!49339 modify save_type api doc and trainModel para

Merge pull request !49339 from 周超/master5

docs/api/lite_api_python/mindspore_lite/mindspore_lite.ModelType.rst

@@ -7,9 +7,9 @@ mindspore_lite.ModelType
 
     适用于以下场景：
 
-    1. Converter时，设置 `save_type` 参数， `ModelType` 用于定义转换生成的模型类型。
+    1. 调用 `mindspore_lite.Converter`时，设置 `save_type` 参数， `ModelType` 用于定义转换生成的模型类型。
 
-    2. Converter之后，当从文件加载或构建模型以进行推理时， `ModelType` 用于定义输入模型框架类型。
+    2. 调用 `mindspore_lite.Converter`之后，当从文件加载或构建模型以进行推理时， `ModelType` 用于定义输入模型框架类型。
 
     目前，支持以下 `ModelType` ：
 

mindspore/lite/python/api/model.py

@@ -32,10 +32,11 @@ class ModelType(Enum):
 
     Used in the following scenarios:
 
-    1. When Converter, set `save_type` parameter, `ModelType` used to define the model type generated by Converter.
+    1. When using 'mindspore_lite.Converter', set `save_type` parameter, `ModelType` used to define the model type
+    generated by Converter.
 
-    2. After Converter, When loading or building a model from file for predicting, the `ModelType` is used to define
-    Input model framework type.
+    2. After using 'mindspore_lite.Converter', when loading or building a model from file for predicting, the
+    `ModelType` is used to define Input model framework type.
 
     Currently, the following `ModelType` are supported:
 

mindspore/lite/test/st/scripts/base_functions.sh

@@ -142,12 +142,12 @@ function Convert() {
             echo "./converter_lite --fmk=${model_fmk} --modelFile=${model_file} --weightFile=${weight_file} --outputFile=${output_file}\
               --inputDataType=${in_dtype} --outputDataType=${out_dtype} --inputShape=${spec_shapes} --fp16=${fp16_weight}\
               --configFile=${config_file} --saveType=${save_type} --optimize=${optimize} \
-              --trainModel=${train_model} --inputDataFormat=${input_format}"
+              --inputDataFormat=${input_format}"
               
             ./converter_lite --fmk=${model_fmk} --modelFile=${model_file} --weightFile=${weight_file} --outputFile=${output_file}\
               --inputDataType=${in_dtype} --outputDataType=${out_dtype} --inputShape="${spec_shapes}" --fp16=${fp16_weight}\
               --configFile=${config_file} --saveType=${save_type} --optimize=${optimize} \
-              --trainModel=${train_model} --inputDataFormat=${input_format} >> "$4" 
+              --inputDataFormat=${input_format} >> "$4" 
         else
             echo "./converter_lite --fmk=${model_fmk} --modelFile=${model_file} --weightFile=${weight_file} --outputFile=${output_file}\
               --inputDataType=${in_dtype} --outputDataType=${out_dtype} --inputShape=${spec_shapes} --fp16=${fp16_weight}\

mindspore/lite/tools/converter/converter_lite/converter_flags.cc

@@ -50,10 +50,13 @@ Flags::Flags() {
   AddFlag(&Flags::saveFP16Str, "fp16",
           "Serialize const tensor in Float16 data type, only effective for const tensor in Float32 data type. on | off",
           "off");
+// Cloud infer do not support trainModel para
+#if !defined(ENABLE_CLOUD_FUSION_INFERENCE) && !defined(ENABLE_CLOUD_INFERENCE)
   AddFlag(&Flags::trainModelIn, "trainModel",
           "whether the model is going to be trained on device. "
           "true | false",
           "false");
+#endif
   AddFlag(&Flags::dec_key, "decryptKey",
           "The key used to decrypt the file, expressed in hexadecimal characters. Only valid when fmkIn is 'MINDIR'",
           "");

mindspore/lite/tools/converter/converter_lite/converter_flags.h

@@ -60,7 +60,7 @@ class Flags : public virtual mindspore::lite::FlagParser {
   std::string outputDataTypeStr;
   DataType outputDataType;
   std::string configFile;
-  std::string trainModelIn;
+  std::string trainModelIn = "false";
   bool trainModel = false;
   std::string inTensorShape;
   mutable std::map<std::string, std::vector<int64_t>> graph_input_shape_map;