onert-micro.h File Reference

Go to the source code of this file.

Data Structures
struct	nnfw_tensorinfo
	tensor info describes the type and shape of tensors More...
struct	nnfw_loss_info
struct	nnfw_adam_option
struct	nnfw_train_info
	Training information to prepare training. More...

Macros
#define	NNFW_MAX_RANK   (6)
	Maximum rank expressible with nnfw.
#define	NNFW_TRAINABLE_OPS_MAX_SIZE   (256)
	Maximum numer of trainable operations.

Typedefs
typedef struct nnfw_session	nnfw_session
typedef struct nnfw_tensorinfo	nnfw_tensorinfo
	tensor info describes the type and shape of tensors
typedef struct nnfw_loss_info	nnfw_loss_info
typedef struct nnfw_adam_option	nnfw_adam_option
typedef struct nnfw_train_info	nnfw_train_info
	Training information to prepare training.

Enumerations
enum	NNFW_TYPE { NNFW_TYPE_TENSOR_FLOAT32 = 0 , NNFW_TYPE_TENSOR_INT32 = 1 }
enum	NNFW_STATUS {   NNFW_STATUS_NO_ERROR = 0 , NNFW_STATUS_ERROR = 1 , NNFW_STATUS_UNEXPECTED_NULL = 2 , NNFW_STATUS_INVALID_STATE = 3 ,   NNFW_STATUS_OUT_OF_MEMORY = 4 , NNFW_STATUS_INSUFFICIENT_OUTPUT_SIZE = 5 , NNFW_STATUS_DEPRECATED_API = 6 }
	Result values returned from a call to an API function. More...
enum	NNFW_TRAIN_LOSS { NNFW_TRAIN_LOSS_UNDEFINED = 0 , NNFW_TRAIN_LOSS_MEAN_SQUARED_ERROR = 1 , NNFW_TRAIN_LOSS_CATEGORICAL_CROSSENTROPY = 2 }
enum	NNFW_TRAIN_LOSS_REDUCTION { NNFW_TRAIN_LOSS_REDUCTION_UNDEFINED = 0 , NNFW_TRAIN_LOSS_REDUCTION_SUM_OVER_BATCH_SIZE = 1 , NNFW_TRAIN_LOSS_REDUCTION_SUM = 2 }
enum	NNFW_TRAIN_OPTIMIZER { NNFW_TRAIN_OPTIMIZER_UNDEFINED = 0 , NNFW_TRAIN_OPTIMIZER_SGD = 1 , NNFW_TRAIN_OPTIMIZER_ADAM = 2 }

Functions
NNFW_STATUS	nnfw_train_set_traininfo (nnfw_session *session, const nnfw_train_info *info)
	Set training information.
NNFW_STATUS	nnfw_create_session (nnfw_session **session)
	Create a new session instance.
NNFW_STATUS	nnfw_close_session (nnfw_session *session)
	Close a session instance.
NNFW_STATUS	nnfw_load_model_from_file (nnfw_session *session, const char *package_file_path)
	Load model from nnpackage file or directory.
NNFW_STATUS	nnfw_train_prepare (nnfw_session *session)
	Prepare session to be ready for training.
NNFW_STATUS	nnfw_train (nnfw_session *session, bool update_weights)
	Train the model.
NNFW_STATUS	nnfw_train_export_circle (nnfw_session *session, const char *path)
	Export current training model into circle model.
NNFW_STATUS	nnfw_train_export_checkpoint (nnfw_session *session, const char *path)
NNFW_STATUS	nnfw_train_import_checkpoint (nnfw_session *session, const char *path)
NNFW_STATUS	nnfw_train_set_input (nnfw_session *session, uint32_t index, void *input, const nnfw_tensorinfo *input_info)
	Set training input.
NNFW_STATUS	nnfw_train_set_expected (nnfw_session *session, uint32_t index, void *expected, const nnfw_tensorinfo *expected_info)
	Set training expected output.
NNFW_STATUS	nnfw_train_get_loss (nnfw_session *session, uint32_t index, float *loss)
	Get loss value for expected output.
NNFW_STATUS	nnfw_train_set_output (nnfw_session *session, uint32_t index, NNFW_TYPE type, void *buffer, size_t length)
	Set training output buffer.

