Overview

API	Description
Pipeline	Provides interfaces to create and execute stream pipelines with neural networks and sensors.
Service	Provides interfaces to store and fetch the pipeline description for AI application developers.
Single	Provides interfaces to invoke a neural network model with a single instance of input data.
Trainer	Provides interfaces to create and train Machine Learning models on the device locally.

Functions
int	ml_tensors_info_create (ml_tensors_info_h *info)
	Creates a tensors information handle with default value.
int	ml_tensors_info_create_extended (ml_tensors_info_h *info)
	Creates an extended tensors information handle with default value.
int	ml_tensors_info_destroy (ml_tensors_info_h info)
	Frees the given handle of a tensors information.
int	ml_tensors_info_validate (const ml_tensors_info_h info, bool *valid)
	Validates the given tensors information.
int	ml_tensors_info_clone (ml_tensors_info_h dest, const ml_tensors_info_h src)
	Copies the tensors information.
int	ml_tensors_info_set_count (ml_tensors_info_h info, unsigned int count)
	Sets the number of tensors with given handle of tensors information.
int	ml_tensors_info_get_count (ml_tensors_info_h info, unsigned int *count)
	Gets the number of tensors with given handle of tensors information.
int	ml_tensors_info_set_tensor_name (ml_tensors_info_h info, unsigned int index, const char *name)
	Sets the tensor name with given handle of tensors information.
int	ml_tensors_info_get_tensor_name (ml_tensors_info_h info, unsigned int index, char **name)
	Gets the tensor name with given handle of tensors information.
int	ml_tensors_info_set_tensor_type (ml_tensors_info_h info, unsigned int index, const ml_tensor_type_e type)
	Sets the tensor type with given handle of tensors information.
int	ml_tensors_info_get_tensor_type (ml_tensors_info_h info, unsigned int index, ml_tensor_type_e *type)
	Gets the tensor type with given handle of tensors information.
int	ml_tensors_info_set_tensor_dimension (ml_tensors_info_h info, unsigned int index, const ml_tensor_dimension dimension)
	Sets the tensor dimension with given handle of tensors information.
int	ml_tensors_info_get_tensor_dimension (ml_tensors_info_h info, unsigned int index, ml_tensor_dimension dimension)
	Gets the tensor dimension with given handle of tensors information.
int	ml_tensors_info_get_tensor_size (ml_tensors_info_h info, int index, size_t *data_size)
	Gets the size of tensors data in the given tensors information handle in bytes.
int	ml_tensors_data_create (const ml_tensors_info_h info, ml_tensors_data_h *data)
	Creates a tensor data frame with the given tensors information.
int	ml_tensors_data_destroy (ml_tensors_data_h data)
	Frees the given tensors' data handle.
int	ml_tensors_data_get_tensor_data (ml_tensors_data_h data, unsigned int index, void *raw_data, size_t data_size)
	Gets a tensor data of given handle.
int	ml_tensors_data_set_tensor_data (ml_tensors_data_h data, unsigned int index, const void *raw_data, const size_t data_size)
	Copies a tensor data to given handle.
int	ml_tensors_data_clone (const ml_tensors_data_h in, ml_tensors_data_h *out)
	Copies the tensor data frame.
int	ml_tensors_data_get_info (const ml_tensors_data_h data, ml_tensors_info_h *info)
	Gets the tensors information of given tensor data frame.
const char *	ml_error (void)
	Returns a human-readable string describing the last error.
const char *	ml_strerror (int error_code)
	Returns a human-readable string describing an error code.
int	ml_option_create (ml_option_h *option)
	Creates ml-option instance.
int	ml_option_destroy (ml_option_h option)
	Destroys the ml-option instance.
int	ml_option_set (ml_option_h option, const char key, void value, ml_data_destroy_cb destroy)
	Sets a new key-value in ml-option instance.
int	ml_option_get (ml_option_h option, const char key, void *value)
	Gets a value of key in ml-option instance.
int	ml_information_destroy (ml_information_h ml_info)
	Destroys the ml-information instance.
int	ml_information_get (ml_information_h ml_info, const char key, void *value)
	Gets a value of key in ml-information instance.
int	ml_information_list_destroy (ml_information_list_h ml_info_list)
	Destroys the ml-information-list instance.
int	ml_information_list_length (ml_information_list_h ml_info_list, unsigned int *length)
	Gets the number of ml-information in ml-information-list instance.
int	ml_information_list_get (ml_information_list_h ml_info_list, unsigned int index, ml_information_h *ml_info)
	Gets a ml-information in ml-information-list instance with given index.
Typedefs
typedef unsigned int	ml_tensor_dimension [(16)]
	The dimensions of a tensor that NNStreamer supports.
typedef void *	ml_tensors_info_h
	A handle of a tensors metadata instance.
typedef void *	ml_tensors_data_h
	A handle of input or output frames. ml_tensors_info_h is the handle for tensors metadata.
typedef enum _ml_tensor_type_e	ml_tensor_type_e
	Possible data element types of tensor in NNStreamer.
typedef void(*	ml_data_destroy_cb )(void *data)
	The function to be called when destroying the data in machine learning API.
typedef void *	ml_option_h
	A handle of a ml-option instance.
typedef void *	ml_information_h
	A handle of a ml-information instance.
typedef void *	ml_information_list_h
	A handle of a list of ml-information instance.
Defines
#define	ML_TENSOR_RANK_LIMIT (16)
	The maximum rank that NNStreamer supports with Tizen APIs.
#define	ML_TENSOR_SIZE_LIMIT (256)
	The maximum number of other/tensor instances that other/tensors may have.

