Merge branch 'oneflow_dev' of https://github.com/QuantumLiu/TensorLayerX

 into oneflow_dev

.dockerignore

@@ -1 +1,2 @@
 .git
+.log

.gitignore

@@ -2,6 +2,7 @@
 __pycache__/
 *.py[cod]
 *$py.class
+.DS_Store  
 
 # C extensions
 *.so

.pyup.yml

@@ -42,9 +42,6 @@ requirements:
     # Not necessary, but recommended libraries
     - requirements/requirements_extra.txt
 
-    # Requirements for contrib loggers
-    - requirements_contrib_loggers.txt
-
     # Requirements for the db
     - requirements/requirements_db.txt
 

.readthedocs.yml

@@ -8,7 +8,7 @@ formats:
     - pdf
 
 python:
-    version: 3.6
+    version: 3.7
 
 requirements_file: 
     requirements/requirements_doc.txt

README.md

@@ -14,57 +14,91 @@
 [![Downloads](https://pepy.tech/badge/tensorlayerx/week)](https://pepy.tech/project/tensorlayerx/week)
 [![Docker Pulls](https://img.shields.io/docker/pulls/tensorlayer/tensorlayerx.svg)](https://hub.docker.com/r/tensorlayer/tensorlayerx/)
 
-🇬🇧 TensorLayerX is a multi-backend AI framework, which supports TensorFlow, Pytorch, MindSpore, PaddlePaddle, OneFlow and Jittor as the backends, allowing users to run the code on different hardware like Nvidia-GPU and Huawei-Ascend. 
+[TensorLayerX](https://tensorlayerx.readthedocs.io) is a multi-backend AI framework, supports TensorFlow, Pytorch, MindSpore, PaddlePaddle, OneFlow and Jittor as the backends, allowing users to run the code on different hardware like Nvidia-GPU, Huawei-Ascend, Cambricon and more.
 This project is maintained by researchers from Peking University, Imperial College London, Princeton, Stanford, Tsinghua, Edinburgh and Peng Cheng Lab.
-[supported layers](https://shimo.im/sheets/kJGCCTxXvqj99RGV/F5m5Z). 
 
-🇨🇳 TensorLayerX 是一个跨平台开发框架，支持TensorFlow, Pytorch, MindSpore, PaddlePaddle, OneFlow和Jittor，用户不需要修改任何代码即可以运行在各类操作系统和AI硬件上（如Nvidia-GPU 和 Huawei-Ascend），并支持混合框架的开发。这个项目由北京大学、鹏城实验室、爱丁堡大学、帝国理工、清华、普林斯顿、斯坦福等机构的研究人员维护。
-[支持列表](https://shimo.im/sheets/kJGCCTxXvqj99RGV/F5m5Z)。
 
+GitHub: https://github.com/tensorlayer/TensorLayerX  
+OpenI: https://openi.pcl.ac.cn/OpenI/TensorLayerX
 
 # Document
 TensorLayerX has extensive documentation for both beginners and professionals. 
 
 [![English Documentation](https://img.shields.io/badge/documentation-english-blue.svg)](https://tensorlayerx.readthedocs.io/en/latest/)
 
-# DeepLearning course  
-🔥We have opened a video course for introductory learning deep learning, with example codes based on TensorLayerX.  
-[Bilibili link](https://www.bilibili.com/video/BV1xB4y1h7V2?share_source=copy_web&vd_source=467c17f872fcde378494433520e19999)
+# Deep Learning course  
+We have video courses for deep learning, with example codes based on TensorLayerX.  
+[Bilibili link](https://www.bilibili.com/video/BV1xB4y1h7V2?share_source=copy_web&vd_source=467c17f872fcde378494433520e19999) (chinese)
+
 # Design Features
 
-Compare with [TensorLayer](https://github.com/tensorlayer/TensorLayer), [TensorLayerX](http://tensorlayerx.readthedocs.io)（TLX) is a brand new seperated project for platform-agnostic purpose. 
+<p align="center"><img src="https://git.openi.org.cn/hanjr/tensorlayerx-image/raw/branch/master/version.png" width="840"\></p>
 
-Compare to TensorLayer version:
+- ***Compatibility***: Support worldwide frameworks and AI chips, enabling one code runs on all platforms.
 
