update version under TensorLayerX (#242)

Author: hanjr92

README.md

@@ -1,50 +1,26 @@
 ## Super Resolution Examples
 
+🔥🔥🔥🔥🔥🔥**Now, we update this script under [TensorLayerX](https://github.com/tensorlayer/TensorLayerX)! For earlier version, please check [srgan release](https://github.com/tensorlayer/srgan/releases) and [tensorlayer](https://github.com/tensorlayer/TensorLayer).**
 
 
-
-We run this script under [TensorFlow](https://www.tensorflow.org) 2.0 and the [**TensorLayer2.0+**](https://github.com/tensorlayer/tensorlayer). For TensorLayer 1.4 version, please check [release](https://github.com/tensorlayer/srgan/releases).
-
-<!---
-⚠️ This repo will be merged into example folder of [tensorlayer](https://github.com/zsdonghao/tensorlayer) soon.
--->
-🚀🚀🚀🚀🚀🚀 **THIS PROJECT WILL BE CLOSED AND MOVED TO [THIS FOLDER](https://github.com/tensorlayer/tensorlayer/tree/master/examples) IN A MONTH.**
-
-🚀🚀🚀🚀🚀🚀 **THIS PROJECT WILL BE CLOSED AND MOVED TO [THIS FOLDER](https://github.com/tensorlayer/tensorlayer/tree/master/examples) IN A MONTH.**
-
-🚀🚀🚀🚀🚀🚀 **THIS PROJECT WILL BE CLOSED AND MOVED TO [THIS FOLDER](https://github.com/tensorlayer/tensorlayer/tree/master/examples) IN A MONTH.**
-
-<!--More cool Computer Vision applications such as pose estimation and style transfer can be found in this [organization](https://github.com/tensorlayer).**
--->
-
 ### SRGAN Architecture
 
-TensorFlow Implementation of ["Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"](https://arxiv.org/abs/1609.04802)
+TensorLayerX Implementation of ["Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"](https://arxiv.org/abs/1609.04802)
 
-<a href="http://tensorlayer.readthedocs.io">
+<a href="https://tensorlayerx.readthedocs.io/en/latest/">
 <div align="center">
 	<img src="img/model.jpeg" width="80%" height="10%"/>
 </div>
 </a>
-
-
-### Results
-
-<a href="http://tensorlayer.readthedocs.io">
-<div align="center">
-	<img src="img/SRGAN_Result2.png" width="80%" height="50%"/>
-</div>
-</a>
-
-<a href="http://tensorlayer.readthedocs.io">
+<a href="https://tensorlayerx.readthedocs.io/en/latest/">
 <div align="center">
 	<img src="img/SRGAN_Result3.png" width="80%" height="50%"/>
 </div>
 </a>
 
 ### Prepare Data and Pre-trained VGG
 
-- 1. You need to download the pretrained VGG19 model in [here](https://github.com/tensorlayer/pretrained-models/tree/master/models) as [tutorial_models_vgg19.py](https://github.com/tensorlayer/tensorlayer/blob/master/examples/pretrained_cnn/tutorial_models_vgg19.py) show.
+- 1. You need to download the pretrained VGG19 model weights in [here](https://github.com/tensorlayer/pretrained-models/tree/master/models).
 - 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`. 
@@ -53,34 +29,106 @@ TensorFlow Implementation of ["Photo-Realistic Single Image Super-Resolution Usi
 
 
 ### Run
+
+#### 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
 ```
 
-- Start evaluation. 
+🔥Just modify a line of code in **train.py**, easily change to any framework!
+
+```python
+import os
+os.environ['TL_BACKEND'] = 'tensorflow'
+# os.environ['TL_BACKEND'] = 'mindspore'
+# os.environ['TL_BACKEND'] = 'paddle'
+```
+🚧 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!     |
 
-<!--([pretrained model](https://github.com/tensorlayer/srgan/releases/tag/1.2.0) for DIV2K)-->
 
+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=evaluate 
+python train.py --mode=eval
 ```
 
+Results will be saved under the folder srgan/samples/. 
+
+### Results
+
+<a href="http://tensorlayer.readthedocs.io">
+<div align="center">
+	<img src="img/SRGAN_Result2.png" width="80%" height="50%"/>
+</div>
+</a>
+
 
 ### 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)
 
-### Author
-- [zsdonghao](https://github.com/zsdonghao)
+
 
 ### Citation
 If you find this project useful, we would be grateful if you cite the TensorLayer paper：
@@ -93,6 +141,15 @@ title = {{TensorLayer: A Versatile Library for Efficient Deep Learning Developme
 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

config.py

@@ -3,9 +3,7 @@
 
 config = edict()
 config.TRAIN = edict()
-
-## Adam
-config.TRAIN.batch_size = 8 # [16] use 8 if your GPU memory is small, and use [2, 4] in tl.vis.save_images / use 16 for faster training
+config.TRAIN.batch_size = 16 # [16] use 8 if your GPU memory is small
 config.TRAIN.lr_init = 1e-4
 config.TRAIN.beta1 = 0.9