update index of docs

Signed-off-by: NTing Wang <kathy.wangting@huawei.com>

lite/docs/source_en/api/context.md

-# mindspore::lite
-
-## Allocator
-
-Allocator defines a memory pool for dynamic memory malloc and memory free.
-
-## Context
-
-Context is defined for holding environment variables during runtime.
-
-**Constructors & Destructors**
-
-```
-Context()
-```
-
-Constructor of MindSpore Lite Context using default value for parameters.
-
-```
-Context(int thread_num, std::shared_ptr< Allocator > allocator, DeviceContext device_ctx)
-```
-Constructor of MindSpore Lite Context using input value for parameters.
-
-- Parameters
-
-    - `thread_num`: Define the work thread number during the runtime.
-
-    - `allocator`: Define the allocator for malloc.
-
-    - `device_ctx`: Define device information during the runtime.
-
-- Returns
-
-    The instance of MindSpore Lite Context.
-
-``` 
- ~Context()
-```
-Destructor of MindSpore Lite Context.
-
-**Public Attributes**
-
-``` 
-float16_priority
-``` 
-A **bool** value. Defaults to **false**. Prior enable float16 inference.
-
-```
-device_ctx_{DT_CPU}
-```
-A **DeviceContext** struct.
-
-``` 
-thread_num_
-``` 
-
-An **int** value. Defaults to **2**. Thread number config for thread pool.
-
-``` 
-allocator
-``` 
-
-A **std::shared_ptr<Allocator>** pointer.
-
-``` 
-cpu_bind_mode_ 
-``` 
-
-A **CpuBindMode** enum variable. Defaults to **MID_CPU**.     
-
-## CpuBindMode
-An **enum** type. CpuBindMode defined for holding bind cpu strategy argument.
-
-**Attributes**
-``` 
-MID_CPU = -1
-``` 
-Bind middle cpu first.
-
-``` 
-HIGHER_CPU = 1
-``` 
-Bind higher cpu first.
-
-``` 
-NO_BIND = 0
-``` 
-No bind.
-    
-## DeviceType
-An **enum** type. DeviceType defined for holding user's preferred backend.
-
-**Attributes**
-``` 
-DT_CPU = -1
-``` 
-CPU device type.
-
-``` 
-DT_GPU = 1
-``` 
-GPU device type.
-
-``` 
-DT_NPU = 0
-``` 
-NPU device type, not supported yet.
-    
-## DeviceContext
-
-A **struct** . DeviceContext defined for holding DeviceType.
-
-**Attributes**
-``` 
-type
-``` 
-A **DeviceType** variable. The device type.
\ No newline at end of file

lite/docs/source_en/api/vision.md

-# mindspore::lite
-
-**Functions**
-``` 
-std::string Version()
-``` 
-Global method to get a version string.
-
-- Returns
-
-    The version string of MindSpore Lite.

lite/docs/source_en/apicc/apicc.rst

+C++ API
+=======
+
+.. toctree::
+  :maxdepth: 1
+
+  class_list
+  lite
+  session
+  tensor
+  errorcode_and_metatype
\ No newline at end of file

lite/docs/source_en/api/class_list.md → lite/docs/source_en/apicc/class_list.md

-Here is a list of all namespace members with links to the namespace documentation for each member:
+# Class List
+
+Here is a list of all classes with links to the namespace documentation for each member:
 
 | Namespace | Class Name | Description |
 | --- | --- | --- |
-| mindspore::lite | [Allocator](https://www.mindspore.cn/lite/docs/en/master/api/context.html#allocator) | Allocator defines a memory pool for dynamic memory malloc and memory free. |
-| mindspore::lite | [Context](https://www.mindspore.cn/lite/docs/en/master/api/context.html#context) | Context defines for holding environment variables during runtime. |
-| mindspore::lite | [ModelImpl](https://www.mindspore.cn/lite/docs/en/master/api/model.html#modelimpl) | ModelImpl defines the implement class of Model in MindSpore Lite. |
-| mindspore::lite | [PrimitiveC](https://www.mindspore.cn/lite/docs/en/master/api/model.html#primitivec) | Primitive defines as prototype of operator. |
-| mindspore::lite | [Model](https://www.mindspore.cn/lite/docs/en/master/api/model.html#model) | Model defines model in MindSpore Lite for managing graph. |
-| mindspore::lite | [ModelBuilder](https://www.mindspore.cn/lite/docs/en/master/api/model.html#modelbuilder) | ModelBuilder is defined by MindSpore Lite. |
-| mindspore::session | [LiteSession](https://www.mindspore.cn/lite/docs/en/master/api/lite_session.html#litesession) | LiteSession defines session in MindSpore Lite for compiling Model and forwarding model. |
-| mindspore::tensor | [MSTensor](https://www.mindspore.cn/lite/docs/en/master/api/ms_tensor.html#mstensor) | MSTensor defines tensor in MindSpore Lite. |
\ No newline at end of file
+| mindspore::lite | [Allocator](https://www.mindspore.cn/lite/docs/en/master/apicc/lite.html#allocator) | Allocator defines a memory pool for dynamic memory malloc and memory free. |
+| mindspore::lite | [Context](https://www.mindspore.cn/lite/docs/en/master/apicc/lite.html#context) | Context defines for holding environment variables during runtime. |
+| mindspore::lite | [ModelImpl](https://www.mindspore.cn/lite/docs/en/master/apicc/lite.html#modelimpl) | ModelImpl defines the implement class of Model in MindSpore Lite. |
+| mindspore::lite | [PrimitiveC](https://www.mindspore.cn/lite/docs/en/master/apicc/lite.html#primitivec) | Primitive defines as prototype of operator. |
+| mindspore::lite | [Model](https://www.mindspore.cn/lite/docs/en/master/apicc/lite.html#model) | Model defines model in MindSpore Lite for managing graph. |
+| mindspore::lite | [ModelBuilder](https://www.mindspore.cn/lite/docs/en/master/apicc/lite.html#modelbuilder) | ModelBuilder is defined by MindSpore Lite. |
+| mindspore::session | [LiteSession](https://www.mindspore.cn/lite/docs/en/master/apicc/session.html#litesession) | LiteSession defines session in MindSpore Lite for compiling Model and forwarding model. |
+| mindspore::tensor | [MSTensor](https://www.mindspore.cn/lite/docs/en/master/apicc/tensor.html#mstensor) | MSTensor defines tensor in MindSpore Lite. |
\ No newline at end of file

lite/docs/source_en/api/errorcode_and_metatype.md → lite/docs/source_en/apicc/errorcode_and_metatype.md

+# ErrorCode and MetaType
+
 Description of error code and meta type supported in MindSpore Lite.
 
-# ErrorCode
+## ErrorCode
 
 | Definition  | Value | Description |
 | ---  | --- | --- |
@@ -23,7 +25,7 @@ Description of error code and meta type supported in MindSpore Lite.
 | RET_INFER_ERR | -501 | Failed to infer shape. | 
 | RET_INFER_INVALID | -502 | Invalid infer shape before runtime. | 
 
-# MetaType
+## MetaType
 An **enum** type.
 
