidu,Googledrive
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.

python train.py --mode=eval

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

Results

Reference

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}}