nnfw_experimental.h File Reference

#include "nnfw.h"

Go to the source code of this file.

Data Structures
struct	nnfw_operand
struct	nnfw_custom_kernel_params
struct	custom_kernel_registration_info
struct	nnfw_loss_info
struct	nnfw_train_info
	Training information to prepare training. More...

Typedefs
typedef void(*	nnfw_custom_eval) (nnfw_custom_kernel_params *params, char *userdata, size_t userdata_size)
typedef struct nnfw_loss_info	nnfw_loss_info
typedef struct nnfw_train_info	nnfw_train_info
	Training information to prepare training.

Enumerations
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 }
enum	NNFW_TRAIN_NUM_OF_TRAINABLE_OPS_SPECIAL_VALUES { NNFW_TRAIN_TRAINABLE_INCORRECT_STATE = -2 , NNFW_TRAIN_TRAINABLE_ALL = -1 , NNFW_TRAIN_TRAINABLE_NONE = 0 }
	Special values of num_of_trainable_ops. Positive values are used to indicate layers to be trained from the back of the graph. More...
enum	NNFW_QUANTIZE_TYPE {   NNFW_QUANTIZE_TYPE_NOT_SET , NNFW_QUANTIZE_TYPE_U8_ASYM , NNFW_QUANTIZE_TYPE_I16_SYM , NNFW_QUANTIZE_TYPE_WO_I8_SYM ,   NNFW_QUANTIZE_TYPE_WO_I16_SYM }
	Convert between training mode and inference mode. More...
enum	NNFW_CODEGEN_PREF { NNFW_CODEGEN_PREF_DEFAULT , NNFW_CODEGEN_PREF_PERFORMANCE_FIRST , NNFW_CODEGEN_PREF_MEMORY_FIRST , NNFW_CODEGEN_PREF_COMPILE_TIME_FIRST }
	Preference for target-dependent code generation. More...
enum	NNFW_PREPARE_CONFIG { NNFW_PREPARE_CONFIG_PROFILE }
	Configuration key for prepare (compile and schedule) More...
enum	NNFW_RUN_CONFIG { NNFW_RUN_CONFIG_DUMP_MINMAX , NNFW_RUN_CONFIG_TRACE , NNFW_RUN_CONFIG_PROFILE }
	Configuration key for execution. More...

