Tizen Native API
7.0
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The NNStreamer Single API provides interfaces to invoke a neural network model with a single instance of input data.
Required Header
#include <nnstreamer/nnstreamer-single.h>
Overview
The NNStreamer Single API provides interfaces to invoke a neural network model with a single instance of input data. This function is a syntactic sugar of NNStreamer Pipeline API with simplified features; thus, users are supposed to use NNStreamer Pipeline API directly if they want more advanced features. The user is expected to preprocess the input data for the given neural network model.
This function allows the following operations with NNStreamer:
- Open a machine learning model with various mechanisms.
- Close the model.
- Interfaces to enter a single instance of input data to the opened model.
- Utility functions to get the information of opened model.
Note that this function set is supposed to be thread-safe.
Related Features
This function is related with the following features:
- http://tizen.org/feature/machine_learning
- http://tizen.org/feature/machine_learning.inference
It is recommended to probe features in your application for reliability.
You can check if a device supports the related features for this function by using System Information, thereby controlling the procedure of your application.
To ensure your application is only running on the device with specific features, please define the features in your manifest file using the manifest editor in the SDK.
For example, your application accesses to the camera device, then you have to add 'http://tizen.org/privilege/camera' into the manifest of your application.
More details on featuring your application can be found from Feature Element.
Functions | |
int | ml_single_open (ml_single_h *single, const char *model, const ml_tensors_info_h input_info, const ml_tensors_info_h output_info, ml_nnfw_type_e nnfw, ml_nnfw_hw_e hw) |
Opens an ML model and returns the instance as a handle. | |
int | ml_single_open_full (ml_single_h *single, const char *model, const ml_tensors_info_h input_info, const ml_tensors_info_h output_info, ml_nnfw_type_e nnfw, ml_nnfw_hw_e hw, const char *custom_option) |
Opens an ML model and returns the instance as a handle with custom options. | |
int | ml_single_close (ml_single_h single) |
Closes the opened model handle. | |
int | ml_single_invoke (ml_single_h single, const ml_tensors_data_h input, ml_tensors_data_h *output) |
Invokes the model with the given input data. | |
int | ml_single_invoke_fast (ml_single_h single, const ml_tensors_data_h input, ml_tensors_data_h output) |
Invokes the model with the given input data and fills the output data handle. This is generally faster than ml_single_invoke(). | |
int | ml_single_invoke_dynamic (ml_single_h single, const ml_tensors_data_h input, const ml_tensors_info_h in_info, ml_tensors_data_h *output, ml_tensors_info_h *out_info) |
Invokes the model with the given input data with the given tensors information. | |
int | ml_single_get_input_info (ml_single_h single, ml_tensors_info_h *info) |
Gets the information (tensor dimension, type, name and so on) of required input data for the given model. | |
int | ml_single_get_output_info (ml_single_h single, ml_tensors_info_h *info) |
Gets the information (tensor dimension, type, name and so on) of output data for the given model. | |
int | ml_single_set_input_info (ml_single_h single, const ml_tensors_info_h info) |
Sets the information (tensor dimension, type, name and so on) of required input data for the given model. | |
int | ml_single_set_timeout (ml_single_h single, unsigned int timeout) |
Sets the maximum amount of time to wait for an output, in milliseconds. | |
int | ml_single_set_property (ml_single_h single, const char *name, const char *value) |
Sets the property value for the given model. | |
int | ml_single_get_property (ml_single_h single, const char *name, char **value) |
Gets the property value for the given model. | |
int | ml_single_open_with_option (ml_single_h *single, const ml_option_h option) |
Makes a single instance with given ml-option. | |
Typedefs | |
typedef void * | ml_single_h |
A handle of a single-shot instance. |
Typedef Documentation
typedef void* ml_single_h |
A handle of a single-shot instance.
- Since :
- 5.5
Function Documentation
int ml_single_close | ( | ml_single_h | single | ) |
Closes the opened model handle.
Note that this should be called before destroying the inference data by ml_tensors_data_destroy(). 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] single The model handle to be closed.
- 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 (Pipeline is not negotiated yet.)
int ml_single_get_input_info | ( | ml_single_h | single, |
ml_tensors_info_h * | info | ||
) |
Gets the information (tensor dimension, type, name and so on) of required input data for the given model.
Note that a model may not have such information if its input type is flexible. The names of tensors are sometimes unavailable (optional), while its dimensions and types are always available.
- Since :
- 5.5
- Parameters:
-
[in] single The model handle. [out] info The handle of input tensors information. The caller is responsible for freeing the information with ml_tensors_info_destroy().
- 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_single_get_output_info | ( | ml_single_h | single, |
ml_tensors_info_h * | info | ||
) |
Gets the information (tensor dimension, type, name and so on) of output data for the given model.
Note that a model may not have such information if its output type is flexible and output type is not determined statically. The names of tensors are sometimes unavailable (optional), while its dimensions and types are always available.