-<p align="center"><img src="https://git.openi.org.cn/hanjr/tensorlayerx-image/raw/branch/master/version.png" width="840"\></p>
+- ***Model Zoo***: Provide a series of applications containing classic and SOTA models, covering CV, NLP, RL and other fields.
+
+- ***Deployment***: Support ONNX protocol, model export, import and deployment.
+
+# Multi-backend Design
+
+You can immediately use TensorLayerX to define a model via Pytorch-stype, and switch to any backends easily.
+
+```python
+import os
+os.environ['TL_BACKEND'] = 'tensorflow' # modify this line, switch to any backends easily!
+#os.environ['TL_BACKEND'] = 'mindspore'
+#os.environ['TL_BACKEND'] = 'paddle'
+#os.environ['TL_BACKEND'] = 'torch'
+import tensorlayerx as tlx
+from tensorlayerx.nn import Module
+from tensorlayerx.nn import Linear
+class CustomModel(Module):
+
+  def __init__(self):
+      super(CustomModel, self).__init__()
+
+      self.linear1 = Linear(out_features=800, act=tlx.ReLU, in_features=784)
+      self.linear2 = Linear(out_features=800, act=tlx.ReLU, in_features=800)
+      self.linear3 = Linear(out_features=10, act=None, in_features=800)
 
-🔥TensorLayerX inherits the features of the previous verison, including ***Simplicity***, ***Flexibility*** and ***Zero-cost Abstraction***. ***Compare with [TensorLayer](https://github.com/tensorlayer/TensorLayer), TensorLayerX supports more backends, such as TensorFlow, MindSpore, PaddlePaddle and PyTorch. It allows users to run the same code on different hardwares like Nvidia-GPU and Huawei-Ascend.*** In addition, more features are ***under development***.
+  def forward(self, x, foo=False):
+      z = self.linear1(x)
+      z = self.linear2(z)
+      out = self.linear3(z)
+      if foo:
+          out = tlx.softmax(out)
+      return out
 
-- ***Model Zoo***: Build a series of model Zoos containing classic and sota models,covering CV, NLP, RL and other fields.
+MLP = CustomModel()
+MLP.set_eval()
+```
 
-- ***Deploy***: In feature, TensorLayerX will support the ONNX protocol, supporting model export, import and deployment.
 
-- ***Parallel***: In order to improve the efficiency of neural network model training, parallel computing is indispensable. 
 
 # Resources
 
-- [TLX2ONNX](https://github.com/tensorlayer/TLX2ONNX/) ONNX Model Exporter for TensorLayerX. ✅
-- [Examples](https://github.com/tensorlayer/TensorLayerX/tree/main/examples) for tutorials✅
-- [GammaGL](https://github.com/BUPT-GAMMA/GammaGL) is a multi-backend graph learning library based on TensorLayerX.✅
-- OpenIVA an easy-to-use product-level deployment framework✅
-- [TLXZoo](https://github.com/tensorlayer/TLXZoo) pretrained models/backbones🚧
-- TLXCV a bunch of Computer Vision applications🚧
-- TLXNLP a bunch of Natural Language Processing applications🚧
-- TLXRL a bunch of Reinforcement Learning applications, check [RLZoo](https://github.com/tensorlayer/RLzoo) for the old version✅
+- [Examples](https://github.com/tensorlayer/TensorLayerX/tree/main/examples) for tutorials
+- [GammaGL](https://github.com/BUPT-GAMMA/GammaGL) is series of graph learning algorithm
+- [TLXZoo](https://github.com/tensorlayer/TLXZoo) a series of pretrained backbones
+- [TLXCV](https://github.com/tensorlayer/TLXCV) a series of Computer Vision applications
+- [TLXNLP](https://github.com/tensorlayer/TLXNLP) a series of Natural Language Processing applications
+- [TLX2ONNX](https://github.com/tensorlayer/TLX2ONNX/) ONNX model exporter for TensorLayerX.
+- [Paddle2TLX](https://github.com/tensorlayer/paddle2tlx) model code converter from PaddlePaddle to TensorLayerX.  
 