 | Type Name | Definition | Value | Description |

lite/docs/source_en/api/model.md → lite/docs/source_en/apicc/lite.md

-# mindspore::lite
-## ModelImpl
-ModelImpl defines the implement class of Model in MindSpore Lite.
-
-## PrimitiveC
-Primitive is defined as prototype of operator.
-
-## Model
-Model defines model in MindSpore Lite for managing graph.
-
-**Constructors & Destructors**
-```
-Model()
-```
-
-Constructor of MindSpore Lite Model using default value for parameters.
-
-```
-virtual ~Model()
-```
-
-Destructor of MindSpore Lite Model.
-
-**Public Member Functions**
-```
-PrimitiveC* GetOp(const std::string &name) const
-```
-Get MindSpore Lite Primitive by name.
-
-- Parameters 
-
-    - `name`: Define name of primitive to be returned.
-    
-- Returns 
-
-    The pointer of MindSpore Lite Primitive.
-
-```     
-const schema::MetaGraph* GetMetaGraph() const
-```
-Get graph defined in flatbuffers.
-
-- Returns  
-
-    The pointer of graph defined in flatbuffers.
-
-```
-void FreeMetaGraph()
-```
-Free MetaGraph in MindSpore Lite Model.
-
-**Static Public Member Functions**
-```
-static Model *Import(const char *model_buf, size_t size)
-```
-Static method to create a Model pointer.
-
-- Parameters    
-
-    - `model_buf`: Define the buffer read from a model file.   
-
-    - `size`: variable. Define bytes number of model buffer.
-
-- Returns  
-
-    Pointer of MindSpore Lite Model.
-        
-**Public Attributes**
-```
- model_impl_ 
-```
-The **pointer** of implement of model in MindSpore Lite. Defaults to **nullptr**.
-
-## ModelBuilder
-ModelBuilder is defined by MindSpore Lite.
-
-**Constructors & Destructors**
-```
-ModelBuilder()
-```
-
-Constructor of MindSpore Lite ModelBuilder using default value for parameters.
-
-```
-virtual ~ModelBuilder()
-```
-
-Destructor of MindSpore Lite ModelBuilder.
-
-**Public Member Functions**
-```
-virtual std::string AddOp(const PrimitiveC &op, const std::vector<OutEdge> &inputs)
-```
-
-Add primitive into model builder for model building.
-
-- Parameters    
-
-    - `op`: Define the primitive to be added.   
-
-    - `inputs`: Define input edge of primitive to be added.
-    
-- Returns   
-
-    ID of the added primitive.
-
-```  
-const schema::MetaGraph* GetMetaGraph() const
-```
-Get graph defined in flatbuffers.
-
-- Returns   
-
-    The pointer of graph defined in flatbuffers.
-
-```
-virtual Model *Construct()
-```
-Finish constructing the model.
-
-## OutEdge
-**Attributes**
-```
-nodeId
-```
-A **string** variable. ID of a node linked by this edge.
-
-```
-outEdgeIndex
-```
-A **size_t** variable. Index of this edge.
\ No newline at end of file
+# mindspore::lite context
+
+## Allocator
+
+Allocator defines a memory pool for dynamic memory malloc and memory free.
+
+## Context
+
+Context is defined for holding environment variables during runtime.
+
+**Constructors & Destructors**
+
+```
+Context()
+```
+
+Constructor of MindSpore Lite Context using default value for parameters.
+
+```
+Context(int thread_num, std::shared_ptr< Allocator > allocator, DeviceContext device_ctx)
+```
+Constructor of MindSpore Lite Context using input value for parameters.
+
+- Parameters
+
+    - `thread_num`: Define the work thread number during the runtime.
+
+    - `allocator`: Define the allocator for malloc.
+
+    - `device_ctx`: Define device information during the runtime.
+
+- Returns
+
+    The instance of MindSpore Lite Context.
+
+``` 
+ ~Context()
+```
+Destructor of MindSpore Lite Context.
+
+**Public Attributes**
+
+``` 
+float16_priority
+``` 
+A **bool** value. Defaults to **false**. Prior enable float16 inference.
+
+```
+device_ctx_{DT_CPU}
+```
+A **DeviceContext** struct.
+
+``` 
+thread_num_
+``` 
+
+An **int** value. Defaults to **2**. Thread number config for thread pool.
+
+``` 
+allocator
+``` 
+
+A **std::shared_ptr<Allocator>** pointer.
+
+``` 
+cpu_bind_mode_ 
+``` 
+
+A **CpuBindMode** enum variable. Defaults to **MID_CPU**.     
+
+## ModelImpl
+ModelImpl defines the implement class of Model in MindSpore Lite.
+
+## PrimitiveC
+Primitive is defined as prototype of operator.
+
+## Model
+Model defines model in MindSpore Lite for managing graph.
+
+**Constructors & Destructors**
+```
+Model()
+```
+
+Constructor of MindSpore Lite Model using default value for parameters.
+
+```
+virtual ~Model()
+```
+
+Destructor of MindSpore Lite Model.
+
+**Public Member Functions**
+```
+PrimitiveC* GetOp(const std::string &name) const
+```
+Get MindSpore Lite Primitive by name.
+
+- Parameters 
+
+    - `name`: Define name of primitive to be returned.
+    
+- Returns 
+
+    The pointer of MindSpore Lite Primitive.
+
+```     
+const schema::MetaGraph* GetMetaGraph() const
+```
+Get graph defined in flatbuffers.
+
+- Returns  
+
+    The pointer of graph defined in flatbuffers.
+
+```
+void FreeMetaGraph()
+```
+Free MetaGraph in MindSpore Lite Model.
+
+**Static Public Member Functions**
+```
+static Model *Import(const char *model_buf, size_t size)
+```
+Static method to create a Model pointer.
+
+- Parameters    
+
+    - `model_buf`: Define the buffer read from a model file.   
+
+    - `size`: variable. Define bytes number of model buffer.
+
+- Returns  
+
+    Pointer of MindSpore Lite Model.
+        
+**Public Attributes**
+```
+ model_impl_ 
+```
+The **pointer** of implement of model in MindSpore Lite. Defaults to **nullptr**.
+
+## ModelBuilder
+ModelBuilder is defined by MindSpore Lite.
+
+**Constructors & Destructors**
+```
+ModelBuilder()
+```
+
+Constructor of MindSpore Lite ModelBuilder using default value for parameters.
+
+```
+virtual ~ModelBuilder()
+```
+
+Destructor of MindSpore Lite ModelBuilder.
+
+**Public Member Functions**
+```
+virtual std::string AddOp(const PrimitiveC &op, const std::vector<OutEdge> &inputs)
+```
+
+Add primitive into model builder for model building.
+
+- Parameters    
+
+    - `op`: Define the primitive to be added.   
+
+    - `inputs`: Define input edge of primitive to be added.
+    
+- Returns   
+
+    ID of the added primitive.
+
+```  
+const schema::MetaGraph* GetMetaGraph() const
+```
+Get graph defined in flatbuffers.
+
+- Returns   
+
+    The pointer of graph defined in flatbuffers.
+
+```
+virtual Model *Construct()
+```
+Finish constructing the model.
+
+## OutEdge
+**Attributes**
+```
+nodeId
+```
+A **string** variable. ID of a node linked by this edge.
+
+```
+outEdgeIndex
+```
+A **size_t** variable. Index of this edge.
+
+## CpuBindMode
+An **enum** type. CpuBindMode defined for holding bind cpu strategy argument.
+
+**Attributes**
+``` 
+MID_CPU = -1
+``` 
+Bind middle cpu first.
+
+``` 
+HIGHER_CPU = 1
+``` 
+Bind higher cpu first.
+
+``` 
+NO_BIND = 0
+``` 
+No bind.
+    
+## DeviceType
+An **enum** type. DeviceType defined for holding user's preferred backend.
+
+**Attributes**
+``` 
+DT_CPU = -1
+``` 
+CPU device type.
+
+``` 
+DT_GPU = 1
+``` 
+GPU device type.
+
+``` 
+DT_NPU = 0
+``` 
+NPU device type, not supported yet.
+    
+## DeviceContext
+
+A **struct** . DeviceContext defined for holding DeviceType.
+
+**Attributes**
+``` 
+type
+``` 
+A **DeviceType** variable. The device type.
+
+## Version
+
+``` 
+std::string Version()
+``` 
+Global method to get a version string.
+
+- Returns
+
+    The version string of MindSpore Lite.
\ No newline at end of file