- Since :
- 5.5
- Parameters:
-
[in] single The model handle. [out] info The handle of output tensors information. The caller is responsible for freeing the information with ml_tensors_info_destroy().
- 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_single_get_property | ( | ml_single_h | single, |
const char * | name, | ||
char ** | value | ||
) |
Gets the property value for the given model.
- Since :
- 6.0
- Parameters:
-
[in] single The model handle. [in] name The property name. [out] value The property value. The caller is responsible for freeing the value using g_free().
- 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_single_invoke | ( | ml_single_h | single, |
const ml_tensors_data_h | input, | ||
ml_tensors_data_h * | output | ||
) |
Invokes the model with the given input data.
Even if the model has flexible input data dimensions, input data frames of an instance of a model should share the same dimension. Note that this will wait for the result until the invoke process is done. If an application wants to change the time to wait for an output, set the timeout using ml_single_set_timeout().
- Since :
- 5.5
- Parameters:
-
[in] single The model handle to be inferred. [in] input The input data to be inferred. [out] output The allocated output buffer. The caller is responsible for freeing the output buffer with ml_tensors_data_destroy().
- Returns:
0
on success. Otherwise a negative error value.
- Note:
- If the data for the output buffer is allocated by the neural network framework (ML_NNFW_TYPE_CUSTOM_FILTER supports this), then this buffer will be freed when closing the single automatically by the neural network framework, and will not available for use later. It is recommended to copy the output buffer from output if it is required to use it after the single handle is closed.
- Return values:
-
ML_ERROR_NONE Successful. ML_ERROR_NOT_SUPPORTED Not supported. ML_ERROR_INVALID_PARAMETER Fail. The parameter is invalid. ML_ERROR_STREAMS_PIPE Failed to push a buffer into source element. ML_ERROR_TIMED_OUT Failed to get the result from sink element. ML_ERROR_OUT_OF_MEMORY Failed to allocate required memory.
int ml_single_invoke_dynamic | ( | ml_single_h | single, |
const ml_tensors_data_h | input, | ||
const ml_tensors_info_h | in_info, | ||
ml_tensors_data_h * | output, | ||
ml_tensors_info_h * | out_info | ||
) |
Invokes the model with the given input data with the given tensors information.
This function changes the input tensors information for the model, and returns the corresponding output data. A model/framework may not support changing the information. Note that this will wait for the result until the invoke process is done. If an application wants to change the time to wait for an output, set the timeout using ml_single_set_timeout().
- Since :
- 6.0
- Parameters:
-
[in] single The model handle to be inferred. [in] input The input data to be inferred. [in] in_info The handle of input tensors information. [out] output The allocated output buffer. The caller is responsible for freeing the output buffer with ml_tensors_data_destroy(). [out] out_info The handle of output tensors information. The caller is responsible for freeing the information with ml_tensors_info_destroy().
- 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_STREAMS_PIPE Failed to push a buffer into source element. ML_ERROR_TIMED_OUT Failed to get the result from sink element. ML_ERROR_OUT_OF_MEMORY Failed to allocate required memory.
int ml_single_invoke_fast | ( | ml_single_h | single, |
const ml_tensors_data_h | input, | ||
ml_tensors_data_h | output | ||
) |
Invokes the model with the given input data and fills the output data handle. This is generally faster than ml_single_invoke().
The caller should preallocate memory buffers of the given output handle before calling the API. Note that ml_single_invoke() allocates memory buffers of the output handle in the API, which may incur memcpy.
- Since :
- 6.5
- Parameters:
-
[in] single The model handle to be inferred. [in] input The input data to be inferred. [in,out] output The output data to be filled by the API. Output should be preallocated before calling the API.
- 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_STREAMS_PIPE Failed to push a buffer into source element. ML_ERROR_TIMED_OUT Failed to get the result from sink element. ML_ERROR_OUT_OF_MEMORY Failed to allocate required memory.
int ml_single_open | ( | ml_single_h * | single, |
const char * | model, | ||
const ml_tensors_info_h | input_info, | ||
const ml_tensors_info_h | output_info, | ||
ml_nnfw_type_e | nnfw, | ||
ml_nnfw_hw_e | hw | ||
) |
Opens an ML model and returns the instance as a handle.
Even if the model has flexible input data dimensions, input data frames of an instance of a model should share the same dimension.
- Since :
- 5.5
- Remarks:
- http://tizen.org/privilege/mediastorage is needed if model is relevant to media storage.
- http://tizen.org/privilege/externalstorage is needed if model is relevant to external storage.
- Parameters:
-
[out] single This is the model handle opened. Users are required to close the given instance with ml_single_close(). [in] model This is the path to the neural network model file. [in] input_info This is required if the given model has flexible input dimension, where the input dimension MUST be given before executing the model. It is required by some custom filters of NNStreamer. You may set NULL if it's not required. [in] output_info This is required if the given model has flexible output dimension. [in] nnfw The neural network framework used to open the given model. Set ML_NNFW_TYPE_ANY to let it auto-detect. [in] hw Tell the corresponding nnfw to use a specific hardware. Set ML_NNFW_HW_ANY if it does not matter.