Macro Definition Documentation

NNFW_MAX_RANK

#define NNFW_MAX_RANK   (6)

Maximum rank expressible with nnfw.

Definition at line 109 of file onert-micro.h.

NNFW_TRAINABLE_OPS_MAX_SIZE

#define NNFW_TRAINABLE_OPS_MAX_SIZE   (256)

Maximum numer of trainable operations.

Definition at line 179 of file onert-micro.h.

Typedef Documentation

nnfw_adam_option

typedef struct nnfw_adam_option nnfw_adam_option

nnfw_loss_info

typedef struct nnfw_loss_info nnfw_loss_info

nnfw_session

typedef struct nnfw_session nnfw_session

Definition at line 72 of file onert-micro.h.

nnfw_tensorinfo

typedef struct nnfw_tensorinfo nnfw_tensorinfo

tensor info describes the type and shape of tensors

This structure is used to describe input and output tensors. Application can get input and output tensor type and shape described in model by using nnfw_input_tensorinfo and nnfw_output_tensorinfo

Maximum rank is 6 (NNFW_MAX_RANK). And tensor's dimension value is filled in 'dims' field from index 0. For example, if tensor's rank is 4, application can get dimension value from dims[0], dims[1], dims[2], and dims[3]

nnfw_train_info

typedef struct nnfw_train_info nnfw_train_info

Training information to prepare training.

Enumeration Type Documentation

NNFW_STATUS

enum NNFW_STATUS

Result values returned from a call to an API function.

Enumerator
NNFW_STATUS_NO_ERROR	Successful
NNFW_STATUS_ERROR	An error code for general use. Mostly used when there is no specific value for that certain situation.
NNFW_STATUS_UNEXPECTED_NULL	Unexpected null argument is given.
NNFW_STATUS_INVALID_STATE	When a function was called but it is not valid for the current session state.
NNFW_STATUS_OUT_OF_MEMORY	When it is out of memory
NNFW_STATUS_INSUFFICIENT_OUTPUT_SIZE	When it was given an insufficient output buffer
NNFW_STATUS_DEPRECATED_API	When API is deprecated

Definition at line 85 of file onert-micro.h.

{
  NNFW_STATUS_NO_ERROR = 0,
  NNFW_STATUS_ERROR = 1,
  NNFW_STATUS_UNEXPECTED_NULL = 2,
  NNFW_STATUS_INVALID_STATE = 3,
  NNFW_STATUS_OUT_OF_MEMORY = 4,
  NNFW_STATUS_INSUFFICIENT_OUTPUT_SIZE = 5,
  NNFW_STATUS_DEPRECATED_API = 6,
} NNFW_STATUS;

NNFW_TRAIN_LOSS

enum NNFW_TRAIN_LOSS

Enumerator
NNFW_TRAIN_LOSS_UNDEFINED
NNFW_TRAIN_LOSS_MEAN_SQUARED_ERROR
NNFW_TRAIN_LOSS_CATEGORICAL_CROSSENTROPY

Definition at line 139 of file onert-micro.h.

{
  NNFW_TRAIN_LOSS_UNDEFINED = 0,
  NNFW_TRAIN_LOSS_MEAN_SQUARED_ERROR = 1,
  NNFW_TRAIN_LOSS_CATEGORICAL_CROSSENTROPY = 2,
} NNFW_TRAIN_LOSS;

NNFW_TRAIN_LOSS_REDUCTION

enum NNFW_TRAIN_LOSS_REDUCTION

Enumerator
NNFW_TRAIN_LOSS_REDUCTION_UNDEFINED	Undefined
NNFW_TRAIN_LOSS_REDUCTION_SUM_OVER_BATCH_SIZE	Scalar sum divided by number of elements in losses
NNFW_TRAIN_LOSS_REDUCTION_SUM	Scalar sum of weighted losses

Definition at line 146 of file onert-micro.h.

{
  NNFW_TRAIN_LOSS_REDUCTION_UNDEFINED = 0,
  NNFW_TRAIN_LOSS_REDUCTION_SUM_OVER_BATCH_SIZE = 1,
  NNFW_TRAIN_LOSS_REDUCTION_SUM = 2,
} NNFW_TRAIN_LOSS_REDUCTION;

NNFW_TRAIN_OPTIMIZER

enum NNFW_TRAIN_OPTIMIZER

Enumerator
NNFW_TRAIN_OPTIMIZER_UNDEFINED
NNFW_TRAIN_OPTIMIZER_SGD
NNFW_TRAIN_OPTIMIZER_ADAM

Definition at line 156 of file onert-micro.h.

{
  NNFW_TRAIN_OPTIMIZER_UNDEFINED = 0,
  NNFW_TRAIN_OPTIMIZER_SGD = 1,
  NNFW_TRAIN_OPTIMIZER_ADAM = 2,
} NNFW_TRAIN_OPTIMIZER;

NNFW_TYPE

enum NNFW_TYPE

Enumerator
NNFW_TYPE_TENSOR_FLOAT32	A tensor of 32 bit floating point
NNFW_TYPE_TENSOR_INT32	A tensor of 32 bit signed integer

Definition at line 74 of file onert-micro.h.

{
  NNFW_TYPE_TENSOR_FLOAT32 = 0,
  NNFW_TYPE_TENSOR_INT32 = 1,
} NNFW_TYPE;

Function Documentation

nnfw_close_session()

NNFW_STATUS nnfw_close_session

(

nnfw_session *

session

)

Close a session instance.

After called, access to closed session by application will be invalid

Parameters

[in]

session

The session to be closed

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 60 of file nnfw_api.cc.

{
  delete session;
  return NNFW_STATUS_NO_ERROR;
}

References NNFW_STATUS_NO_ERROR.

Referenced by NNFW_SESSION::close_session(), and main().

nnfw_create_session()

NNFW_STATUS nnfw_create_session

(

nnfw_session **

session

)

Create a new session instance.

This only creates a session. Model is loaded after nnfw_load_model_from_file is invoked. And inference is performed after nnfw_run is invoked.

nnfw_close_session should be called once if session is no longer needed

Parameters

[out]

session

The session to be created

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 390 of file onert-micro.cpp.

{ return nnfw_session::create(session); }

References nnfw_session::create().

Referenced by main(), and NNFW_SESSION::NNFW_SESSION().

nnfw_load_model_from_file()

NNFW_STATUS nnfw_load_model_from_file	(	nnfw_session *	session,
		const char *	package_file_path
	)

Load model from nnpackage file or directory.

The length of package_file_path must not exceed 1024 bytes including zero at the end.

Parameters

[in]	session	nnfw_session loading the given nnpackage file/dir
[in]	package_file_path	Path to the nnpackage file or unzipped directory to be loaded

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 392 of file onert-micro.cpp.

{
  return session->load_model_from_file(package_file_path);
}

Referenced by main(), and NNFW_SESSION::NNFW_SESSION().

nnfw_train()

NNFW_STATUS nnfw_train	(	nnfw_session *	session,
		bool	update_weights
	)

Train the model.

Note

This function should be called after nnfw_train_set_input and nnfw_train_set_expected for each input and expected output

In order to use update_weights as false, it should be called after nnfw_train_set_output.

Parameters

[in]	session	The session to be trained
[in]	update_weights	If true, update weights of the model If false, do not update weights of the model (for validation)

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 399 of file onert-micro.cpp.

{
  return session->train_run(update_weights);
}

nnfw_train_export_checkpoint()

NNFW_STATUS nnfw_train_export_checkpoint	(	nnfw_session *	session,
		const char *	path
	)

Definition at line 409 of file onert-micro.cpp.

{
  return session->train_export_checkpoint(path);
}

nnfw_train_export_circle()

NNFW_STATUS nnfw_train_export_circle	(	nnfw_session *	session,
		const char *	path
	)

Export current training model into circle model.

Note

This function should be called on training mode This function should be called after nnfw_train

Parameters

[in]	session	The session to export inference model
[in]	path	The path to export inference model

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 404 of file onert-micro.cpp.

{
  return session->train_export_circle(path);
}

nnfw_train_get_loss()

NNFW_STATUS nnfw_train_get_loss	(	nnfw_session *	session,
		uint32_t	index,
		float *	loss
	)

Get loss value for expected output.

Note

This function should be called after nnfw_train

Parameters

[in]	session	The session to get loss value
[in]	index	The index of loss value [0, number of expected outputs)
[out]	loss	The loss value

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 433 of file onert-micro.cpp.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->train_get_loss(index, loss);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_train_import_checkpoint()

NNFW_STATUS nnfw_train_import_checkpoint	(	nnfw_session *	session,
		const char *	path
	)

Definition at line 414 of file onert-micro.cpp.

{
  return session->train_import_checkpoint(path);
}

nnfw_train_prepare()

NNFW_STATUS nnfw_train_prepare

(

nnfw_session *

session

)

Prepare session to be ready for training.

Note

The session will be entered into training mode

   If training info is NOT set in session, this function returns @c NNFW_STATUS_ERROR .
   You should set training info using {@link nnfw_train_set_traininfo}.

Parameters

[in]

session

The session to be prepared for training

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 397 of file onert-micro.cpp.

{ return session->train_prepare(); }

nnfw_train_set_expected()

NNFW_STATUS nnfw_train_set_expected	(	nnfw_session *	session,
		uint32_t	index,
		void *	expected,
		const nnfw_tensorinfo *	expected_info
	)

Set training expected output.

Note

This function should be called after nnfw_train_prepare

Parameters

session	The session to be set training inputs and expected model outputs
index	The index of training expected output
expected	The expected buffers for training
expected_info	The shape and type of expected buffer If it is nullptr, it will not change shape and batch size

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 426 of file onert-micro.cpp.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->train_set_expected(index, expected);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_train_set_input()

NNFW_STATUS nnfw_train_set_input	(	nnfw_session *	session,
		uint32_t	index,
		void *	input,
		const nnfw_tensorinfo *	input_info
	)

Set training input.

Note

This function should be called after nnfw_train_prepare

Parameters

[in]	session	The session to be set training inputs and expected model outputs
[in]	index	The index of training input
[in]	input	The input buffers for training
[in]	input_info	The shape and type of input buffer If it is nullptr, it will not change shape and batch size

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 419 of file onert-micro.cpp.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->train_set_input(index, input);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_train_set_output()

NNFW_STATUS nnfw_train_set_output	(	nnfw_session *	session,
		uint32_t	index,
		NNFW_TYPE	type,
		void *	buffer,
		size_t	length
	)

Set training output buffer.

This function must be called after nnfw_train_prepare, buffer given to this function can be reused for training. length must be greater or equal than the operand requires. An output operand can have unspecified shape and deduced dynamically during the execution. You must provide buffer large enough.

Parameters

[in]	session	Session from inference output is to be extracted
[in]	index	Index of output to be set (0-indexed)
[in]	type	Type of the output
[out]	buffer	Raw buffer for output
[in]	length	Size of bytes of output buffer

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 445 of file onert-micro.cpp.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->train_set_output(index, type, buffer, length);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_train_set_traininfo()

NNFW_STATUS nnfw_train_set_traininfo	(	nnfw_session *	session,
		const nnfw_train_info *	info
	)

Set training information.

Note

This function should be called after calling nnfw_load_model_from_file and before calling nnfw_train_prepare

Parameters

[in]	session	The session to be set training information
[in]	info	The training information

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 439 of file onert-micro.cpp.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->train_set_traininfo(info);
}

References info, and NNFW_RETURN_ERROR_IF_NULL.