lite/docs/source_en/api/lite_session.md → lite/docs/source_en/apicc/session.md

-# mindspore::session 
-
-## LiteSession
-
-LiteSession defines session in MindSpore Lite for compiling Model and forwarding model.
-
-**Constructors & Destructors**
-```
-LiteSession()
-```
-Constructor of MindSpore Lite LiteSession using default value for parameters.
-    
-```
-~LiteSession()
-```
-Destructor of MindSpore Lite LiteSession.
-
-**Public Member Functions**
-```
-virtual void BindThread(bool if_bind)
-```
-Attempt to bind or unbind threads in the thread pool to or from the specified cpu core.
-
-- Parameters
-
-    - `if_bind`: Define whether to bind or unbind threads.
-
-```
-virtual int CompileGraph(lite::Model *model)
-```
-Compile MindSpore Lite model. 
-
-> Note: CompileGraph should be called before RunGraph.
-
-- Parameters
-
-    - `model`: Define the model to be compiled.
-
-- Returns
-
-    STATUS as an error code of compiling graph, STATUS is defined in errorcode.h.
-
-```
-virtual std::vector <tensor::MSTensor *> GetInputs() const
-```
-Get input MindSpore Lite MSTensors of model.
-
-- Returns
-
-    The vector of MindSpore Lite MSTensor.
-
-```   
-std::vector <tensor::MSTensor *> GetInputsByName(const std::string &node_name) const
-```
-Get input MindSpore Lite MSTensors of model by node name.
-
-- Parameters
-
-    - `node_name`: Define node name.
-
-- Returns
-
-    The vector of MindSpore Lite MSTensor.
-    
-```
-virtual int RunGraph(const KernelCallBack &before = nullptr, const KernelCallBack &after = nullptr)
-```
-Run session with callback. 
-    
-> Note: RunGraph should be called after CompileGraph.
-    
-- Parameters
-
-    - `before`: Define a call_back_function to be called before running each node.
-
-    - `after`: Define a call_back_function called after running each node.
-
-- Returns
-
-    STATUS as an error code of running graph, STATUS is defined in errorcode.h.
-
-```   
-virtual std::unordered_map<std::string, std::vector<mindspore::tensor::MSTensor *>> GetOutputMapByNode() const
-```
-Get output MindSpore Lite MSTensors of model mapped by node name.
-
-- Returns
-
-    The map of output node name and MindSpore Lite MSTensor.
-
-```      
-virtual std::vector <tensor::MSTensor *> GetOutputsByNodeName(const std::string &node_name) const
-```
-Get output MindSpore Lite MSTensors of model by node name.
-
-- Parameters
-
-    - `node_name`: Define node name.
-
-- Returns
-
-    The vector of MindSpore Lite MSTensor.
-
-```
-virtual std::unordered_map <std::string, mindspore::tensor::MSTensor *> GetOutputMapByTensor() const
-```
-Get output MindSpore Lite MSTensors of model mapped by tensor name.
-
-- Returns
-
-    The map of output tensor name and MindSpore Lite MSTensor.
-
-```        
-virtual std::vector <std::string> GetOutputTensorNames() const
-```
-Get name of output tensors of model compiled by this session.
-
-- Returns
-
-    The vector of string as output tensor names in order.
-
-```      
-virtual mindspore::tensor::MSTensor *GetOutputByTensorName(const std::string &tensor_name) const
-```
-Get output MindSpore Lite MSTensors of model by tensor name.
-
-- Parameters
-
-    - `tensor_name`: Define tensor name.
-
-- Returns
-
-    Pointer of MindSpore Lite MSTensor.
-
-```     
-virtual mindspore::tensor::MSTensor *GetOutputByTensorName(const std::string &tensor_name) const
-```
-Get output MindSpore Lite MSTensors of model by tensor name.
-    
-- Parameters
-
-    - `tensor_name`: Define tensor name.
-
-- Returns
-
-  Pointer of MindSpore Lite MSTensor.
-
-```      
-virtual int Resize(const std::vector <tensor::MSTensor *> &inputs)
-
-```
-Resize inputs shape.
-
-- Parameters
-
-    - `inputs`: Define the new inputs shape.
-
-- Returns
-
-    STATUS as an error code of resize inputs, STATUS is defined in errorcode.h.
-
-**Static Public Member Functions**
-
-```
-static LiteSession *CreateSession(lite::Context *context)
-```
-Static method to create a LiteSession pointer.
-
-- Parameters
-
-    - `context`: Define the context of session to be created.
-
-- Returns
-
-    Pointer of MindSpore Lite LiteSession.
-
-        
-## CallBackParam
-
-CallBackParam defines input arguments for callBack function.
-    
-**Attributes**
-```
-name_callback_param
-```
-A **string** variable. Node name argument.
-
-```
-type_callback_param
-```
+# mindspore::session 
+
+## LiteSession
+
+LiteSession defines session in MindSpore Lite for compiling Model and forwarding model.
+
+**Constructors & Destructors**
+```
+LiteSession()
+```
+Constructor of MindSpore Lite LiteSession using default value for parameters.
+    
+```
+~LiteSession()
+```
+Destructor of MindSpore Lite LiteSession.
+
+**Public Member Functions**
+```
+virtual void BindThread(bool if_bind)
+```
+Attempt to bind or unbind threads in the thread pool to or from the specified cpu core.
+
+- Parameters
+
+    - `if_bind`: Define whether to bind or unbind threads.
+
+```
+virtual int CompileGraph(lite::Model *model)
+```
+Compile MindSpore Lite model. 
+
+> Note: CompileGraph should be called before RunGraph.
+
+- Parameters
+
+    - `model`: Define the model to be compiled.
+
+- Returns
+
+    STATUS as an error code of compiling graph, STATUS is defined in errorcode.h.
+
+```
+virtual std::vector <tensor::MSTensor *> GetInputs() const
+```
+Get input MindSpore Lite MSTensors of model.
+
+- Returns
+
+    The vector of MindSpore Lite MSTensor.
+
+```   
+std::vector <tensor::MSTensor *> GetInputsByName(const std::string &node_name) const
+```
+Get input MindSpore Lite MSTensors of model by node name.
+
+- Parameters
+
+    - `node_name`: Define node name.
+
+- Returns
+
+    The vector of MindSpore Lite MSTensor.
+    
+```
+virtual int RunGraph(const KernelCallBack &before = nullptr, const KernelCallBack &after = nullptr)
+```
+Run session with callback. 
+    
+> Note: RunGraph should be called after CompileGraph.
+    
+- Parameters
+
+    - `before`: Define a call_back_function to be called before running each node.
+
+    - `after`: Define a call_back_function called after running each node.
+
+- Returns
+
+    STATUS as an error code of running graph, STATUS is defined in errorcode.h.
+
+```   
+virtual std::unordered_map<std::string, std::vector<mindspore::tensor::MSTensor *>> GetOutputMapByNode() const
+```
+Get output MindSpore Lite MSTensors of model mapped by node name.
+
+- Returns
+
+    The map of output node name and MindSpore Lite MSTensor.
+
+```      
+virtual std::vector <tensor::MSTensor *> GetOutputsByNodeName(const std::string &node_name) const
+```
+Get output MindSpore Lite MSTensors of model by node name.
+
+- Parameters
+
+    - `node_name`: Define node name.
+
+- Returns
+
+    The vector of MindSpore Lite MSTensor.
+
+```
+virtual std::unordered_map <std::string, mindspore::tensor::MSTensor *> GetOutputMapByTensor() const
+```
+Get output MindSpore Lite MSTensors of model mapped by tensor name.
+
+- Returns
+
+    The map of output tensor name and MindSpore Lite MSTensor.
+
+```        
+virtual std::vector <std::string> GetOutputTensorNames() const
+```
+Get name of output tensors of model compiled by this session.
+
+- Returns
+
+    The vector of string as output tensor names in order.
+
+```      
+virtual mindspore::tensor::MSTensor *GetOutputByTensorName(const std::string &tensor_name) const
+```
+Get output MindSpore Lite MSTensors of model by tensor name.
+
+- Parameters
+
+    - `tensor_name`: Define tensor name.
+
+- Returns
+
+    Pointer of MindSpore Lite MSTensor.
+
+```     
+virtual mindspore::tensor::MSTensor *GetOutputByTensorName(const std::string &tensor_name) const
+```
+Get output MindSpore Lite MSTensors of model by tensor name.
+    
+- Parameters
+
+    - `tensor_name`: Define tensor name.
+
+- Returns
+
+  Pointer of MindSpore Lite MSTensor.
+
+```      
+virtual int Resize(const std::vector <tensor::MSTensor *> &inputs)
+
+```
+Resize inputs shape.
+
+- Parameters
+
+    - `inputs`: Define the new inputs shape.
+
+- Returns
+
+    STATUS as an error code of resize inputs, STATUS is defined in errorcode.h.
+
+**Static Public Member Functions**
+
+```
+static LiteSession *CreateSession(lite::Context *context)
+```
+Static method to create a LiteSession pointer.
+
+- Parameters
+
+    - `context`: Define the context of session to be created.
+
+- Returns
+
+    Pointer of MindSpore Lite LiteSession.
+
+        
+## CallBackParam
+
+CallBackParam defines input arguments for callBack function.
+    
+**Attributes**
+```
+name_callback_param
+```
+A **string** variable. Node name argument.
+
+```
+type_callback_param
+```
 A **string** variable. Node type argument.
\ No newline at end of file

lite/docs/source_en/index.rst

@@ -11,5 +11,6 @@ MindSpore Lite Documentation
    :maxdepth: 1
 
    architecture
+   apicc/apicc
    operator_list
    glossary

tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learn.md → tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learning.md