Functions
NNFW_STATUS	nnfw_register_custom_op_info (nnfw_session *session, const char *id, custom_kernel_registration_info *info)
NNFW_STATUS	nnfw_input_tensorindex (nnfw_session *session, const char *tensorname, uint32_t *index)
	Get the input tensor index by name.
NNFW_STATUS	nnfw_output_tensorindex (nnfw_session *session, const char *tensorname, uint32_t *index)
	Get the input tensor index by name.
NNFW_STATUS	nnfw_set_backends_per_operation (nnfw_session *session, const char *backend_settings)
	Set the backend for each operation in the session.
NNFW_STATUS	nnfw_prepare_pipeline (nnfw_session *session, const char *map_file_path=nullptr)
	Prepare session to be ready for inference.
NNFW_STATUS	nnfw_push_pipeline_input (nnfw_session *session, void *inputs, void *lengths)
	Set input buffer.
NNFW_STATUS	nnfw_pop_pipeline_output (nnfw_session *session, void *outputs)
	Get last outputs of partitioned model in session.
NNFW_STATUS	nnfw_train_get_traininfo (nnfw_session *session, nnfw_train_info *info)
	Get training information.
NNFW_STATUS	nnfw_train_set_traininfo (nnfw_session *session, const nnfw_train_info *info)
	Set training information.
NNFW_STATUS	nnfw_train_prepare (nnfw_session *session)
	Prepare session to be ready for training.
NNFW_STATUS	nnfw_train_set_input (nnfw_session *session, uint32_t index, const void *input, const nnfw_tensorinfo *input_info)
	Set training input.
NNFW_STATUS	nnfw_train_set_expected (nnfw_session *session, uint32_t index, const void *expected, const nnfw_tensorinfo *expected_info)
	Set training 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.
NNFW_STATUS	nnfw_train (nnfw_session *session, bool update_weights)
	Train the model.
NNFW_STATUS	nnfw_train_get_loss (nnfw_session *session, uint32_t index, float *loss)
	Get loss value for expected output.
NNFW_STATUS	nnfw_train_export_circle (nnfw_session *session, const char *path)
	Export circle model.
NNFW_STATUS	nnfw_train_import_checkpoint (nnfw_session *session, const char *path)
	Import circle checkpoint.
NNFW_STATUS	nnfw_train_export_checkpoint (nnfw_session *session, const char *path)
	Export circle checkpoint.
NNFW_STATUS	nnfw_train_input_tensorinfo (nnfw_session *session, uint32_t index, nnfw_tensorinfo *info)
	Get the training model input information.
NNFW_STATUS	nnfw_train_expected_tensorinfo (nnfw_session *session, uint32_t index, nnfw_tensorinfo *info)
	Get the training model expected output information.
NNFW_STATUS	nnfw_set_quantization_type (nnfw_session *session, NNFW_QUANTIZE_TYPE qtype)
	Set quantization type.
NNFW_STATUS	nnfw_set_quantized_model_path (nnfw_session *session, const char *path)
	Set exported quantized model path.
NNFW_STATUS	nnfw_quantize (nnfw_session *session)
	Quantize circle model.
NNFW_STATUS	nnfw_set_codegen_model_path (nnfw_session *session, const char *path)
	Set exported codegen model path.
NNFW_STATUS	nnfw_codegen (nnfw_session *session, const char *target, NNFW_CODEGEN_PREF pref)
	Generate target-dependent code.
NNFW_STATUS	nnfw_set_odc_param_minmax_records_count (nnfw_session *session, int minmax_records_count)
	Set MinMax records count in auto compilation mode with on-device compiler.
NNFW_STATUS	nnfw_odc_delete_minmax_file (nnfw_session *session)
	Delete MinMax file for on-device compiler.
NNFW_STATUS	nnfw_run_with_auto_compilation (nnfw_session *session, const char *target, NNFW_CODEGEN_PREF pref)
	Run inference with auto compilation.
NNFW_STATUS	nnfw_set_prepare_config (nnfw_session *session, NNFW_PREPARE_CONFIG key, const char *value)
	Set prepare configuration.
NNFW_STATUS	nnfw_reset_prepare_config (nnfw_session *session)
	Reset prepare configurations.
NNFW_STATUS	nnfw_set_execute_config (nnfw_session *session, const NNFW_RUN_CONFIG key, const char *value)
	Set execution (run or train) configuration.
NNFW_STATUS	nnfw_reset_execute_config (nnfw_session *session)
	Reset execution (run or train) configurations.

Typedef Documentation

nnfw_custom_eval

typedef void(* nnfw_custom_eval) (nnfw_custom_kernel_params *params, char *userdata, size_t userdata_size)

Definition at line 55 of file nnfw_experimental.h.

nnfw_loss_info

typedef struct nnfw_loss_info nnfw_loss_info

nnfw_train_info

typedef struct nnfw_train_info nnfw_train_info

Training information to prepare training.

Enumeration Type Documentation

NNFW_CODEGEN_PREF

enum NNFW_CODEGEN_PREF

Preference for target-dependent code generation.

Enumerator
NNFW_CODEGEN_PREF_DEFAULT	Use the default configuration
NNFW_CODEGEN_PREF_PERFORMANCE_FIRST	Do best efforts to generate target-dependent code for performance
NNFW_CODEGEN_PREF_MEMORY_FIRST	Do best efforts to generate target-dependent code for reducing host memory usage
NNFW_CODEGEN_PREF_COMPILE_TIME_FIRST	Do best efforts to generate target-dependent code for reducing compilation time

Definition at line 516 of file nnfw_experimental.h.