 More resources can be found [here](https://github.com/tensorlayer)
 
 
-# Quick Start
+# Installation
 
-## Installation
+- The latest TensorLayerX compatible with the following backend version
 
-### Via docker
+| TensorLayerX | TensorFlow | MindSpore | PaddlePaddle|PyTorch|
+| :-----:| :----: | :----: |:-----:|:----:|
+|  v0.5.8  | v2.4.0 | v1.8.1 | v2.2.0 | v1.10.0 |
+| v0.5.7 | v2.0.0 | v1.6.1 | v2.0.2 | v1.10.0 |
+
+- Via docker
 
 Docker is an open source application container engine. In the [TensorLayerX Docker Repository](https://hub.docker.com/repository/docker/tensorlayer/tensorlayerx), 
 different versions of TensorLayerX have been installed in docker images.
@@ -74,56 +108,35 @@ different versions of TensorLayerX have been installed in docker images.
 docker pull tensorlayer/tensorlayerx:tagname
 ```
 
-
-### Via pip
+- Via pip for the stable version
 ```bash
 # install from pypi
 pip3 install tensorlayerx 
 ```
 
-### Build from source
+- Build from source for the latest version
 ```bash
 # install from Github
 pip3 install git+https://github.com/tensorlayer/tensorlayerx.git 
 ```
 For more installation instructions, please refer to [Installtion](https://tensorlayerx.readthedocs.io/en/latest/user/installation.html)
 
-## Define a model
 
-You can immediately use tensorlayerx to define a model, using your favourite framework in the background, like so:
-```python
-import os
-os.environ['TL_BACKEND'] = 'tensorflow' # modify this line, switch to any framework easily!
-#os.environ['TL_BACKEND'] = 'mindspore'
-#os.environ['TL_BACKEND'] = 'paddle'
-#os.environ['TL_BACKEND'] = 'torch'
-import tensorlayerx as tlx
-from tensorlayerx.nn import Module
-from tensorlayerx.nn import Linear
-class CustomModel(Module):
+# Contributing
+Join our community as a code contributor, find out more in our [Help wanted list](https://github.com/tensorlayer/TensorLayerX/issues/5) and [Contributing](https://tensorlayerx.readthedocs.io/en/latest/user/contributing.html) guide!
 
-  def __init__(self):
-      super(CustomModel, self).__init__()
 
-      self.linear1 = Linear(out_features=800, act=tlx.ReLU, in_features=784)
-      self.linear2 = Linear(out_features=800, act=tlx.ReLU, in_features=800)
-      self.linear3 = Linear(out_features=10, act=None, in_features=800)
+# Getting Involved
 
-  def forward(self, x, foo=False):
-      z = self.linear1(x)
-      z = self.linear2(z)
-      out = self.linear3(z)
-      if foo:
-          out = tlx.softmax(out)
-      return out
+We suggest users to report bugs using Github issues. Users can also discuss how to use TensorLayerX in the following slack channel.
 
-MLP = CustomModel()
-MLP.set_eval()
-```
-
-# Contributing
-Join our community as a code contributor, find out more in our [Help wanted list](https://github.com/tensorlayer/TensorLayerX/issues/5) and [Contributing](https://tensorlayerx.readthedocs.io/en/latest/user/contributing.html) guide!
+<br/>
 
+<a href="https://join.slack.com/t/tensorlayer/shared_invite/enQtODk1NTQ5NTY1OTM5LTQyMGZhN2UzZDBhM2I3YjYzZDBkNGExYzcyZDNmOGQzNmYzNjc3ZjE3MzhiMjlkMmNiMmM3Nzc4ZDY2YmNkMTY" target="\_blank">
+	<div align="center">
+		<img src="https://github.com/tensorlayer/TensorLayer/blob/bdc2c14ff9ed9bd3ec7004d625e15683df7b530d/img/join_slack.png?raw=true" width="40%"/>
+	</div>
+</a>
 
 # Contact
  - [email protected]
@@ -133,14 +146,6 @@ Join our community as a code contributor, find out more in our [Help wanted list
 If you find TensorLayerX useful for your project, please cite the following papers：
 