-# MobileNetV2 增量学习
-`CPU` `Ascend` `GPU` `模型开发` `中级` `高级`
-
-<!-- TOC -->
-
-- [增量学习](#增量学习)
-  - [概述](#概述)
-  - [任务描述及准备](#任务描述及准备)
-    - [环境配置](#环境配置)
-    - [下载代码](#下载代码)
-    - [准备预训练模型](#准备预训练模型)
-    - [准备数据](#准备数据)
-  - [预训练模型加载代码详解](#预训练模型加载代码详解)
-  - [参数简介](#参数简介)
-    - [运行Python文件](#运行python文件)
-    - [运行Shell脚本](#运行shell脚本)
-  - [加载增量学习训练](#加载增量学习训练)
-    - [CPU加载训练](#cpu加载训练)
-    - [GPU加载训练](#gpu加载训练)
-    - [Ascend加载训练](#ascend加载训练)
-    - [增量学习训练结果](#增量学习训练结果)
-  - [验证增量学习训练模型](#验证增量学习训练模型)
-    - [验证模型](#验证模型)
-    - [验证结果](#验证结果)
-<!-- /TOC -->
-
-<a href="https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learn.md" target="_blank"><img src="../_static/logo_source.png"></a>&nbsp;&nbsp;
-
-## 概述
-
-计算机视觉任务中，从头开始训练一个网络耗时巨大，需要大量计算能力。预训练模型选择的常见的OpenImage、ImageNet、VOC、COCO等公开大型数据集，规模达到几十万甚至超过上百万张。大部分任务数据规模较大，训练网络模型时，如果不使用预训练模型，从头开始训练网络，需要消耗大量的时间与计算能力，模型容易陷入局部极小值和过拟合。因此大部分任务都会选择预训练模型，在其上做增量学习。  
-
-MindSpore是一个多元化的机器学习框架。既可以在手机等端侧和PC等设备上运行，也可以在云上的服务器集群上运行。目前MobileNetV2支持在Windows系统中使用单核CPU做增量学习，在EulerOS、Ubuntu系统中使用单个或者多个Ascend AI处理器或GPU中做增量学习，本教程将会介绍如何在不同系统与处理器下的MindSpore框架中做增量学习的训练与验证。
-
-目前，Window上暂只支持支持CPU，Ubuntu与EulerOS上支持CPU、GPU与Ascend AI处理器三种处理器。 
-
->你可以在这里找到完整可运行的样例代码：https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/mobilenetv2
-
-## 任务描述及准备
-
-### 环境配置
-
-若在本地环境运行，需要安装MindSpore框架，配置CPU、GPU或Ascend AI处理器。若在华为云环境上运行，不需要安装MindSpore框架，不需要配置Ascend AI处理器、CPU与GPU，可以跳过本小节。
-
-1. 安装MindSpore框架
-    在EulerOS、Ubuntu或者Windows等系统上需要根据系统和处理器架构[安装对应版本MindSpore框架](https://www.mindspore.cn/install)。
-
-2. 配置CPU环境  
-    使用CPU时，在代码中，需要在调用CPU开始训练或测试前，按照如下代码设置：
-
-    ```Python
-    if config.platform == "CPU":
-        context.set_context(mode=context.GRAPH_MODE, device_target=config.platform, \
-            save_graphs=False)
-    ```
-
-3. 配置GPU环境  
-    使用GPU时，在代码中，需要在调用GPU开始训练或测试前，按照如下代码设置：
-
-    ```Python
-    elif config.platform == "GPU":
-        context.set_context(mode=context.GRAPH_MODE, device_target=config.platform, \
-            save_graphs=False)
-        init("nccl")
-        context.set_auto_parallel_context(device_num=get_group_size(),
-                                          parallel_mode=ParallelMode.DATA_PARALLEL,
-                                          mirror_mean=True)
-    ```
-
-4. 配置Ascend环境  
-    以Ascend 910 AI处理器为例，1个8个处理器环境的json配置文件`hccl_config.json`示例如下。单/多处理器环境可以根据以下示例调整`"server_count"`与`device`：
-
-    ```json
-    {
-        "version": "1.0",
-        "server_count": "1",
-        "server_list": [
-            {
-                "server_id": "10.155.111.140",
-                "device": [ba
-                    {"device_id": "0","device_ip": "192.1.27.6","rank_id": "0"},
-                    {"device_id": "1","device_ip": "192.2.27.6","rank_id": "1"},
-                    {"device_id": "2","device_ip": "192.3.27.6","rank_id": "2"},
-                    {"device_id": "3","device_ip": "192.4.27.6","rank_id": "3"},
-                    {"device_id": "4","device_ip": "192.1.27.7","rank_id": "4"},
-                    {"device_id": "5","device_ip": "192.2.27.7","rank_id": "5"},
-                    {"device_id": "6","device_ip": "192.3.27.7","rank_id": "6"},
-                    {"device_id": "7","device_ip": "192.4.27.7","rank_id": "7"}],
-                "host_nic_ip": "reserve"
-            }
-        ],
-        "status": "completed"
-    }
-    ```
-
-    使用Ascend AI处理器时，在代码中，需要在调用Ascend AI处理器开始训练或测试前，按照如下代码设置：
-
-    ```Python
-    elif config.platform == "Ascend":
-        context.set_context(mode=context.GRAPH_MODE, device_target=config.platform, \
-            device_id=config.device_id, save_graphs=False)
-        if config.run_distribute:
-            context.set_auto_parallel_context(device_num=config.rank_size,
-                                              parallel_mode=ParallelMode.DATA_PARALLEL,
-                                              parameter_broadcast=True, mirror_mean=True)
-            auto_parallel_context().set_all_reduce_fusion_split_indices([140])
-            init()
-    ...
-    ```
-
-### 下载代码  
-
-在Gitee中克隆[MindSpore开源项目仓库](https://gitee.com/mindspore/mindspore.git)，进入`./model_zoo/official/cv/mobilenetv2/`。  
-
-```bash
-git clone https://gitee.com/mindspore/mindspore/pulls/5766
-cd ./mindspore/model_zoo/official/cv/mobilenetv2
-```
-
-代码结构如下：
-
-```
-├─MobileNetV2
-    ├─README.md     # descriptions about MobileNetV2
-    ├─scripts
-    │   run_train.sh   # Shell script for train with Ascend or GPU
-    │   run_eval.sh    # Shell script for evaluation with Ascend or GPU
-    ├─src
-    │   config.py      # parameter configuration
-    │   dataset.py     # creating dataset
-    │   launch.py      # start Python script
-    │   lr_generator.py     # learning rate config
-    │   mobilenetV2.py      # MobileNetV2 architecture
-    │   models.py        # net utils to load ckpt_file, define_net...
-    │   utils.py        # net utils to switch precision, set_context and so on
-    ├─train.py      # training script
-    └─eval.py       #  evaluation script
-```
-
-运行增量学习训练与测试时，Windows、Ubuntu与EulersOS上可以使用Python文件`train.py`与`eval.py`，Ubuntu与EulerOS上还可以使用Shell脚本文件`run_train.sh`与`run_eval.sh`。
-
-使用脚本文件`run_train.sh`时，该文件会将运行`launch.py`并且将参数传入`launch.py`，`launch.py`根据分配的CPU、GPU或Ascend AI处理器数量，启动单个/多个进程运行`train.py`，每一个进程分配对应的一个处理器。
-
-### 准备预训练模型  
-
-[下载预训练模型](https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetV2.ckpt)到以下目录：  
-`./pretrain_checkpoint/[pretrain_checkpoint_file]`  
-
-```Python
-mkdir pretrain_checkpoint
-wget -P ./pretrain_checkpoint https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetV2.ckpt
-```
-
-### 准备数据
-
-准备ImageFolder格式管理的数据集，运行`run_train.sh`时加入`[dataset_path]`参数，运行`train.py`时加入`--dataset_path [dataset_path]`参数：
-
-数据集结构如下：
-
-```
-└─ImageFolder
-    ├─train  
-    │   class1Folder
-    │   class2Folder
-    │   ......
-    └─eval
-        class1Folder
-        class2Folder
-        ......
-```
-
-## 预训练模型加载代码详解
-
