mindspore::session

LiteSession

LiteSession defines sessions in MindSpore Lite for compiling Model and forwarding inference.

Constructors & Destructors

LiteSession

LiteSession()

Constructor of MindSpore Lite LiteSession using default value for parameters.

~LiteSession

~LiteSession()

Destructor of MindSpore Lite LiteSession.

Public Member Functions

BindThread

virtual void BindThread(bool if_bind)

Attempts to bind threads in the thread pool to the specified CPU core or unbind threads from the core.

Parameters
- if_bind: Define whether to bind or unbind threads.

CompileGraph

virtual int CompileGraph(lite::Model *model)

Compile MindSpore Lite model.

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.

GetInputs

virtual std::vector <tensor::MSTensor *> GetInputs() const

Get input MindSpore Lite MSTensors of model.

Returns

The vector of MindSpore Lite MSTensor.

GetInputsByTensorName

mindspore::tensor::MSTensor *GetInputsByTensorName(const std::string &name) const

Get input MindSpore Lite MSTensors of model by tensor name.

Parameters
- name: Define tensor name.
Returns

MindSpore Lite MSTensor.

RunGraph

virtual int RunGraph(const KernelCallBack &before = nullptr, const KernelCallBack &after = nullptr)

Run session with callback.

RunGraph should be called after CompileGraph.

Parameters
- before: A KernelCallBack function. Define a callback function to be called before running each node.
- after: A KernelCallBack function. Define a callback function to be called after running each node.
Returns

STATUS as an error code of running graph, STATUS is defined in errorcode.h.

GetOutputsByNodeName

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.

GetOutputs

virtual std::unordered_map <std::string, mindspore::tensor::MSTensor *> GetOutputs() const

Get the MSTensors output of the MindSpore Lite model mapped by tensor name.

Returns

The map of output tensor name and MindSpore Lite MSTensor.

GetOutputTensorNames

virtual std::vector <std::string> GetOutputTensorNames() const

Get name of output tensors of model compiled by this session.

Returns

A string variable, contains the output tensors’ names in order.

GetOutputByTensorName

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.

Resize

virtual int Resize(const std::vector <tensor::MSTensor *> &inputs, const std::vector<std::vector<int>> &dims)

Resize inputs shape.

Parameters
- inputs: Model inputs.
- dims: defines the new inputs shape. Its order should be consistent with inputs.
Returns

STATUS as an error code of resize inputs, STATUS is defined in errorcode.h.

Static Public Member Functions

CreateSession

static LiteSession *CreateSession(const lite::Context *context)

Static method to create a LiteSession pointer.

Parameters
- context: Define the context of session to be created.
Returns

Pointer that points to MindSpore Lite MSTensor.

static LiteSession *CreateSession(const char *model_buf, size_t size, const lite::Context *context);

Static method to create a LiteSession pointer. The returned LiteSession pointer has already read model_buf and completed graph compilation.

Parameters
- model_buf: Define the buffer read from a model file.
- size: variable. Define the byte number of model buffer.
- context: Define the context of session to be created.
Returns

Pointer that points to MindSpore Lite LiteSession.

TrainSession

#include <lite_session.h>

Inherited from LiteSession, TrainSession defines the class that allows training the MindSpore model.

Constructors & Destructors

~TrainSession

virtual ~TrainSession() = default;

Static method to create a TrainSession object.

Public Member Functions

CreateSession

static TrainSession *CreateSession(const char *model_buf, size_t size, lite::Context *context, bool train_mode = false);

Static method to create a TrainSession object.

Parameters
- model_buf: A buffer that was read from a MS model file.
- size: Length of the buffer.
- context: Defines the context of the session to be created.
- train_mode: Training mode to initialize Session with.
Returns

Pointer that points to MindSpore Lite TrainSession.

static TrainSession *CreateSession(const std::string &filename, lite::Context *context, bool train_mode = false);

Static method to create a TrainSession object.

Parameters
- filename: Filename to read flatbuffer from.
- context: Defines the context of the session to be created.
- train_mode: Training mode to initialize Session with.
Returns

Pointer that points to MindSpore Lite TrainSession.

ExportToBuf

virtual void *ExportToBuf(char *buf, size_t *len) const = 0;

Export the trained model into a buffer.

Parameters
- buf: The buffer to be exported into. If equal to nullptr, buf will be allocated.
- len: Size of the pre-allocated buffer, and the returned size of the exported buffer.
Returns

Pointer that points to MindSpore Lite TrainSession.

SaveToFile

virtual int SaveToFile(const std::string &filename) const = 0;

Save the trained model into a flatbuffer file.

Parameters
- filename: Name of the file to save flatbuffer.
Returns

0 represents success or -1 in case of error.

Train

virtual int Train() = 0;

Set model to train mode.

Returns

STATUS as an error code of compiling graph, STATUS is defined in errorcode.h

IsTrain

bool IsTrain() { return train_mode_ == true; }

Checks whether the current model is under the train mode.

Returns

Boolean indication if model is in train mode.

Eval

virtual int Eval() = 0;

Set model to eval mode.

Returns

STATUS as an error code of compiling graph, STATUS is defined in errorcode.h.

IsEval

bool IsEval() { return train_mode_ == false; }

Check mode of model.

Returns

boolean indication if model is in eval mode.