 ```
-@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},
@@ -149,6 +154,13 @@ If you find TensorLayerX useful for your project, please cite the following pape
   year={2021},
   organization={IEEE}
 }
+@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}
+} 
 ```
 
 

README.rst

@@ -89,5 +89,5 @@ License
 
 TensorLayerX is released under the Apache 2.0 license.
 
-.. |TENSORLAYER-LOGO| image:: https://git.openi.org.cn/TensorLayer/tensorlayer3.0/src/branch/master/img/tl_transparent_logo.png
+.. |TENSORLAYER-LOGO| image:: https://git.openi.org.cn/hanjr/tensorlayerx-image/raw/branch/master/tlx-LOGO--02.jpg
    :target: https://tensorlayerx.readthedocs.io/en/latest/

countlines.py

@@ -22,7 +22,6 @@ def countlines(start, lines=0, header=True, begin_start=None):
                     print('{:>10} |{:>10} | {:<20}'.format(
                             newlines, lines, reldir_of_thing))
 
-
     for thing in os.listdir(start):
         thing = os.path.join(start, thing)
         if os.path.isdir(thing):
@@ -31,4 +30,4 @@ def countlines(start, lines=0, header=True, begin_start=None):
     return lines
 
 if __name__ == '__main__':
-    countlines('.')
+    countlines('.')

docker/DockerLint.bat

@@ -1,4 +1,4 @@
-docker pull hadolint/hadolint:latest
-docker run --rm -i hadolint/hadolint hadolint --ignore DL3007 - < Dockerfile
+docker pull tensorlayer/tensorlayerx:tagname
+docker run --rm -i tensorlayer/tensorlayerx hadolint --ignore DL3007 - < Dockerfile
 
 PAUSE;

docker/Dockerfile

@@ -24,8 +24,8 @@ RUN echo "Container Tag: ${TF_CONTAINER_VERSION}" \
         echo "Building a Nightly Release" \
         && apt-get install -y git \
         && mkdir /dist/ && cd /dist/ \
-        && git clone https://github.com/tensorlayer/tensorlayer.git \
-        && cd tensorlayer \
+        && git clone https://github.com/tensorlayer/tensorlayerx.git \
+        && cd tensorlayerx \
         && pip install --disable-pip-version-check --no-cache-dir --upgrade -e .[all]; \
     else \
         echo "Building Tag Release: $TL_VERSION" \

docs/conf.py

@@ -72,6 +72,7 @@
     'tensorflow',
     'tqdm',
     'h5py',
+    'rich',
 
     # TL C++ Packages
     'tensorlayerx.third_party.roi_pooling.roi_pooling.roi_pooling_ops',

docs/index.rst

@@ -3,7 +3,7 @@ Welcome to TensorLayerX
 
 
 .. image:: user/my_figs/tlx_transparent_logo.png
-  :width: 30 %
+  :width: 60 %
   :align: center
   :target: https://github.com/tensorlayer/TensorLayerX
 
@@ -34,7 +34,6 @@ to the library as a developer.
   user/installation
   user/examples
   user/contributing
-  user/get_involved
   user/faq
 
 .. toctree::