-在增量学习时，需要加载预训练模型。不同数据集和任务中特征提取层（卷积层）分布趋于一致，但是特征向量的组合（全连接层）不相同，分类数量（全连接层output_size)通常也不一致。在增量学习时，只加载与训练特征提取层参数，不加载与训练全连接层参数；在微调与初始训练时，加载与训练特征提取层参数与全连接层参数。
-
-在训练与测试之前，首先按照代码第1行，构建MobileNetV2的backbone网络，head网络，并且构建包含这两个子网络的MobileNetV2网络。代码第4-11行展示了如何在`fine_tune`训练模式下，将预训练模型加载入`net`(MobileNetV2)；在`incremental_learn`训练模式下，将预训练模型分别加载入backbone_net子网络，并且冻结backbone_net中的参数，不参与训练。代码第22-24行展示了如何冻结网络参数。
-
-```Python
- 1:  backbone_net, head_net, net = define_net(args_opt, config)
- 2:  ...
- 3:  def define_net(args, config):
- 4:      backbone_net = MobileNetV2Backbone(platform=args.platform)
- 5:      head_net = MobileNetV2Head(input_channel=backbone_net.out_channels, num_classes=config.num_classes)
- 6:      net = mobilenet_v2(backbone_net, head_net)  
- 7:      if args.pretrain_ckpt:
- 8:          if args.train_method == "fine_tune":
- 9:              load_ckpt(net, args.pretrain_ckpt)
-10:          elif args.train_method == "incremental_learn":
-11:              load_ckpt(backbone_net, args.pretrain_ckpt, trainable=False)
-12:          elif args.train_method == "train":
-13:              pass
-14:          else:
-15:              raise ValueError("must input the usage of pretrain_ckpt when the pretrain_ckpt isn't None")
-16:      return backbone_net, head_net, net
-17:  ...
-18:  def load_ckpt(network, pretrain_ckpt_path, trainable=True):
-19:      """load the pretrain checkpoint and with the param trainable or not"""
-20:      param_dict = load_checkpoint(pretrain_ckpt_path)
-21:      load_param_into_net(network, param_dict)
-22:      if not trainable:
-23:          for param in network.get_parameters():
-24:              param.requires_grad = False
-```
-
-## 参数简介  
-
-### 运行Python文件
-在Windows与Linux系统上训练时，运行`train.py`时需要传入`dataset_path`、`platform`、`train_method`与`pretrain_ckpt`四个参数。验证时，运行`eval.py`并且传入`dataset_path`、`platform`、`pretrain_ckpt`与`head_ckpt`四个参数。
-
-```Shell
-# Windows/Linux train with Python file
-python train.py --dataset_path [dataset_path] --platform [platform] --pretrain_ckpt [pretrain_checkpoint_path] --train_method[("train", "fine_tune", "incremental_learn")]
-
-# Windows/Linux eval with Python file
-python eval.py --dataset_path [dataset_path] --platform [platform] --pretrain_ckpt [pretrain_checkpoint_path] --head_ckpt [head_ckpt_path]
-```
-
-- `--dataset_path`：训练与验证数据集地址，无默认值，用户训练/验证时必须输入。
-- `--platform`：处理器类型，默认为“Ascend”，可以设置为“CPU”或"GPU"。
-- `--train_method`：训练方法，必须输入“train"、"fine_tune"和incremental_learn"其中一个。
-- `--pretrain_ckpt`：增量训练或调优时，需要传入pretrain_checkpoint文件路径以加载预训练好的模型参数权重。
-- `--head_ckpt`：增量训练模型验证时，需要传入head_net预训练模型路径以加载预训练好的模型参数权重。
-
-
-### 运行Shell脚本
-在Linux系统上时，可以选择运行Shell脚本文件`./scripts/run_train.sh`与`./scripts/run_eval.sh`。运行时需要在交互界面中同时传入参数。  
-
-```Shell
-# Windows doesn't support Shell
-# Linux train with Shell script 
-sh run_train.sh [PLATFORM] [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [RANK_TABLE_FILE] [DATASET_PATH] [TRAIN_METHOD] [CKPT_PATH]
-
-# Linux eval with Shell script for incremental learn
-sh run_eval.sh [PLATFORM] [DATASET_PATH] [PRETRAIN_CKPT_PATH] [HEAD_CKPT_PATH]
-```
-
-- `[PLATFORM]`：处理器类型，默认为“Ascend”，可以设置为“GPU”。
-- `[DEVICE_NUM]`：每个节点（一台服务器/PC相当于一个节点）进程数量，建议设置为机器上Ascend AI处理器数量或GPU数量。
-- `[VISIABLE_DEVICES(0,1,2,3,4,5,6,7)]`：字符串格式的的设备ID，训练将会根据`[VISIABLE_DEVICES]`将进程绑定到对应ID的设备上，多个设备ID之间使用','分隔，建议ID数量与进程数量相同。
-- `[RANK_TABLE_FILE]`：platform选择Ascend时，需要配置Ascend的配置Json文件,。
-- `[DATASET_PATH]`：训练与验证数据集地址，无默认值，用户训练/验证时必须输入。
-- `[CKPT_PATH]`：增量训练或调优时，需要传入checkpoint文件路径以加载预训练好的模型参数权重。
-- `[TRAIN_METHOD]`：训练方法，必须输入`train`、`fine_tune`和`incremental_learn`其中一个。
-- `[PRETRAIN_CKPT_PATH]`：针对增量学习的模型做验证时，需要输入主干网络层保存模型路径。
-- `[HEAD_CKPT_PATH]`：针对增量学习的模型做验证时，需要输入全连接层保存模型路径。
-
-## 加载增量学习训练  
-
-Windows系统上，MobileNetV2做增量学习训练时，只能运行`train.py`。Linux系统上，使用MobileNetV2做增量学习训练时，可以选择运行`run_train.sh`， 并在运行Shell脚本文件时传入[参数](#参数简介)。
-
-Windows系统输出信息到交互式命令行，Linux系统环境下运行`run_train.sh`时，命令行结尾使用`&> [log_file_path]`将标准输出与错误输出写入log文件。 增量学习成功开始训练，`./train/device*/log*.log`中会持续写入每一个epoch的训练时间与Loss等信息。若未成功，上述log文件会写入失败报错信息。
-
-### CPU加载训练  
-
-- 设置节点数量
-
-  目前运行`train.py`时仅支持单处理器，不需要调整处理器数量。运行`run_train.sh`文件时，`CPU`设备默认为单处理器，目前暂不支持修改CPU数量。
-
-- 开始增量训练
-
-  使用样例1：通过Python文件调用1个CPU处理器。
-
-    ```Shell
-    # Windows or Linux with Python
-    python train.py --platform CPU --dataset_path /store/dataset/OpenImage/train/ -- train_method incremental_learn --pretrain_ckpt ./pretrain_checkpoint/mobilenetV2.ckpt
-    ```
-
-  使用样例2：通过Shell文件调用1个CPU处理器。
-
-    ```Shell
-    # Linux with Shell
-    sh run_train.sh CPU /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt
-    ```
-
-### GPU加载训练  
-
-- 设置节点数量
-
-  目前运行`train.py`时仅支持单处理器，不需要调整节点数量。运行`run_train.sh`文件时，设置`[nproc_per_node]`为GPU数量， `[visible_devices]`为可使用的处理器编号，即GPU的ID，可以选择一个或多个设备ID，使用`,`隔开。
-
-- 开始增量训练
-
-  - 使用样例1：通过Python文件调用1个GPU处理器。
-
-    ```Shell
-    # Windows or Linux with Python
-    python train.py --platform GPU --dataset_path /store/dataset/OpenImage/train/ --pretrain_ckpt ./pretrain_checkpoint/mobilenetV2.ckpt --train_method incremental_learn
-    ```
-
-  - 使用样例2：通过Shell脚本调用1个GPU处理器，设备ID为`“0”`。
-
-    ```Shell
-    # Linux with Shell
-    sh run_train.sh GPU 1 0 /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt
-    ```
-
-  - 使用样例3：通过Shell脚本调用8个GPU处理器，设备ID为`“0,1,2,3,4,5,6,7”`。
-
-    ```Shell
-    # Linux with Shell
-    sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt
-    ```
-
-### Ascend加载训练  
-
-- 设置节点数量
-
-  目前运行`train.py`时仅支持单处理器，不需要调整节点数量。运行`run_train.sh`文件时，设置`[nproc_per_node]`为Ascend AI处理器数量， `[visible_devices]`为可使用的处理器编号，即Ascend AI处理器的ID，8卡服务器可以选择0-7中一个或多个设备ID，使用`,`隔开。Ascend节点处理器数量目前只能设置为1或者8。
-
-- 开始增量训练
-
-  - 使用样例1：通过Python文件调用1个Ascend处理器。
-
-    ```Shell
-    # Windows or Linux with Python
-    python train.py --platform Ascend --dataset_path /store/dataset/OpenImage/train/ --train_method incremental_learn --pretrain_ckpt  ./pretrain_checkpoint/mobilenetV2.ckpt
-    ```
-
-  - 使用样例2：通过Shell脚本调用1个Ascend AI处理器，设备ID为“0”。
-
-    ```Shell
-    # Linux with Shell