Define Documentation

#define ML_TENSOR_RANK_LIMIT (16)

The maximum rank that NNStreamer supports with Tizen APIs.

Since :: 5.5

Remarks:: The maximum rank in Tizen APIs is 4 until tizen 7.0 and 16 since 8.0.

#define ML_TENSOR_SIZE_LIMIT (256)

The maximum number of other/tensor instances that other/tensors may have.

Since :: 5.5

Remarks:: The maximum size in Tizen APIs is 16 until tizen 7.0 and 256 since 8.0.

Typedef Documentation

typedef void(* ml_data_destroy_cb)(void *data)

The function to be called when destroying the data in machine learning API.

Since :: 7.0

Parameters:

[in] data The data to be destroyed.

typedef void* ml_information_h

A handle of a ml-information instance.

Since :: 8.0

typedef void* ml_information_list_h

A handle of a list of ml-information instance.

Since :: 8.0

typedef void* ml_option_h

A handle of a ml-option instance.

Since :: 7.0

typedef unsigned int ml_tensor_dimension[(16)]

The dimensions of a tensor that NNStreamer supports.

Since :: 5.5

typedef enum _ml_tensor_type_e ml_tensor_type_e

Possible data element types of tensor in NNStreamer.

Since :: 5.5

typedef void* ml_tensors_data_h

A handle of input or output frames. ml_tensors_info_h is the handle for tensors metadata.

Since :: 5.5

typedef void* ml_tensors_info_h

A handle of a tensors metadata instance.

Since :: 5.5

Enumeration Type Documentation

enum _ml_tensor_type_e

Possible data element types of tensor in NNStreamer.

Since :: 5.5

Enumerator:

ML_TENSOR_TYPE_INT32	Integer 32bit
ML_TENSOR_TYPE_UINT32	Unsigned integer 32bit
ML_TENSOR_TYPE_INT16	Integer 16bit
ML_TENSOR_TYPE_UINT16	Unsigned integer 16bit
ML_TENSOR_TYPE_INT8	Integer 8bit
ML_TENSOR_TYPE_UINT8	Unsigned integer 8bit
ML_TENSOR_TYPE_FLOAT64	Float 64bit
ML_TENSOR_TYPE_FLOAT32	Float 32bit
ML_TENSOR_TYPE_INT64	Integer 64bit
ML_TENSOR_TYPE_UINT64	Unsigned integer 64bit
ML_TENSOR_TYPE_FLOAT16	FP16, IEEE 754. Note that this type is supported only in aarch64/arm devices. (Since 7.0)
ML_TENSOR_TYPE_UNKNOWN	Unknown type

enum ml_error_e

Enumeration for the error codes of NNStreamer.