- Returns:
0
on success. Otherwise a negative error value.
- Return values:
-
ML_ERROR_NONE Successful. ML_ERROR_NOT_SUPPORTED Not supported. ML_ERROR_PERMISSION_DENIED The application does not have the privilege to access to the media storage or external storage. ML_ERROR_INVALID_PARAMETER Fail. The parameter is invalid. ML_ERROR_STREAMS_PIPE Failed to start the pipeline. ML_ERROR_OUT_OF_MEMORY Failed to allocate required memory.
int ml_single_open_full | ( | ml_single_h * | single, |
const char * | model, | ||
const ml_tensors_info_h | input_info, | ||
const ml_tensors_info_h | output_info, | ||
ml_nnfw_type_e | nnfw, | ||
ml_nnfw_hw_e | hw, | ||
const char * | custom_option | ||
) |
Opens an ML model and returns the instance as a handle with custom options.
Even if the model has flexible input data dimensions, input data frames of an instance of a model should share the same dimension.
- Since :
- 6.5
- Remarks:
- http://tizen.org/privilege/mediastorage is needed if model is relevant to media storage.
- http://tizen.org/privilege/externalstorage is needed if model is relevant to external storage.
- Parameters:
-
[out] single This is the model handle opened. Users are required to close the given instance with ml_single_close(). [in] model This is the path to the neural network model file. [in] input_info This is required if the given model has flexible input dimension, where the input dimension MUST be given before executing the model. It is required by some custom filters of NNStreamer. You may set NULL if it's not required. [in] output_info This is required if the given model has flexible output dimension. [in] nnfw The neural network framework used to open the given model. Set ML_NNFW_TYPE_ANY to let it auto-detect. [in] hw Tell the corresponding nnfw to use a specific hardware. Set ML_NNFW_HW_ANY if it does not matter. [in] custom_option Comma separated list of options. Use this parameter to fine-tune and optimize specific neural network framework. (e.g. NumThreads:N to set the number of threads in TensorFlow-Lite) You may set NULL if it's not required. See NNStreamer documentation (https://nnstreamer.github.io/gst/nnstreamer/tensor_filter/README.html) for the details.
- Returns:
0
on success. Otherwise a negative error value.
- Return values:
-
ML_ERROR_NONE Successful. ML_ERROR_NOT_SUPPORTED Not supported. ML_ERROR_PERMISSION_DENIED The application does not have the privilege to access to the media storage or external storage. ML_ERROR_INVALID_PARAMETER Fail. The parameter is invalid. ML_ERROR_STREAMS_PIPE Failed to start the pipeline. ML_ERROR_OUT_OF_MEMORY Failed to allocate required memory.
int ml_single_open_with_option | ( | ml_single_h * | single, |
const ml_option_h | option | ||
) |
Makes a single instance with given ml-option.
- Since :
- 7.0
- Remarks:
- http://tizen.org/privilege/mediastorage is needed if option is relevant to media storage.
- http://tizen.org/privilege/externalstorage is needed if option is relevant to external storage.
- Parameters:
-
[out] single This is the model handle opened. Users are required to close the given instance with ml_single_close(). [in] option The handle of ml-option.
- Returns:
0
on success. Otherwise a negative error value.
- Return values:
-
ML_ERROR_NONE Successful. ML_ERROR_NOT_SUPPORTED Not supported. ML_ERROR_PERMISSION_DENIED The application does not have the privilege to access to the media storage or external storage. ML_ERROR_INVALID_PARAMETER Fail. The parameter is invalid. ML_ERROR_STREAMS_PIPE Failed to start the pipeline. ML_ERROR_OUT_OF_MEMORY Failed to allocate required memory.
int ml_single_set_input_info | ( | ml_single_h | single, |
const ml_tensors_info_h | info | ||
) |
Sets the information (tensor dimension, type, name and so on) of required input data for the given model.
Note that a model/framework may not support changing the information. Use ml_single_get_input_info() and ml_single_get_output_info() instead for this framework.
- Since :
- 6.0
- Parameters:
-
[in] single The model handle. [in] info The handle of input 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 Fail. The parameter is invalid.
int ml_single_set_property | ( | ml_single_h | single, |
const char * | name, | ||
const char * | value | ||
) |
Sets the property value for the given model.
Note that a model/framework may not support changing the property after opening the model.
- Since :
- 6.0
- Parameters:
-
[in] single The model handle. [in] name The property name. [in] value The property value.
- 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_single_set_timeout | ( | ml_single_h | single, |
unsigned int | timeout | ||
) |
Sets the maximum amount of time to wait for an output, in milliseconds.
- Since :
- 5.5
- Parameters:
-
[in] single The model handle. [in] timeout The time to wait for an output.
- 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.