-    sh run_train.sh Ascend 1 0 ~/rank_table.json /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt
-    ```
-
-  - 使用样例3：通过Shell脚本调用8个Ascend AI处理器，设备ID为”0,1,2,3,4,5,6,7“。
-
-    ```Shell
-    # Linux with Shell
-    sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 ~/rank_table.json /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt
-    ```
-
-### 增量学习训练结果  
-
-- 查看运行结果。
-
-  - 运行Python文件时在交互式命令行中查看打印信息，`Linux`上运行Shell脚本运行后使用`cat ./train/device0/log0.log`中查看打印信息，输出结果如下：
-
-    ```Shell
-    train args: Namespace(dataset_path='.\\dataset\\train', platform='CPU', \
-    pretrain_ckpt='.\\pretrain_checkpoint\\mobilenetV2.ckpt', train_method='incremental_learn')
-    cfg: {'num_classes': 26, 'image_height': 224, 'image_width': 224, 'batch_size': 150, \
-    'epoch_size': 15, 'warmup_epochs': 0, 'lr_max': 0.03, 'lr_end': 0.03, 'momentum': 0.9, \
-    'weight_decay': 4e-05, 'label_smooth': 0.1, 'loss_scale': 1024, 'save_checkpoint': True, \
-    'save_checkpoint_epochs': 1, 'keep_checkpoint_max': 20, 'save_checkpoint_path': './checkpoint', \
-    'platform': 'CPU'}
-    Processing batch: 16: 100%|███████████████████████████████████████████ █████████████████████| 16/16 [00:00<?, ?it/s]
-    epoch[15], iter[16] cost: 256.030, per step time: 256.030, avg loss: 1.775total cos 7.2574 s
-    ```
-
-- 查看保存的checkpoint文件。
-
-  - Windows上使用`dir checkpoint`查看保存的模型文件：
-
-    ```Shell
-    dir checkpoint
-    2020//0814 11:20        267,727 mobilenetv2_head_1.ckpt
-    2020//0814 11:21        267,727 mobilenetv2_head_10.ckpt
-    2020//0814 11:21        267,727 mobilenetv2_head_11.ckpt
-    ...
-    2020//0814 11:21        267,727 mobilenetv2_head_7.ckpt
-    2020//0814 11:21        267,727 mobilenetv2_head_8.ckpt
-    2020//0814 11:21        267,727 mobilenetv2_head_9.ckpt
-    ```
-
-  - Linux上使用`ls ./checkpoint`查看保存的模型文件：
-
-    ```Shell
-    ls ./checkpoint/
-    mobilenetv2_head_1.ckpt  mobilenetv2_head_2.ckpt
-    mobilenetv2_head_3.ckpt  mobilenetv2_head_4.ckpt
-    ...
-    ```
-## 验证增量学习训练模型
-
-### 验证模型
-
-使用验证集测试模型性能，需要输入必要[参数](#参数简介)，`--platform`默认为“Ascend”，可自行设置为"CPU"或"GPU"。最终在交互式命令行中展示标准输出与错误输出，或者将其写入`infer.log`文件。
-
-```Shell
-# Windows/Linux with Python
-python eval.py --dataset_path \store\dataset\openimage\val\ --platform CPU pretrain_ckpt .\pretrain_checkpoint\mobilenetV2.ckpt --head_ckpt .\checkpoint\mobilenetv2_head_15,ckpt
-
-# Linux with Shell
-sh run_infer.sh CPU /store/dataset/openimage/val/ ../pretrain_checkpoint/mobilenetV2.ckpt ../checkpoint/mobilenetv2_head_15.ckpt
-```
-
-### 验证结果
-
-- 运行Python文件时在交互式命令行中输出验证结果，Shell脚本将把这些信息写入`./infer.log`中，需要使用`cat ./infer.log`查看，以Window运行结果为例，结果如下：
-
-  ```Shell
-  result:{'acc': 0.9466666666666666666667}
-  pretrain_ckpt = .\pretrain_checkpoint\mobilenetV2.ckpt
-  head_ckpt = .\checkpoint\mobilenetv2_head_15.ckpt
-  ```
+# MobileNetV2 增量学习
+`CPU` `Ascend` `GPU` `模型开发` `中级` `高级`
+
+<!-- TOC -->
+
+- [增量学习](#增量学习)
+  - [概述](#概述)
+  - [任务描述及准备](#任务描述及准备)
+    - [环境配置](#环境配置)
+    - [下载代码](#下载代码)
+    - [准备预训练模型](#准备预训练模型)
+    - [准备数据](#准备数据)
+  - [预训练模型加载代码详解](#预训练模型加载代码详解)
+  - [参数简介](#参数简介)
+    - [运行Python文件](#运行python文件)
+    - [运行Shell脚本](#运行shell脚本)
+  - [加载增量学习训练](#加载增量学习训练)
+    - [CPU加载训练](#cpu加载训练)
+    - [GPU加载训练](#gpu加载训练)
+    - [Ascend加载训练](#ascend加载训练)
+    - [增量学习训练结果](#增量学习训练结果)
+  - [验证增量学习训练模型](#验证增量学习训练模型)
+    - [验证模型](#验证模型)
+    - [验证结果](#验证结果)
+<!-- /TOC -->
+
+<a href="https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learn.md" target="_blank"><img src="../_static/logo_source.png"></a>&nbsp;&nbsp;
+
+## 概述
+
+计算机视觉任务中，从头开始训练一个网络耗时巨大，需要大量计算能力。预训练模型选择的常见的OpenImage、ImageNet、VOC、COCO等公开大型数据集，规模达到几十万甚至超过上百万张。大部分任务数据规模较大，训练网络模型时，如果不使用预训练模型，从头开始训练网络，需要消耗大量的时间与计算能力，模型容易陷入局部极小值和过拟合。因此大部分任务都会选择预训练模型，在其上做增量学习。  
+
+MindSpore是一个多元化的机器学习框架。既可以在手机等端侧和PC等设备上运行，也可以在云上的服务器集群上运行。目前MobileNetV2支持在Windows系统中使用单核CPU做增量学习，在EulerOS、Ubuntu系统中使用单个或者多个Ascend AI处理器或GPU中做增量学习，本教程将会介绍如何在不同系统与处理器下的MindSpore框架中做增量学习的训练与验证。
+
+目前，Window上暂只支持支持CPU，Ubuntu与EulerOS上支持CPU、GPU与Ascend AI处理器三种处理器。 
+
+>你可以在这里找到完整可运行的样例代码：https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/mobilenetv2
+
+## 任务描述及准备
+
+### 环境配置
+
+若在本地环境运行，需要安装MindSpore框架，配置CPU、GPU或Ascend AI处理器。若在华为云环境上运行，不需要安装MindSpore框架，不需要配置Ascend AI处理器、CPU与GPU，可以跳过本小节。
+
+1. 安装MindSpore框架
+    在EulerOS、Ubuntu或者Windows等系统上需要根据系统和处理器架构[安装对应版本MindSpore框架](https://www.mindspore.cn/install)。
+
+2. 配置CPU环境  
+    使用CPU时，在代码中，需要在调用CPU开始训练或测试前，按照如下代码设置：
+
+    ```Python
+    if config.platform == "CPU":
+        context.set_context(mode=context.GRAPH_MODE, device_target=config.platform, \
+            save_graphs=False)
+    ```
+
+3. 配置GPU环境  
+    使用GPU时，在代码中，需要在调用GPU开始训练或测试前，按照如下代码设置：
+
+    ```Python
+    elif config.platform == "GPU":
+        context.set_context(mode=context.GRAPH_MODE, device_target=config.platform, \
+            save_graphs=False)
+        init("nccl")
+        context.set_auto_parallel_context(device_num=get_group_size(),
+                                          parallel_mode=ParallelMode.DATA_PARALLEL,
+                                          mirror_mean=True)
+    ```
+
+4. 配置Ascend环境  
+    以Ascend 910 AI处理器为例，1个8个处理器环境的json配置文件`hccl_config.json`示例如下。单/多处理器环境可以根据以下示例调整`"server_count"`与`device`：
+
+    ```json
+    {
+        "version": "1.0",
+        "server_count": "1",
+        "server_list": [
+            {
+                "server_id": "10.155.111.140",
+                "device": [ba
+                    {"device_id": "0","device_ip": "192.1.27.6","rank_id": "0"},
+                    {"device_id": "1","device_ip": "192.2.27.6","rank_id": "1"},
+                    {"device_id": "2","device_ip": "192.3.27.6","rank_id": "2"},
+                    {"device_id": "3","device_ip": "192.4.27.6","rank_id": "3"},
+                    {"device_id": "4","device_ip": "192.1.27.7","rank_id": "4"},
+                    {"device_id": "5","device_ip": "192.2.27.7","rank_id": "5"},
+                    {"device_id": "6","device_ip": "192.3.27.7","rank_id": "6"},
