Minor fixes based on @kuke's feedback

Author: Varun Arora

doc/fluid/design/onnx/onnx_convertor.md

@@ -4,8 +4,6 @@
 
 Therefore, it is necessary to enable the conversion between PaddlePaddle and ONNX. This design doc is aimed at implementing a convertor, mainly for converting between **Fluid** models and ONNX (it is very likely that we may support older v2 models in the future). A complete convertor should be bidirectional - with a frontend AND a backend, but considering the importance, the we will start with the frontend i.e. Fluid models to ONNX models.
 
-One thing that makes it doable in Fluid's case is the use of a static IR - the `ProgramDesc` - as opposed to a dynamic graph, as created in the cases of frameworks like PyTorch.
-
 
 # How it works
 
@@ -24,6 +22,8 @@ Here are a few major considerations when it comes to converting models:
    2. Checking to see if the generated nodes on the graph are validated by the internal ONNX checkers.
 - **Versioning**: ONNX versions its op listing over versions. In fact, it has versioning on 3 different levels: ops, graphs, and ONNX models. This requires that we are conscious about versioning the convertor and updating tests and op convertor logic for each release. It also implies that we release pre-trained ONNX models upon each version release.
 
+One thing that makes this conversion more feasible in Fluid's case is the use of a static IR - the `ProgramDesc` - as opposed to a dynamic graph, as created in the cases of frameworks like PyTorch.
+
 
 # Project structure
 
@@ -45,11 +45,17 @@ The project contains four important parts:
 # Usage
 The converter should be designed to very easy-to-use. Bidirectional conversion between a Fluid inference model and an ONNX binary model will be supported. Model validation will also provided to verify the correctness of converted model.
 
-* Fluid inference model to ONNX binary model
+* Convert Fluid inference model to ONNX binary model
+
+    ```
+    python convert.py --fluid_model <fluid inference model> --onnx_model <ONNX model> validate True
+    ```
+
+* Validate the converted model
 
-```
-python convert.py --fluid_model <fluid inference model> --onnx_model <ONNX model> validate True
-```
+    ```
+    python validate.py --fluid_model <fluid inference model> --onnx_model <ONNX model>
+    ```
 
 The conversion and model validation will be completed consecutively, finally output a readable model structure description. And for the converse conversion, users only need to exchange the input and output.