Since :: 5.5

Enumerator:

ML_ERROR_NONE	Success!
ML_ERROR_INVALID_PARAMETER	Invalid parameter
ML_ERROR_STREAMS_PIPE	Cannot create or access the pipeline.
ML_ERROR_TRY_AGAIN	The pipeline is not ready, yet (not negotiated, yet)
ML_ERROR_UNKNOWN	Unknown error
ML_ERROR_TIMED_OUT	Time out
ML_ERROR_NOT_SUPPORTED	The feature is not supported
ML_ERROR_PERMISSION_DENIED	Permission denied
ML_ERROR_OUT_OF_MEMORY	Out of memory (Since 6.0)
ML_ERROR_IO_ERROR	I/O error for database and filesystem (Since 7.0)

enum ml_nnfw_hw_e

Types of hardware resources to be used for NNFWs. Note that if the affinity (nnn) is not supported by the driver or hardware, it is ignored.

Since :: 5.5

Enumerator:

ML_NNFW_HW_ANY	Hardware resource is not specified.
ML_NNFW_HW_AUTO	Try to schedule and optimize if possible.
ML_NNFW_HW_CPU	0x1000: any CPU. 0x1nnn: CPU # nnn-1.
ML_NNFW_HW_CPU_SIMD	0x1100: SIMD in CPU. (Since 6.0)
ML_NNFW_HW_CPU_NEON	0x1100: NEON (alias for SIMD) in CPU. (Since 6.0)
ML_NNFW_HW_GPU	0x2000: any GPU. 0x2nnn: GPU # nnn-1.
ML_NNFW_HW_NPU	0x3000: any NPU. 0x3nnn: NPU # nnn-1.
ML_NNFW_HW_NPU_MOVIDIUS	0x3001: Intel Movidius Stick. (Since 6.0)
ML_NNFW_HW_NPU_EDGE_TPU	0x3002: Google Coral Edge TPU (USB). (Since 6.0)
ML_NNFW_HW_NPU_VIVANTE	0x3003: VeriSilicon's Vivante. (Since 6.0)
ML_NNFW_HW_NPU_SLSI	0x3004: Samsung S.LSI. (Since 6.5)
ML_NNFW_HW_NPU_SR	0x13000: any SR (Samsung Research) made NPU. (Since 6.0)

enum ml_nnfw_type_e

Types of NNFWs.

To check if a nnfw-type is supported in a system, an application may call the API, ml_check_nnfw_availability().

Since :: 5.5

Enumerator:

ML_NNFW_TYPE_ANY	NNFW is not specified (Try to determine the NNFW with file extension).
ML_NNFW_TYPE_CUSTOM_FILTER	Custom filter (Independent shared object).
ML_NNFW_TYPE_TENSORFLOW_LITE	Tensorflow-lite (.tflite).
ML_NNFW_TYPE_TENSORFLOW	Tensorflow (.pb).
ML_NNFW_TYPE_NNFW	Neural Network Inference framework, which is developed by SR (Samsung Research).
ML_NNFW_TYPE_MVNC	Intel Movidius Neural Compute SDK (libmvnc). (Since 6.0)
ML_NNFW_TYPE_OPENVINO	Intel OpenVINO. (Since 6.0)
ML_NNFW_TYPE_VIVANTE	VeriSilicon's Vivante. (Since 6.0)
ML_NNFW_TYPE_EDGE_TPU	Google Coral Edge TPU (USB). (Since 6.0)
ML_NNFW_TYPE_ARMNN	Arm Neural Network framework (support for caffe and tensorflow-lite). (Since 6.0)
ML_NNFW_TYPE_SNPE	Qualcomm SNPE (Snapdragon Neural Processing Engine (.dlc). (Since 6.0)
ML_NNFW_TYPE_PYTORCH	PyTorch (.pt). (Since 6.5)
ML_NNFW_TYPE_NNTR_INF	Inference supported from NNTrainer, SR On-device Training Framework (Since 6.5)
ML_NNFW_TYPE_VD_AIFW	Inference framework for Samsung Tizen TV (Since 6.5)
ML_NNFW_TYPE_TRIX_ENGINE	TRIxENGINE accesses TRIV/TRIA NPU low-level drivers directly (.tvn). (Since 6.5) You may need to use high-level drivers wrapping this low-level driver in some devices: e.g., AIFW
ML_NNFW_TYPE_MXNET	Apache MXNet (Since 7.0)
ML_NNFW_TYPE_TVM	Apache TVM (Since 7.0)
ML_NNFW_TYPE_ONNX_RUNTIME	Open Neural Network Exchange (ONNX) Runtime (Since 9.0)
ML_NNFW_TYPE_NCNN	Tencent ncnn (Since 9.0)
ML_NNFW_TYPE_SNAP	SNAP (Samsung Neural Acceleration Platform), only for Android. (Since 6.0)

Function Documentation

const char* ml_error ( void )

Returns a human-readable string describing the last error.