+                    {"device_id": "7","device_ip": "192.4.27.7","rank_id": "7"}],
+                "host_nic_ip": "reserve"
+            }
+        ],
+        "status": "completed"
+    }
+    ```
+
+    使用Ascend AI处理器时，在代码中，需要在调用Ascend AI处理器开始训练或测试前，按照如下代码设置：
+
+    ```Python
+    elif config.platform == "Ascend":
+        context.set_context(mode=context.GRAPH_MODE, device_target=config.platform, \
+            device_id=config.device_id, save_graphs=False)
+        if config.run_distribute:
+            context.set_auto_parallel_context(device_num=config.rank_size,
+                                              parallel_mode=ParallelMode.DATA_PARALLEL,
+                                              parameter_broadcast=True, mirror_mean=True)
+            auto_parallel_context().set_all_reduce_fusion_split_indices([140])
+            init()
+    ...
+    ```
+
+### 下载代码  
+
+在Gitee中克隆[MindSpore开源项目仓库](https://gitee.com/mindspore/mindspore.git)，进入`./model_zoo/official/cv/mobilenetv2/`。  
+
+```bash
+git clone https://gitee.com/mindspore/mindspore/pulls/5766
+cd ./mindspore/model_zoo/official/cv/mobilenetv2
+```
+
+代码结构如下：
+
+```
+├─MobileNetV2
+    ├─README.md     # descriptions about MobileNetV2
+    ├─scripts
+    │   run_train.sh   # Shell script for train with Ascend or GPU
+    │   run_eval.sh    # Shell script for evaluation with Ascend or GPU
+    ├─src
+    │   config.py      # parameter configuration
+    │   dataset.py     # creating dataset
+    │   launch.py      # start Python script
+    │   lr_generator.py     # learning rate config
+    │   mobilenetV2.py      # MobileNetV2 architecture
+    │   models.py        # net utils to load ckpt_file, define_net...
+    │   utils.py        # net utils to switch precision, set_context and so on
+    ├─train.py      # training script
+    └─eval.py       #  evaluation script
+```
+
+运行增量学习训练与测试时，Windows、Ubuntu与EulersOS上可以使用Python文件`train.py`与`eval.py`，Ubuntu与EulerOS上还可以使用Shell脚本文件`run_train.sh`与`run_eval.sh`。
+
+使用脚本文件`run_train.sh`时，该文件会将运行`launch.py`并且将参数传入`launch.py`，`launch.py`根据分配的CPU、GPU或Ascend AI处理器数量，启动单个/多个进程运行`train.py`，每一个进程分配对应的一个处理器。
+
+### 准备预训练模型  
+
+[下载预训练模型](https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetV2.ckpt)到以下目录：  
+`./pretrain_checkpoint/[pretrain_checkpoint_file]`  
+
+```Python
+mkdir pretrain_checkpoint
+wget -P ./pretrain_checkpoint https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetV2.ckpt
+```
+
+### 准备数据
+
+准备ImageFolder格式管理的数据集，运行`run_train.sh`时加入`[dataset_path]`参数，运行`train.py`时加入`--dataset_path [dataset_path]`参数：
+
+数据集结构如下：
+
+```
+└─ImageFolder
+    ├─train  
+    │   class1Folder
+    │   class2Folder
+    │   ......
+    └─eval
+        class1Folder
+        class2Folder
+        ......
+```
+
+## 预训练模型加载代码详解
+
+在增量学习时，需要加载预训练模型。不同数据集和任务中特征提取层（卷积层）分布趋于一致，但是特征向量的组合（全连接层）不相同，分类数量（全连接层output_size)通常也不一致。在增量学习时，只加载与训练特征提取层参数，不加载与训练全连接层参数；在微调与初始训练时，加载与训练特征提取层参数与全连接层参数。
+
+在训练与测试之前，首先按照代码第1行，构建MobileNetV2的backbone网络，head网络，并且构建包含这两个子网络的MobileNetV2网络。代码第4-11行展示了如何在`fine_tune`训练模式下，将预训练模型加载入`net`(MobileNetV2)；在`incremental_learn`训练模式下，将预训练模型分别加载入backbone_net子网络，并且冻结backbone_net中的参数，不参与训练。代码第22-24行展示了如何冻结网络参数。
+
+```Python
+ 1:  backbone_net, head_net, net = define_net(args_opt, config)
+ 2:  ...
+ 3:  def define_net(args, config):
+ 4:      backbone_net = MobileNetV2Backbone(platform=args.platform)
+ 5:      head_net = MobileNetV2Head(input_channel=backbone_net.out_channels, num_classes=config.num_classes)
+ 6:      net = mobilenet_v2(backbone_net, head_net)  
+ 7:      if args.pretrain_ckpt:
+ 8:          if args.train_method == "fine_tune":
+ 9:              load_ckpt(net, args.pretrain_ckpt)
+10:          elif args.train_method == "incremental_learn":
+11:              load_ckpt(backbone_net, args.pretrain_ckpt, trainable=False)
+12:          elif args.train_method == "train":
+13:              pass
+14:          else:
+15:              raise ValueError("must input the usage of pretrain_ckpt when the pretrain_ckpt isn't None")
+16:      return backbone_net, head_net, net
+17:  ...
+18:  def load_ckpt(network, pretrain_ckpt_path, trainable=True):
+19:      """load the pretrain checkpoint and with the param trainable or not"""
+20:      param_dict = load_checkpoint(pretrain_ckpt_path)
+21:      load_param_into_net(network, param_dict)
+22:      if not trainable:
+23:          for param in network.get_parameters():
+24:              param.requires_grad = False
+```
+
+## 参数简介  
+
+### 运行Python文件
+在Windows与Linux系统上训练时，运行`train.py`时需要传入`dataset_path`、`platform`、`train_method`与`pretrain_ckpt`四个参数。验证时，运行`eval.py`并且传入`dataset_path`、`platform`、`pretrain_ckpt`与`head_ckpt`四个参数。
+
+```Shell
+# Windows/Linux train with Python file
+python train.py --dataset_path [dataset_path] --platform [platform] --pretrain_ckpt [pretrain_checkpoint_path] --train_method[("train", "fine_tune", "incremental_learn")]
+
+# Windows/Linux eval with Python file
+python eval.py --dataset_path [dataset_path] --platform [platform] --pretrain_ckpt [pretrain_checkpoint_path] --head_ckpt [head_ckpt_path]
+```
+
+- `--dataset_path`：训练与验证数据集地址，无默认值，用户训练/验证时必须输入。
+- `--platform`：处理器类型，默认为“Ascend”，可以设置为“CPU”或"GPU"。
+- `--train_method`：训练方法，必须输入“train"、"fine_tune"和incremental_learn"其中一个。
+- `--pretrain_ckpt`：增量训练或调优时，需要传入pretrain_checkpoint文件路径以加载预训练好的模型参数权重。
+- `--head_ckpt`：增量训练模型验证时，需要传入head_net预训练模型路径以加载预训练好的模型参数权重。
+
+
+### 运行Shell脚本
+在Linux系统上时，可以选择运行Shell脚本文件`./scripts/run_train.sh`与`./scripts/run_eval.sh`。运行时需要在交互界面中同时传入参数。  
+
+```Shell
+# Windows doesn't support Shell
+# Linux train with Shell script 
+sh run_train.sh [PLATFORM] [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [RANK_TABLE_FILE] [DATASET_PATH] [TRAIN_METHOD] [CKPT_PATH]
+
+# Linux eval with Shell script for incremental learn
+sh run_eval.sh [PLATFORM] [DATASET_PATH] [PRETRAIN_CKPT_PATH] [HEAD_CKPT_PATH]
+```
+
+- `[PLATFORM]`：处理器类型，默认为“Ascend”，可以设置为“GPU”。
