Update doc in PaddleDetection (#3074)

* update doc in PaddleDetection

docs/CONFIG.md

-# Introduction
+# Config Pipline
+
+## Introduction
 
 PaddleDetection takes a rather principled approach to configuration management. We aim to automate the configuration workflow and to reduce configuration errors.
 
 
-# Rationale
+## Rationale
 
 Presently, configuration in mainstream frameworks are usually dictionary based: the global config is simply a giant, loosely defined Python dictionary.
 
@@ -12,7 +14,7 @@ This approach is error prone, e.g., misspelled or displaced keys may lead to ser
 To avoid the common pitfalls, with automation and static analysis in mind, we propose a configuration design that is user friendly, easy to maintain and extensible.
 
 
-# Design
+## Design
 
 The design utilizes some of Python's reflection mechanism to extract configuration schematics from Python class definitions.
 
@@ -21,7 +23,7 @@ To be specific, it extracts information from class constructor arguments, includ
 This approach advocates modular and testable design, leading to a unified and extensible code base.
 
 
-## API
+### API
 
 Most of the functionality is exposed in `ppdet.core.workspace` module.
 
@@ -34,7 +36,7 @@ Most of the functionality is exposed in `ppdet.core.workspace` module.
 -   `load_config` and `merge_config`: Loading yaml file and merge config settings from command line.
 
 
-## Example
+### Example
 
 Take the `RPNHead` module for example, it is composed of several PaddlePaddle operators. We first wrap those operators into classes, then pass in instances of these classes when instantiating the `RPNHead` module.
 
@@ -156,7 +158,7 @@ LearningRate:
 ```
 
 
-# Requirements
+## Requirements
 
 Two Python packages are used, both are optional.
 
@@ -170,7 +172,7 @@ pip install typeguard http://github.com/willthefrog/docstring_parser/tarball/mas
 ```
 
 
-# Tooling
+## Tooling
 
 A small utility (`tools/configure.py`) is included to simplify the configuration process, it provides 4 commands to walk users through the configuration process:
 
@@ -190,7 +192,7 @@ A small utility (`tools/configure.py`) is included to simplify the configuration
     ```
 
 
-# FAQ
+## FAQ
 
 **Q:** There are some configuration options that are used by multiple modules (e.g., `num_classes`), how do I avoid duplication in config files?
 

docs/CONFIG_cn.md

-# 简介
+# 配置模块
+
+## 简介
 
 为了使配置过程更加自动化并减少配置错误，PaddleDetection的配置管理采取了较为严谨的设计。
 
 
-# 设计思想
+## 设计思想
 
 目前主流框架全局配置基本是一个Python dict，这种设计对配置的检查并不严格，拼写错误或者遗漏的配置项往往会造成训练过程中的严重错误，进而造成时间及资源的浪费。为了避免这些陷阱，从自动化和静态分析的原则出发，PaddleDetection采用了一种用户友好、 易于维护和扩展的配置设计。
 
 
-# 基本设计
+## 基本设计
 
 利用Python的反射机制，PaddleDection的配置系统从Python类的构造函数抽取多种信息 - 如参数名、初始值、参数注释、数据类型（如果给出type hint）- 来作为配置规则。 这种设计便于设计的模块化，提升可测试性及扩展性。
 
 
-## API
+### API
 
 配置系统的大多数功能由 `ppdet.core.workspace` 模块提供
 
@@ -26,7 +28,7 @@
 -   `load_config` and `merge_config`: 加载yaml文件，合并命令行提供的配置项。
 
 
-## 示例
+### 示例
 
 以 `RPNHead` 模块为例，该模块包含多个PaddlePaddle OP，先将这些OP封装成类，并将其实例在构造 `RPNHead` 时注入。
 
@@ -148,7 +150,7 @@ LearningRate:
 ```
 
 
-# 安装依赖
+## 安装依赖
 
 配置系统用到两个Python包，均为可选安装。
 
@@ -162,7 +164,7 @@ pip install typeguard http://github.com/willthefrog/docstring_parser/tarball/mas
 ```
 
 
-# 相关工具
+## 相关工具
 
 为了方便用户配置，PaddleDection提供了一个工具 (`tools/configure.py`)， 共支持四个子命令：
 
@@ -182,7 +184,7 @@ pip install typeguard http://github.com/willthefrog/docstring_parser/tarball/mas
     ```
 
 
-# FAQ
+## FAQ
 
 **Q:** 某些配置项会在多个模块中用到(如 `num_classes`)，如何避免在配置文件中多次重复设置？
 

docs/DATA.md

+# Data Pipline
+
 ## Introduction
 
 The data pipeline is responsible for loading and converting data. Each

docs/DATA_cn.md

+# 数据模块
+
 ## 介绍
 本模块是一个Python模块，用于加载数据并将其转换成适用于检测模型的训练、验证、测试所需要的格式——由多个np.ndarray组成的tuple数组，例如用于Faster R-CNN模型的训练数据格式为：`[(im, im_info, im_id, gt_bbox, gt_class, is_crowd), (...)]`。
 

docs/GETTING_STARTED.md

@@ -162,11 +162,12 @@ export CUDA_VISIBLE_DEVICES=0
 python tools/infer.py -c configs/faster_rcnn_r50_1x.yml \
                       --infer_img=demo/000000570688.jpg \
                       --output_dir=infer_output/ \
-                      --draw_threshold=0.5
+                      --draw_threshold=0.5 \
+                      -o weights=output/faster_rcnn_r50_1x/model_final
 ```
 The visualization files are saved in `output` by default, to specify a different
 path, simply add a `--output_dir=` flag.  
-`--draw_threshold` is an optional argument. Default is 0.5. Different thresholds will produce different results depending on the calculation of [NMS](https://ieeexplore.ieee.org/document/1699659)
+`--draw_threshold` is an optional argument. Default is 0.5. Different thresholds will produce different results depending on the calculation of [NMS](https://ieeexplore.ieee.org/document/1699659). If users want to infer according to customized model path, `-o weights` can be set for specified path.
 
 - Save inference model
 

docs/GETTING_STARTED_cn.md

@@ -156,12 +156,13 @@ export PYTHONPATH=$PYTHONPATH:.
 python tools/infer.py -c configs/faster_rcnn_r50_1x.yml \
                       --infer_img=demo/000000570688.jpg \
                       --output_dir=infer_output/ \
-                      --draw_threshold=0.5
+                      --draw_threshold=0.5 \
+                      -o weights=output/faster_rcnn_r50_1x/model_final
 ```
 
 
 可视化文件默认保存在`output`中，可通过`--output_dir=`指定不同的输出路径。  
-`--draw_threshold` 是个可选参数. 根据 [NMS](https://ieeexplore.ieee.org/document/1699659) 的计算，不同阈值会产生不同的结果。
+`--draw_threshold` 是个可选参数. 根据 [NMS](https://ieeexplore.ieee.org/document/1699659) 的计算，不同阈值会产生不同的结果。如果用户需要对自定义路径的模型进行推断，可以设置`-o weights`指定模型路径。
 
 - 保存推断模型
 

docs/MODEL_ZOO_cn.md

 # 模型库和基线
 
+## 测试环境
+
 - Python 2.7.1
 - PaddlePaddle 1.5
 - CUDA 9.0