This returns a human-readable, null-terminated string describing the most recent error that occurred from a call to one of the functions in the Machine Learning API since the last call to ml_error(). The returned string should *not* be freed or overwritten by the caller.

Since :: 7.0

Returns:: NULL if no error to be reported. Otherwise the error description.

int ml_information_destroy ( ml_information_h ml_info )

Destroys the ml-information instance.

Note that, user should free the allocated values of ml-information in the case that destroy function is not given.

Since :: 8.0

Parameters:

[in] ml_info The ml_information handle to be destroyed.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Fail. The parameter is invalid.

int ml_information_get	(	ml_information_h	ml_info,
		const char *	key,
		void **	value
	)

Gets a value of key in ml-information instance.

This returns the pointer of memory in the handle. If you modify the returned memory (value), the contents of value is updated.

Since :: 8.0

Remarks:: The value should not be released. The value is available until ml_info is destroyed using ml_information_destroy().

Parameters:

[in]	ml_info	The handle of ml-information.
[in]	key	The key to get the corresponding value.
[out]	value	The value of the key.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Fail. The parameter is invalid.

int ml_information_list_destroy ( ml_information_list_h ml_info_list )

Destroys the ml-information-list instance.

Note that, user should free the allocated values of ml-information-list in the case that destroy function is not given.

Since :: 8.0

Parameters:

[in] ml_info_list The ml-information-list handle to be destroyed.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Fail. The parameter is invalid.

int ml_information_list_get	(	ml_information_list_h	ml_info_list,
		unsigned int	index,
		ml_information_h *	ml_info
	)

Gets a ml-information in ml-information-list instance with given index.

Since :: 8.0

Remarks:: The ml_info should not be released. The ml_info is available until ml_info_list is destroyed using ml_information_list_destroy().

Parameters:

[in]	ml_info_list	The handle of ml-information-list.
[in]	index	The index of ml-information in ml-information-list.
[out]	ml_info	The ml-information handle of given index.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Fail. The parameter is invalid.

int ml_information_list_length	(	ml_information_list_h	ml_info_list,
		unsigned int *	length
	)

Gets the number of ml-information in ml-information-list instance.

Since :: 8.0

Parameters:

[in]	ml_info_list	The handle of ml-information-list.
[out]	length	The number of ml-information in ml-information-list.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Fail. The parameter is invalid.

int ml_option_create ( ml_option_h * option )

Creates ml-option instance.

Since :: 7.0

Remarks:: The option should be released using ml_option_destroy().

Parameters:

[out] option Newly created option handle is returned.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Fail. The parameter is invalid.
ML_ERROR_OUT_OF_MEMORY	Failed to allocate required memory.

int ml_option_destroy ( ml_option_h option )

Destroys the ml-option instance.

Note that, user should free the allocated values of ml-option in the case that destroy function is not given.

Since :: 7.0

Parameters:

[in] option The option handle to be destroyed.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Fail. The parameter is invalid.

int ml_option_get	(	ml_option_h	option,
		const char *	key,
		void **	value
	)

Gets a value of key in ml-option instance.

This returns the pointer of memory in the handle. Do not deallocate the returned value. If you modify the returned memory (value), the contents of value is updated.

Since :: 8.0

Parameters:

[in]	option	The handle of ml-option.
[in]	key	The key to get the corresponding value.
[out]	value	The value of the key.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Fail. The parameter is invalid.

int ml_option_set	(	ml_option_h	option,
		const char *	key,
		void *	value,
		ml_data_destroy_cb	destroy
	)

Sets a new key-value in ml-option instance.

Note that the value should be valid during single task and be freed after destroying the ml-option instance unless proper destroy function is given. When duplicated key is given, the corresponding value is updated with the new one.

Since :: 7.0

Parameters:

[in]	option	The handle of ml-option.
[in]	key	The key to be set.
[in]	value	The value to be set.
[in]	destroy	The function to destroy the value. It is called when the ml-option instance is destroyed.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Fail. The parameter is invalid.

const char* ml_strerror ( int error_code )

Returns a human-readable string describing an error code.