{
  NNFW_CODEGEN_PREF_DEFAULT,
  // TODO Support Traffic and Cycle code generation preference
  NNFW_CODEGEN_PREF_PERFORMANCE_FIRST,
  NNFW_CODEGEN_PREF_MEMORY_FIRST,
  NNFW_CODEGEN_PREF_COMPILE_TIME_FIRST,
} NNFW_CODEGEN_PREF;

NNFW_PREPARE_CONFIG

enum NNFW_PREPARE_CONFIG

Configuration key for prepare (compile and schedule)

Enumerator
NNFW_PREPARE_CONFIG_PROFILE	Prepare to dump execution time profile file (not require value setting) TODO: Use workspace

Definition at line 620 of file nnfw_experimental.h.

{
  NNFW_PREPARE_CONFIG_PROFILE,
} NNFW_PREPARE_CONFIG;

NNFW_QUANTIZE_TYPE

enum NNFW_QUANTIZE_TYPE

Convert between training mode and inference mode.

Note

This function should be called after nnfw_train or nnfw_prepare

Parameters

[in]	session	The session to convert training mode to inference mode
[in]	train	If false, convert training model to inference model If true, convert inference model to training model

Returns

NNFW_STATUS_NO_ERROR if successful On-Device Quantization APIs

On-Device Quantization APIs are designed to be used in the following order

1. nnfw_set_quantization_type
2. nnfw_set_quantized_model_path
3. nnfw_quantize

You should use Quantization APIs after nnfw_load_model_from_file, before nnfw_prepare and nnfw_set_input_tensorinfo.

quantization type

Enumerator
NNFW_QUANTIZE_TYPE_NOT_SET	default value: type not set
NNFW_QUANTIZE_TYPE_U8_ASYM	asymmetric quantization with a scale and zero point
NNFW_QUANTIZE_TYPE_I16_SYM	symmetric quantization with a scale only
NNFW_QUANTIZE_TYPE_WO_I8_SYM	weight-only int8 symmetric quantization
NNFW_QUANTIZE_TYPE_WO_I16_SYM	weight-only int16 symmetric quantization

Definition at line 464 of file nnfw_experimental.h.

{
  NNFW_QUANTIZE_TYPE_NOT_SET,
  NNFW_QUANTIZE_TYPE_U8_ASYM,
  NNFW_QUANTIZE_TYPE_I16_SYM,
  NNFW_QUANTIZE_TYPE_WO_I8_SYM,
  NNFW_QUANTIZE_TYPE_WO_I16_SYM,
 
} NNFW_QUANTIZE_TYPE;

NNFW_RUN_CONFIG

enum NNFW_RUN_CONFIG

Configuration key for execution.

Enumerator
NNFW_RUN_CONFIG_DUMP_MINMAX	Dump minmax data for each layers to workspace (not require value setting)
NNFW_RUN_CONFIG_TRACE	Dump execution event file to workspace (not require value setting)
NNFW_RUN_CONFIG_PROFILE	Dump execution time profile file (not require value setting) You should set prepare configuration NNFW_PREPARE_CONFIG_PROFILE before prepare. Otherwise, this configuration will be ignored. TODO: Use workspace

Definition at line 657 of file nnfw_experimental.h.

{
  NNFW_RUN_CONFIG_DUMP_MINMAX,
  NNFW_RUN_CONFIG_TRACE,
  NNFW_RUN_CONFIG_PROFILE,
} NNFW_RUN_CONFIG;

NNFW_TRAIN_LOSS

enum NNFW_TRAIN_LOSS

Training C APIs

Training APIs are designed to be used in the following order for training

1. nnfw_train_prepare
2. nnfw_train_set_input, nnfw_train_set_expected for inputs & expected outputs
3. nnfw_train
4. nnfw_train_get_loss

If you want to inference after training with the same session, you can use the following order

1. nnfw_set_input
2. nnfw_set_output
3. nnfw_run

Enumerator
NNFW_TRAIN_LOSS_UNDEFINED
NNFW_TRAIN_LOSS_MEAN_SQUARED_ERROR
NNFW_TRAIN_LOSS_CATEGORICAL_CROSSENTROPY

Definition at line 184 of file nnfw_experimental.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 191 of file nnfw_experimental.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_NUM_OF_TRAINABLE_OPS_SPECIAL_VALUES

enum NNFW_TRAIN_NUM_OF_TRAINABLE_OPS_SPECIAL_VALUES

Special values of num_of_trainable_ops. Positive values are used to indicate layers to be trained from the back of the graph.

