clean readme (#369)

Author: luomai

README.md

@@ -96,77 +96,57 @@ More information of the HyperPose Docker image can be found [here](https://hyper
 
 ### Python training library
 
-We recommend to use [Anaconda](https://www.anaconda.com/products/individual) to create a virtual python environment for hyperpose python training library, so we can avoid the possible package conflicts and handle the *cudatoolkit* and *cudnn* library dependency.
+We recommend to use the Python training library with the [Anaconda](https://www.anaconda.com/products/individual) environment which can come with *cudatoolkit* and *cudnn*.
 
 All the following instructions have been tested on the environments below:<br>
-* Ubuntu 18.04, Tesla V100-DGXStation, Nvidia Driver Version 440.33.01, CUDA Verison=10.2  
-* Ubuntu 18.04, Tesla V100-DGXStation, Nvidia Driver Version 410.79, CUDA Verison=10.0  
-* Ubuntu 18.04, TITAN RTX, Nvidia Driver Version 430.64, CUDA Version=10.1  
-* Ubuntu 18.04, TITAN Xp, Nvidia Driver Version 430.26, CUDA Version=10.2
-* Ubuntu 16.04, RTX 2080Ti, Nvidia Driver Version 430.50, CUDA Version=10.1
+* Ubuntu 18.04, Tesla V100-DGX, NVIDIA Driver > 410.79, CUDA > 10.0
+* Ubuntu 18.04, TITAN RTX, NVIDIA Driver 430.64, CUDA 10.1
+* Ubuntu 18.04, TITAN XP, NVIDIA Driver 430.26, CUDA 10.2
+* Ubuntu 16.04, RTX 2080Ti, NVIDIA Driver 430.50, CUDA 10.1
 
-With Anaconda installed, run the following command to configure the appropriate virtual environment:
+After installing Anaconda, run following Bash commands to create a virtual environment:
 
 ```bash
-# >>> create virtual environment (choose yes)
+# Create virtual environment (choose yes)
 conda create -n hyperpose python=3.7
-# >>> activate the virtual environment, start installation
+# Activate the virtual environment, start installation
 conda activate hyperpose
-# >>> install cudatoolkit and cudnn library using conda
+# Install cudatoolkit and cudnn library using conda
 conda install cudatoolkit=10.0.130
 conda install cudnn=7.6.0
 ```
 
-Then we need to using pip to install the python requirements according to the [requirements.txt](https://github.com/tensorlayer/hyperpose/blob/master/requirements.txt):
+We then install the python packages listed in [requirements.txt](https://github.com/tensorlayer/hyperpose/blob/master/requirements.txt):
 
 ```bash
 pip install -r requirements.txt
 ```
 
-Now all the configuration is down, run the following command under the root directory of the repository to test whether hyperpose can be successfully import:
+The below sample Python programs shows how to use HyperPose to train a pose estimation model. HyperPose contains three key APIs through *Config*, *Model* and *Dataset* modules. We use the *Config* module to set up the configuration, and use *Model* and *Dataset* module to assemble the train or evaluation pipline:
 
-```bash
-# >>> Check whether the GPU is avaliable.
-python
->>> import tensorflow as tf
->>> import tensorlayer as tl
->>> tf.test.is_gpu_available()
-# >>> if the output is True, we can then import and run hyperpose now
->>> from hyperpose import Config,Model,Dataset
-```
-
-Congratulations! we can use hyperpose to develop your pose estimation models now!
-
-Hyperpose python training library provides APIs through *Config*, *Model* and *Dataset* modules.
+```python
+import tensorflow as tf
+import tensorlayer as tl
+tf.test.is_gpu_available()
 
-We use the *Config* module to set up the configuration, and use *Model* and *Dataset* module to assemble the train or evaluation pipline, the sample code below shows how to use hyperpose to train a *LightweightOpenpose* model with *Vggtiny* network backbone:
-
-```bash
-# >>> import modules of hyperpose
-from hyperpose import Config,Model,Dataset
-# >>> set model name to distinguish models (neccesarry)
+from hyperpose import Config, Model, Dataset
+# Set model name to distinguish models (necessary)
 Config.set_model_name("My_lopps")
-# >>> set model architecture (and set model backbone when in need)
+# Set model type, model backbone and dataset
 Config.set_model_type(Config.MODEL.LightweightOpenpose)
 Config.set_model_backbone(Config.BACKBONE.Vggtiny)
-# >>> set dataset to use
 Config.set_dataset_type(Config.DATA.MSCOCO)
-# >>> set training type 
+# Set single-node training or parallel-training
 Config.set_train_type(Config.TRAIN.Single_train)
-# >>> configuration is done, get config object and assemble the system
-config=Config.get_config()
-model=Model.get_model(config)
-dataset=Dataset.get_dataset(config)
-train=Model.get_train(config)
-# >>> train!
+config = Config.get_config()
+model = Model.get_model(config)
+dataset = Dataset.get_dataset(config)
+train = Model.get_train(config)
+# Start the training process
 train(model,dataset)
 ```
 
-We provide a sample training script with cli located at [train.py](https://github.com/tensorlayer/hyperpose/blob/master/train.py) which demonstrates the usage of hyperpose python training library, you can either directly use the script to train your model or  use it as a template for further modification.
-
-To evaluate a model using hyperpose is similiar to the training procedure, we also provide a sample evaluation script with cli located at [eval.py](https://github.com/tensorlayer/hyperpose/blob/master/eval.py) as an example and a template for modification.  
-
-More information of the Hyperpose training library usage can be found [here](https://hyperpose.readthedocs.io/en/latest/markdown/quick_start/training.html).
+The full Python training program is [here](https://github.com/tensorlayer/hyperpose/blob/master/train.py). To evaluate the trained model, you can use a Python evaluation program [here](https://github.com/tensorlayer/hyperpose/blob/master/eval.py). More information of the training library is [here](https://hyperpose.readthedocs.io/en/latest/markdown/quick_start/training.html).
 
 
 ## Documentation