+- `[DEVICE_NUM]`：每个节点（一台服务器/PC相当于一个节点）进程数量，建议设置为机器上Ascend AI处理器数量或GPU数量。
+- `[VISIABLE_DEVICES(0,1,2,3,4,5,6,7)]`：字符串格式的的设备ID，训练将会根据`[VISIABLE_DEVICES]`将进程绑定到对应ID的设备上，多个设备ID之间使用','分隔，建议ID数量与进程数量相同。
+- `[RANK_TABLE_FILE]`：platform选择Ascend时，需要配置Ascend的配置Json文件,。
+- `[DATASET_PATH]`：训练与验证数据集地址，无默认值，用户训练/验证时必须输入。
+- `[CKPT_PATH]`：增量训练或调优时，需要传入checkpoint文件路径以加载预训练好的模型参数权重。
+- `[TRAIN_METHOD]`：训练方法，必须输入`train`、`fine_tune`和`incremental_learn`其中一个。
+- `[PRETRAIN_CKPT_PATH]`：针对增量学习的模型做验证时，需要输入主干网络层保存模型路径。
+- `[HEAD_CKPT_PATH]`：针对增量学习的模型做验证时，需要输入全连接层保存模型路径。
+
+## 加载增量学习训练  
+
+Windows系统上，MobileNetV2做增量学习训练时，只能运行`train.py`。Linux系统上，使用MobileNetV2做增量学习训练时，可以选择运行`run_train.sh`， 并在运行Shell脚本文件时传入[参数](#参数简介)。
+
+Windows系统输出信息到交互式命令行，Linux系统环境下运行`run_train.sh`时，命令行结尾使用`&> [log_file_path]`将标准输出与错误输出写入log文件。 增量学习成功开始训练，`./train/device*/log*.log`中会持续写入每一个epoch的训练时间与Loss等信息。若未成功，上述log文件会写入失败报错信息。
+
+### CPU加载训练  
+
+- 设置节点数量
+
+  目前运行`train.py`时仅支持单处理器，不需要调整处理器数量。运行`run_train.sh`文件时，`CPU`设备默认为单处理器，目前暂不支持修改CPU数量。
+
+- 开始增量训练
+
+  使用样例1：通过Python文件调用1个CPU处理器。
+
+    ```Shell
+    # Windows or Linux with Python
+    python train.py --platform CPU --dataset_path /store/dataset/OpenImage/train/ -- train_method incremental_learn --pretrain_ckpt ./pretrain_checkpoint/mobilenetV2.ckpt
+    ```
+
+  使用样例2：通过Shell文件调用1个CPU处理器。
+
+    ```Shell
+    # Linux with Shell
+    sh run_train.sh CPU /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt
+    ```
+
+### GPU加载训练  
+
+- 设置节点数量
+
+  目前运行`train.py`时仅支持单处理器，不需要调整节点数量。运行`run_train.sh`文件时，设置`[nproc_per_node]`为GPU数量， `[visible_devices]`为可使用的处理器编号，即GPU的ID，可以选择一个或多个设备ID，使用`,`隔开。
+
+- 开始增量训练
+
+  - 使用样例1：通过Python文件调用1个GPU处理器。
+
+    ```Shell
+    # Windows or Linux with Python
+    python train.py --platform GPU --dataset_path /store/dataset/OpenImage/train/ --pretrain_ckpt ./pretrain_checkpoint/mobilenetV2.ckpt --train_method incremental_learn
+    ```
+
+  - 使用样例2：通过Shell脚本调用1个GPU处理器，设备ID为`“0”`。
+
+    ```Shell
+    # Linux with Shell
+    sh run_train.sh GPU 1 0 /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt
+    ```
+
+  - 使用样例3：通过Shell脚本调用8个GPU处理器，设备ID为`“0,1,2,3,4,5,6,7”`。
+
+    ```Shell
+    # Linux with Shell
+    sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt
+    ```
+
+### Ascend加载训练  
+
+- 设置节点数量
+
+  目前运行`train.py`时仅支持单处理器，不需要调整节点数量。运行`run_train.sh`文件时，设置`[nproc_per_node]`为Ascend AI处理器数量， `[visible_devices]`为可使用的处理器编号，即Ascend AI处理器的ID，8卡服务器可以选择0-7中一个或多个设备ID，使用`,`隔开。Ascend节点处理器数量目前只能设置为1或者8。
+
+- 开始增量训练
+
+  - 使用样例1：通过Python文件调用1个Ascend处理器。
+
+    ```Shell
+    # Windows or Linux with Python
+    python train.py --platform Ascend --dataset_path /store/dataset/OpenImage/train/ --train_method incremental_learn --pretrain_ckpt  ./pretrain_checkpoint/mobilenetV2.ckpt
+    ```
+
+  - 使用样例2：通过Shell脚本调用1个Ascend AI处理器，设备ID为“0”。
+
+    ```Shell
+    # Linux with Shell
+    sh run_train.sh Ascend 1 0 ~/rank_table.json /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt
+    ```
+
+  - 使用样例3：通过Shell脚本调用8个Ascend AI处理器，设备ID为”0,1,2,3,4,5,6,7“。
+
+    ```Shell
+    # Linux with Shell
+    sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 ~/rank_table.json /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt
+    ```
+
+### 增量学习训练结果  
+
+- 查看运行结果。
+
+  - 运行Python文件时在交互式命令行中查看打印信息，`Linux`上运行Shell脚本运行后使用`cat ./train/device0/log0.log`中查看打印信息，输出结果如下：
+
+    ```Shell
+    train args: Namespace(dataset_path='.\\dataset\\train', platform='CPU', \
+    pretrain_ckpt='.\\pretrain_checkpoint\\mobilenetV2.ckpt', train_method='incremental_learn')
+    cfg: {'num_classes': 26, 'image_height': 224, 'image_width': 224, 'batch_size': 150, \
+    'epoch_size': 15, 'warmup_epochs': 0, 'lr_max': 0.03, 'lr_end': 0.03, 'momentum': 0.9, \
+    'weight_decay': 4e-05, 'label_smooth': 0.1, 'loss_scale': 1024, 'save_checkpoint': True, \
+    'save_checkpoint_epochs': 1, 'keep_checkpoint_max': 20, 'save_checkpoint_path': './checkpoint', \
+    'platform': 'CPU'}
+    Processing batch: 16: 100%|███████████████████████████████████████████ █████████████████████| 16/16 [00:00<?, ?it/s]
+    epoch[15], iter[16] cost: 256.030, per step time: 256.030, avg loss: 1.775total cos 7.2574 s
+    ```
+
+- 查看保存的checkpoint文件。
+
+  - Windows上使用`dir checkpoint`查看保存的模型文件：
+
+    ```Shell
+    dir checkpoint
+    2020//0814 11:20        267,727 mobilenetv2_head_1.ckpt
+    2020//0814 11:21        267,727 mobilenetv2_head_10.ckpt
+    2020//0814 11:21        267,727 mobilenetv2_head_11.ckpt
+    ...
+    2020//0814 11:21        267,727 mobilenetv2_head_7.ckpt
+    2020//0814 11:21        267,727 mobilenetv2_head_8.ckpt
+    2020//0814 11:21        267,727 mobilenetv2_head_9.ckpt
+    ```
+
+  - Linux上使用`ls ./checkpoint`查看保存的模型文件：
+
+    ```Shell
+    ls ./checkpoint/
+    mobilenetv2_head_1.ckpt  mobilenetv2_head_2.ckpt
+    mobilenetv2_head_3.ckpt  mobilenetv2_head_4.ckpt
+    ...
+    ```
+## 验证增量学习训练模型
+
+### 验证模型
+
+使用验证集测试模型性能，需要输入必要[参数](#参数简介)，`--platform`默认为“Ascend”，可自行设置为"CPU"或"GPU"。最终在交互式命令行中展示标准输出与错误输出，或者将其写入`infer.log`文件。
+
+```Shell
+# Windows/Linux with Python
+python eval.py --dataset_path \store\dataset\openimage\val\ --platform CPU pretrain_ckpt .\pretrain_checkpoint\mobilenetV2.ckpt --head_ckpt .\checkpoint\mobilenetv2_head_15,ckpt
+
+# Linux with Shell
+sh run_infer.sh CPU /store/dataset/openimage/val/ ../pretrain_checkpoint/mobilenetV2.ckpt ../checkpoint/mobilenetv2_head_15.ckpt
+```
+
+### 验证结果
+
+- 运行Python文件时在交互式命令行中输出验证结果，Shell脚本将把这些信息写入`./infer.log`中，需要使用`cat ./infer.log`查看，以Window运行结果为例，结果如下：
+
+  ```Shell
+  result:{'acc': 0.9466666666666666666667}
+  pretrain_ckpt = .\pretrain_checkpoint\mobilenetV2.ckpt
+  head_ckpt = .\checkpoint\mobilenetv2_head_15.ckpt
+  ```

tutorials/source_zh_cn/index.rst

@@ -36,6 +36,7 @@ MindSpore教程
    advanced_use/synchronization_training_and_evaluation
    advanced_use/bert_poetry
    advanced_use/optimize_the_performance_of_data_preparation
+   advanced_use/mobilenetv2_incremental_learning
 
 .. toctree::
    :glob: