Update readme of mindconverter.

mindinsight/mindconverter/README.md

@@ -273,7 +273,7 @@ In addition, for operators that are not converted successfully, the input and ou
 
 ## Caution
 
-1. PyTorch, TensorFlow, TF2ONNX(1.7.1) are not an explicitly stated dependency libraries in MindInsight. The Graph conversion requires the consistent PyTorch or TensorFlow version as the model is trained. (MindConverter recommends PyTorch 1.4.0 or 1.6.0)
+1. PyTorch, TensorFlow are not an explicitly stated dependency libraries in MindInsight. The Graph conversion requires the consistent PyTorch or TensorFlow version as the model is trained. (MindConverter recommends PyTorch 1.4.0)
 2. This script conversion tool relies on operators which supported by MindConverter and MindSpore. Unsupported operators may not be successfully mapped to MindSpore operators. You can manually edit, or implement the mapping based on MindConverter, and contribute to our MindInsight repository. We appreciate your support for the MindSpore community.
 3. MindConverter can only guarantee that the converted model scripts require a minor revision or no revision when the inputs' shape fed to the generated model script are equal to the value of `--shape` (The batch size dimension is not limited).
 
@@ -310,9 +310,7 @@ class ConvBNReLU(nn.Sequential):
 
 ## Requirements
 
-For users converting PyTorch model script to MindSpore, there is no need to install other third party package.
-
-For users converting TensorFlow model script to MindSpore, in addition to install the TensorFlow can satisfy the pb model loading, inference and training, users also need to pip install the following third party package:
+For users using MindConverter, in addition to install the TensorFlow can satisfy the pb model loading, inference and training, users also need to pip install the following third party package (tf2onnx is not required for users that convert PyTorch model definition script to MindSpore):
 
 ```text
 onnx>=1.8.0

mindinsight/mindconverter/README_CN.md

@@ -96,7 +96,7 @@ optional arguments:
 另外，当使用基于图结构的脚本生成方案时，请确保原PyTorch项目已在Python包搜索路径中，可通过CLI进入Python交互式命令行，通过import的方式判断是否已满足；若未加入，可通过`--project_path`
 命令手动将项目路径传入，以确保MindConverter可引用到原PyTorch脚本。
 
-> 假设用户项目目录为`/home/user/project/model_training`，用户可通过如下命令手动项目添加至包搜索路径中：`export PYTHONPATH=/home/user/project/model_training:$PYTHONPATH`；
+> 假设用户项目目录为`/home/user/project/model_training`，用户可通过如下命令手动将项目添加至包搜索路径中：`export PYTHONPATH=/home/user/project/model_training:$PYTHONPATH`；
 > 此处MindConverter需要引用原PyTorch脚本，是因为PyTorch模型反向序列化过程中会引用原脚本。
 
 ### TensorFlow模型脚本迁移
@@ -281,9 +281,7 @@ mindconverter --model_file /home/user/xxx/frozen_model.pb --shape 1,224,224,3 \
 
 ## 注意事项
 
-1. PyTorch、TensorFlow、TF2ONNX(1.7.1)
-   不作为MindInsight明确声明的依赖库。若想使用基于图结构的脚本生成工具，需要用户手动安装与生成PyTorch模型版本一致的PyTorch库（MindConverter推荐使用PyTorch 1.4.0或PyTorch
-   1.6.0进行脚本生成），或TensorFlow；
+1. PyTorch、TensorFlow不作为MindInsight明确声明的依赖库。若想使用基于图结构的脚本生成工具，需要用户手动安装与生成PyTorch模型版本一致的PyTorch库（MindConverter推荐使用PyTorch 1.4.0进行脚本生成），或TensorFlow；
 2. 脚本转换工具本质上为算子驱动，对于MindConverter未维护的PyTorch或ONNX算子与MindSpore算子映射，将会出现相应的算子无法转换的问题，对于该类算子，用户可手动修改，或基于MindConverter实现映射关系，向MindInsight仓库贡献。
 3. MindConverter仅保证转换后模型脚本在输入数据尺寸与`--shape`一致的情况下，可达到无需人工修改或少量修改（`--shape`中batch size维度不受限）。
 
@@ -321,10 +319,8 @@ class ConvBNReLU(nn.Sequential):
 
 ## 三方库依赖
 
-对于PyTorch模型脚本转MindSpore的用户，无需额外安装三方依赖库。
-
-用户在使用将TensorFlow模型脚本转为MindSpore时，下列三方库未在MindInsight依赖列表（requirements.txt）中声明。用户除安装可满足导出的Pb模型加载、训练、推理的TensorFlow版本外，还需要安装（pip
-install）如下依赖库：
+用户在使用MindConverter时，下列三方库未在MindInsight依赖列表（requirements.txt）中声明。用户除安装可满足导出的Pb模型加载、训练、推理的TensorFlow版本外，还需要安装（pip
+install）如下依赖库（PyTorch模型脚本转MindSpore的用户无需安装tf2onnx）：
 
 ```text
 onnx>=1.8.0