This returns a human-readable, null-terminated string describing the error code of machine learning API. The returned string should *not* be freed or overwritten by the caller.

Since :: 7.0

Parameters:

[in] error_code The error code of machine learning API.

Returns:: NULL for invalid error code. Otherwise the error description.

int ml_tensors_data_clone	(	const ml_tensors_data_h	in,
		ml_tensors_data_h *	out
	)

Copies the tensor data frame.

Since :: 9.0

Remarks:: The out should be released using ml_tensors_data_destroy().

Parameters:

[in]	in	The handle of tensors data to be cloned.
[out]	out	The handle of tensors data.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.
ML_ERROR_OUT_OF_MEMORY	Failed to allocate required memory.

int ml_tensors_data_create	(	const ml_tensors_info_h	info,
		ml_tensors_data_h *	data
	)

Creates a tensor data frame with the given tensors information.

Since :: 5.5

Remarks:: The data should be released using ml_tensors_data_destroy().; Before 6.0, this function returned ML_ERROR_STREAMS_PIPE in case of an internal error. Since 6.0, ML_ERROR_OUT_OF_MEMORY is returned in such cases, so ML_ERROR_STREAMS_PIPE is not returned by this function anymore.

Parameters:

[in]	info	The handle of tensors information for the allocation.
[out]	data	The handle of tensors data.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.
ML_ERROR_OUT_OF_MEMORY	Failed to allocate required memory.

int ml_tensors_data_destroy ( ml_tensors_data_h data )

Frees the given tensors' data handle.

Note that the opened handle should be closed before calling this function in the case of a single API. If not, the inference engine might try to access the data that is already freed. And it causes the segmentation fault.

Since :: 5.5

Parameters:

[in] data The handle of tensors data.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.

int ml_tensors_data_get_info	(	const ml_tensors_data_h	data,
		ml_tensors_info_h *	info
	)

Gets the tensors information of given tensor data frame.

Since :: 9.0

Remarks:: The info should be released using ml_tensors_info_destroy().

Parameters:

[in]	data	The handle of tensors data.
[out]	info	The handle of tensors information.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.
ML_ERROR_OUT_OF_MEMORY	Failed to allocate required memory.

int ml_tensors_data_get_tensor_data	(	ml_tensors_data_h	data,
		unsigned int	index,
		void **	raw_data,
		size_t *	data_size
	)

Gets a tensor data of given handle.

This returns the pointer of memory block in the handle. Do not deallocate the returned tensor data. The returned pointer (raw_data) directly points to the internal data of data. If you modify the returned memory block (raw_data), the contents of data is updated.

Since :: 5.5

Parameters:

[in]	data	The handle of tensors data.
[in]	index	The index of the tensor.
[out]	raw_data	Raw tensor data in the handle.
[out]	data_size	Byte size of tensor data.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.

int ml_tensors_data_set_tensor_data	(	ml_tensors_data_h	data,
		unsigned int	index,
		const void *	raw_data,
		const size_t	data_size
	)

Copies a tensor data to given handle.

Since :: 5.5

Parameters:

[in]	data	The handle of tensors data.
[in]	index	The index of the tensor.
[in]	raw_data	Raw tensor data to be copied.
[in]	data_size	Byte size of raw data.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.

int ml_tensors_info_clone	(	ml_tensors_info_h	dest,
		const ml_tensors_info_h	src
	)

Copies the tensors information.

Since :: 5.5

Parameters:

[out]	dest	A destination handle of tensors information.
[in]	src	The tensors information to be copied.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid. Note that src should be a valid tensors info handle and dest should be a created (allocated) tensors info handle.

int ml_tensors_info_create ( ml_tensors_info_h * info )

Creates a tensors information handle with default value.

Since :: 5.5

Remarks:: The info should be released using ml_tensors_info_destroy().

Parameters:

[out] info The handle of tensors information.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.
ML_ERROR_OUT_OF_MEMORY	Failed to allocate required memory.

int ml_tensors_info_create_extended ( ml_tensors_info_h * info )

Creates an extended tensors information handle with default value.

An extended tensors support higher rank limit.

Since :: 8.0

Remarks:: The info should be released using ml_tensors_info_destroy().

Parameters:

[out] info The handle of tensors information.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.
ML_ERROR_OUT_OF_MEMORY	Failed to allocate required memory.

int ml_tensors_info_destroy ( ml_tensors_info_h info )

Frees the given handle of a tensors information.

Since :: 5.5

Parameters:

[in] info The handle of tensors information.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.

int ml_tensors_info_get_count	(	ml_tensors_info_h	info,
		unsigned int *	count
	)

Gets the number of tensors with given handle of tensors information.

Since :: 5.5

Parameters:

[in]	info	The handle of tensors information.
[out]	count	The number of tensors.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.

int ml_tensors_info_get_tensor_dimension	(	ml_tensors_info_h	info,
		unsigned int	index,
		ml_tensor_dimension	dimension
	)

Gets the tensor dimension with given handle of tensors information.

Since :: 5.5

Parameters:

[in]	info	The handle of tensors information.
[in]	index	The index of the tensor.
[out]	dimension	The tensor dimension.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.

int ml_tensors_info_get_tensor_name	(	ml_tensors_info_h	info,
		unsigned int	index,
		char **	name
	)

Gets the tensor name with given handle of tensors information.

Since :: 5.5

Remarks:: Before 6.0 this function returned the internal pointer so application developers do not need to free. Since 6.0 the name string is internally copied and returned. So if the function succeeds, name should be released using g_free().

Parameters:

[in]	info	The handle of tensors information.
[in]	index	The index of the tensor.
[out]	name	The tensor name.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.

int ml_tensors_info_get_tensor_size	(	ml_tensors_info_h	info,
		int	index,
		size_t *	data_size
	)

Gets the size of tensors data in the given tensors information handle in bytes.

If an application needs to get the total byte size of tensors, set the index '-1'. Note that the maximum number of tensors is ML_TENSOR_SIZE_LIMIT.

Since :: 5.5

Parameters:

[in]	info	The handle of tensors information.
[in]	index	The index of the tensor.
[out]	data_size	The byte size of tensor data.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.

int ml_tensors_info_get_tensor_type	(	ml_tensors_info_h	info,
		unsigned int	index,
		ml_tensor_type_e *	type
	)

Gets the tensor type with given handle of tensors information.

Since :: 5.5

Parameters:

[in]	info	The handle of tensors information.
[in]	index	The index of the tensor.
[out]	type	The tensor type.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.

int ml_tensors_info_set_count	(	ml_tensors_info_h	info,
		unsigned int	count
	)

Sets the number of tensors with given handle of tensors information.

Since :: 5.5

Parameters:

[in]	info	The handle of tensors information.
[in]	count	The number of tensors.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.

int ml_tensors_info_set_tensor_dimension	(	ml_tensors_info_h	info,
		unsigned int	index,
		const ml_tensor_dimension	dimension
	)

Sets the tensor dimension with given handle of tensors information.

Since :: 5.5

Parameters:

[in]	info	The handle of tensors information.
[in]	index	The index of the tensor to be updated.
[in]	dimension	The tensor dimension to be set.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.

int ml_tensors_info_set_tensor_name	(	ml_tensors_info_h	info,
		unsigned int	index,
		const char *	name
	)

Sets the tensor name with given handle of tensors information.

Since :: 5.5

Parameters:

[in]	info	The handle of tensors information.
[in]	index	The index of the tensor to be updated.
[in]	name	The tensor name to be set.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.

int ml_tensors_info_set_tensor_type	(	ml_tensors_info_h	info,
		unsigned int	index,
		const ml_tensor_type_e	type
	)

Sets the tensor type with given handle of tensors information.

Since :: 5.5

Parameters:

[in]	info	The handle of tensors information.
[in]	index	The index of the tensor to be updated.
[in]	type	The tensor type to be set.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported. E.g., in a machine without fp16 support, trying FLOAT16 is not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.

int ml_tensors_info_validate	(	const ml_tensors_info_h	info,
		bool *	valid
	)

Validates the given tensors information.

If the function returns an error, valid may not be changed.

Since :: 5.5

Parameters:

[in]	info	The handle of tensors information to be validated.
[out]	valid	`true` if it's valid, `false` if it's invalid.

Returns:: 0 on success. Otherwise a negative error value.

Return values:

ML_ERROR_NONE	Successful.
ML_ERROR_NOT_SUPPORTED	Not supported.
ML_ERROR_INVALID_PARAMETER	Given parameter is invalid.

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