Enumerator
NNFW_TRAIN_TRAINABLE_INCORRECT_STATE	Error value of number of trainable ops
NNFW_TRAIN_TRAINABLE_ALL	All layers will be trained
NNFW_TRAIN_TRAINABLE_NONE	No layer will be trained

Definition at line 218 of file nnfw_experimental.h.

{
  NNFW_TRAIN_TRAINABLE_INCORRECT_STATE = -2,
  NNFW_TRAIN_TRAINABLE_ALL = -1,
  NNFW_TRAIN_TRAINABLE_NONE = 0,
 
} NNFW_TRAIN_NUM_OF_TRAINABLE_OPS_SPECIAL_VALUES;

NNFW_TRAIN_OPTIMIZER

enum NNFW_TRAIN_OPTIMIZER

Enumerator
NNFW_TRAIN_OPTIMIZER_UNDEFINED
NNFW_TRAIN_OPTIMIZER_SGD
NNFW_TRAIN_OPTIMIZER_ADAM

Definition at line 201 of file nnfw_experimental.h.

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

Function Documentation

nnfw_codegen()

NNFW_STATUS nnfw_codegen	(	nnfw_session *	session,
		const char *	target,
		NNFW_CODEGEN_PREF	pref
	)

Generate target-dependent code.

This function opens a dynamic shared object. It searches for the object as flollows ld.so(8) search rules. If the nnfw_set_codegen_model_path is not called before this function, the codegen model path is automatically defined and used using the same directory of the original model/package with the target backend extension.

Parameters

[in]	session	nnfw_session the session which contains information about compilation
[in]	target	Target backend to generate code This target string will be used to find a backend library. The name of target backend library should follow the following rules: 'lib' + {backend extension} + '-gen' + {lib extension} And the target string should be a name except 'lib' and {lib extension}. For example, if the backend extension is 'aaa', the backend library should be 'libaaa-gen.so', and the target string should be 'aaa-gen'.
[in]	pref	NNFW_CODEGEN_PREF

Returns

NNFW_STATUS_NO_ERROR if successful, otherwise return NNFW_STATUS_ERROR

Definition at line 347 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->codegen(target, pref);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_input_tensorindex()

NNFW_STATUS nnfw_input_tensorindex	(	nnfw_session *	session,
		const char *	tensorname,
		uint32_t *	index
	)

Get the input tensor index by name.

This function finds an input tensor of the given name. If found, the index value is set to the address that index points to, and returns NNFW_STATUS_NO_ERROR. Otherwise, index is unchanged and returns NNFW_STATUS_ERROR .

Note

If two or more input tensors are of the same name, the one with the lowest index is always returned.

Parameters

[in]	session	the session object
[in]	tensorname	the name of the tensor to find, a null terminated char pointer string
[out]	index	the index to be ret

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 197 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->input_tensorindex(tensorname, index);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_odc_delete_minmax_file()

NNFW_STATUS nnfw_odc_delete_minmax_file

(

nnfw_session *

session

)

Delete MinMax file for on-device compiler.

Parameters

[in]

session

nnfw_session

Returns

NNFW_STATUS_NO_ERROR if successful, otherwise return NNFW_STATUS_ERROR

Definition at line 359 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->delete_odc_minmax_file();
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_output_tensorindex()

NNFW_STATUS nnfw_output_tensorindex	(	nnfw_session *	session,
		const char *	tensorname,
		uint32_t *	index
	)

Get the input tensor index by name.

This function finds an input tensor of the given name. If found, the index value is set to the address that index points to, and returns NNFW_STATUS_NO_ERROR. Otherwise, index is unchanged and returns NNFW_STATUS_ERROR .

Note

If two or more input tensors are of the same name, the one with the lowest index is always returned.

Parameters

[in]	session	the session object
[in]	tensorname	the name of the tensor to find, a null terminated char pointer string
[out]	index	the index to be ret

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 203 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->output_tensorindex(tensorname, index);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_pop_pipeline_output()

NNFW_STATUS nnfw_pop_pipeline_output	(	nnfw_session *	session,
		void *	outputs
	)

Get last outputs of partitioned model in session.

This function must be called after nnfw_prepare_pipeline, outputs given to this function must be cleared for memory management.

Parameters

[in]	session	Session from last outputs is to be extracted
[out]	outputs	Raw buffer for outputs, it must be std::vector<void *> type pointer for multiple output model

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 225 of file nnfw_api.cc.

{
  return nnfw_session::deprecated("nnfw_pop_pipeline_output: Deprecated");
}

References nnfw_session::deprecated().

nnfw_prepare_pipeline()

NNFW_STATUS nnfw_prepare_pipeline	(	nnfw_session *	session,
		const char *	map_file_path = nullptr
	)

Prepare session to be ready for inference.

This phase may finalize model compilation, scheduling, and additional settings.

Parameters

session

the session to be prepared

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 215 of file nnfw_api.cc.

{
  return nnfw_session::deprecated("nnfw_prepare_pipeline: Deprecated");
}

References nnfw_session::deprecated().

nnfw_push_pipeline_input()

NNFW_STATUS nnfw_push_pipeline_input	(	nnfw_session *	session,
		void *	inputs,
		void *	lengths
	)

Set input buffer.

This function must be called after nnfw_prepare_pipeline, inputs given to this function can be reused for many inferences. lengths must be greater or equal than the operand requires. if you give empty inputs to this function, then this function will join all threads.

Parameters

[in]	session	Session to the input is to be set
[in]	inputs	Raw buffers for input, it must be std::vector<void *> type pointer for multiple input model
[in]	lengths	Size of bytes of input buffers, it must be std::vector<uint32_t> type pointer for multiple input model

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 220 of file nnfw_api.cc.

{
  return nnfw_session::deprecated("nnfw_push_pipeline_input: Deprecated");
}

References nnfw_session::deprecated().

nnfw_quantize()

NNFW_STATUS nnfw_quantize

(

nnfw_session *

session

)

Quantize circle model.

Parameters

[in]

session

nnfw_session to quantize

Returns

ODC_STATUS_NO_ERROR if successful, otherwise return ODC_STATUS_ERROR

Definition at line 335 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->quantize();
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_register_custom_op_info()

NNFW_STATUS nnfw_register_custom_op_info	(	nnfw_session *	session,
		const char *	id,
		custom_kernel_registration_info *	info
	)

Definition at line 148 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->register_custom_operation(id, info->eval_function);
}

References info, and NNFW_RETURN_ERROR_IF_NULL.

nnfw_reset_execute_config()

NNFW_STATUS nnfw_reset_execute_config

(

nnfw_session *

session

)

Reset execution (run or train) configurations.

This function reset all execution configuration.

Parameters

[in]

session

nnfw_session to reset all execution configurations

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 394 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->reset_execute_config();
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_reset_prepare_config()

NNFW_STATUS nnfw_reset_prepare_config

(

nnfw_session *

session

)

Reset prepare configurations.

This function reset all prepare configuration.

Parameters

[in]

session

nnfw_session to reset all prepare configurations

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 381 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->reset_prepare_config();
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_run_with_auto_compilation()

NNFW_STATUS nnfw_run_with_auto_compilation	(	nnfw_session *	session,
		const char *	target,
		NNFW_CODEGEN_PREF	pref
	)

Run inference with auto compilation.

This function runs inference with automatic compilation and replaces the original model with a quantized or compiled model inside. During the inference the minmax statistics is collected and after that quantization is performed. If quantization was successful, try to code generating for target backend, otherwise run original float model. If compilation was successful, run compiled model, otherwise run quantized model. On-device compiler (ODC) provides quantization and compilation functionality. Function should be called after model is loaded by nnfw_load_model_from_file, session is prepared for inference by nnfw_prepare, set input and output buffers by nnfw_set_input and nnfw_set_output.

Additionally the following parameters should be set up :

1. Quantization type nnfw_set_quantization_type
2. Quantizated model path nnfw_set_quantized_model_path
3. Minmax records threshold for quantization nnfw_set_odc_param_minmax_records_count

1. File with minMax statistics can be removed by nnfw_odc_delete_minmax_file
2. Compiled model path nnfw_set_codegen_model_path

Parameters

[in]	session	nnfw_session
[in]	target	Target backend to generate code as in nnfw_codegen
[in]	pref	NNFW_CODEGEN_PREF

Returns

NNFW_STATUS_NO_ERROR if successful, otherwise return NNFW_STATUS_ERROR

Definition at line 365 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->run_with_auto_compilation(target, pref);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_set_backends_per_operation()

NNFW_STATUS nnfw_set_backends_per_operation	(	nnfw_session *	session,
		const char *	backend_settings
	)

Set the backend for each operation in the session.

This function assigns backends (acl_cl, acl_neon, cpu) to each operation in the session. If successful,the function returns NNFW_STATUS_NO_ERROR. Otherwise, the function returns NNFW_STATUS_ERROR.

Note

The argument specifying backends must be in the format "OP_BACKEND_MAP=\"0=acl_cl;1=cpu;2=acl_cl\"".

Parameters

[in]	session	the session object
[in]	backend_settings	String containing backend assignments indexed by operation sequence

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 209 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->set_backends_per_operation(backend_settings);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_set_codegen_model_path()

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

Set exported codegen model path.

This function should be called before nnfw_codegen is invoked.

Parameters

[in]	session	nnfw_session to set codegen model path
[in]	path	Target-dependent model path

Returns

NNFW_STATUS_NO_ERROR if successful, otherwise return NNFW_STATUS_ERROR

Definition at line 341 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->set_codegen_model_path(path);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_set_execute_config()

NNFW_STATUS nnfw_set_execute_config	(	nnfw_session *	session,
		const NNFW_RUN_CONFIG	key,
		const char *	value
	)

Set execution (run or train) configuration.

This function set execution configuration to dump execution data to workspace. If you enable configuration to dump execution data into workspace and want to change workspace, refer nnfw_set_workspace to use workspace directory.

Parameters

[in]	session	nnfw_session to set execution configuration
[in]	key	execution configuration key
[in]	value	execution configuration value if needed, otherwise set NULL

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 387 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->set_execute_config(key, value);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_set_odc_param_minmax_records_count()

NNFW_STATUS nnfw_set_odc_param_minmax_records_count	(	nnfw_session *	session,
		int	minmax_records_count
	)

Set MinMax records count in auto compilation mode with on-device compiler.

This function set MinMax records count for quantization in auto compilation mode. To enable automatic compilation mode, use nnfw_run_with_auto_compilation

Parameters

[in]	session	nnfw_session
[in]	minmax_records_count	minmax records count

Returns

NNFW_STATUS_NO_ERROR if successful, otherwise return NNFW_STATUS_ERROR

Definition at line 353 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->set_odc_param_minmax_records_count(minmax_records_count);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_set_prepare_config()

NNFW_STATUS nnfw_set_prepare_config	(	nnfw_session *	session,
		NNFW_PREPARE_CONFIG	key,
		const char *	value
	)

Set prepare configuration.

This function set prepare configuration to decide additional compiling and scheduing feature. If you enable configuration to prepare dumping execution data into workspace, refer nnfw_set_workspace to use workspace directory.

Parameters

[in]	session	nnfw_session to set prepare configuration
[in]	key	prepare configuration key
[in]	value	prepare configuration value

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 374 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->set_prepare_config(key, value);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_set_quantization_type()

NNFW_STATUS nnfw_set_quantization_type	(	nnfw_session *	session,
		NNFW_QUANTIZE_TYPE	qtype
	)

Set quantization type.

This function should be called before nnfw_quantize is invoked.

Parameters

[in]	session	nnfw_session to set quantization type
[in]	pref	NNFW_QUANTIZE_TYPE

Returns

NNFW_STATUS_NO_ERROR if successful, NNFW_STATUS_UNEXPECTED_NULL if session is null, otherwise return NNFW_STATUS_ERROR

Definition at line 323 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->set_quantization_type(qtype);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_set_quantized_model_path()

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

Set exported quantized model path.

This function should be called before nnfw_quantize is invoked.

TODO: If this function is not called, quantized model will not be exported

Parameters

[in]	session	nnfw_session to set quantized model path
[in]	path	Quantized model path

Returns

NNFW_STATUS_NO_ERROR if successful, otherwise return NNFW_STATUS_ERROR

Definition at line 329 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->set_quantized_model_path(path);
}

References NNFW_RETURN_ERROR_IF_NULL.

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);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_train_expected_tensorinfo()

NNFW_STATUS nnfw_train_expected_tensorinfo	(	nnfw_session *	session,
		uint32_t	index,
		nnfw_tensorinfo *	info
	)

Get the training model expected output information.

Note

This function should be called after nnfw_train_prepare

Parameters

[in]	session	The session to get the training model expected output information
[in]	index	The index of training model expected output
[out]	info	The shape and type of training model expected output

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 263 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->train_expected_tensorinfo(index, info);
}

References info, and NNFW_RETURN_ERROR_IF_NULL.

nnfw_train_export_checkpoint()

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

Export circle checkpoint.

Note

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

Parameters

[in]	session	The session to export a checkpoint
[in]	path	The path to export a checkpoint

Returns

NNFW_STATUS_NO_ERROR if successful

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

{
  return session->train_export_checkpoint(path);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_train_export_circle()

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

Export 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

Export 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);
}

References NNFW_RETURN_ERROR_IF_NULL.

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_get_traininfo()

NNFW_STATUS nnfw_train_get_traininfo	(	nnfw_session *	session,
		nnfw_train_info *	info
	)

Get training information.

Note

This function should be called after calling nnfw_load_model_from_file

   For the field which is not set in training information, it returns training information
   filled with default value. The default value of each field is as follows :
   learning_rate = 0.0f, batch_size = 0, *_UNDEF for other enums

Parameters

[in]	session	The session to get training information
[out]	info	Training information

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 238 of file nnfw_api.cc.

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

References info, and NNFW_RETURN_ERROR_IF_NULL.

nnfw_train_import_checkpoint()

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

Import circle checkpoint.

Note

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

Parameters

[in]	session	The session to export a checkpoint
[in]	path	The path to export a checkpoint

Returns

NNFW_STATUS_NO_ERROR if successful

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

{
  return session->train_import_checkpoint(path);
}

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_train_input_tensorinfo()

NNFW_STATUS nnfw_train_input_tensorinfo	(	nnfw_session *	session,
		uint32_t	index,
		nnfw_tensorinfo *	info
	)

Get the training model input information.

Note

This function should be called after nnfw_train_prepare

Parameters

[in]	session	The session to get the training model input information
[in]	index	The index of training model input
[out]	info	The shape and type of training model input

Returns

NNFW_STATUS_NO_ERROR if successful

Definition at line 256 of file nnfw_api.cc.

{
  NNFW_RETURN_ERROR_IF_NULL(session);
  return session->train_input_tensorinfo(index, info);
}

References info, and NNFW_RETURN_ERROR_IF_NULL.

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(); }

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_train_set_expected()

NNFW_STATUS nnfw_train_set_expected	(	nnfw_session *	session,
		uint32_t	index,
		const 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 277 of file nnfw_api.cc.

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

References NNFW_RETURN_ERROR_IF_NULL.

nnfw_train_set_input()

NNFW_STATUS nnfw_train_set_input	(	nnfw_session *	session,
		uint32_t	index,
		const 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 270 of file nnfw_api.cc.

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

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.