AidLite Python API

Model Data Type class DataType

For the AidLite SDK, different models from various frameworks are processed, each with its own input and output data types. In the usage flow described earlier, setting the input and output data types for the model requires this data type.

Member Variable	Type	Value	Description
TYPE_DEFAULT	int	0	Invalid DataType
TYPE_UINT8	int	1	Unsigned byte data
TYPE_INT8	int	2	Byte data
TYPE_UINT32	int	3	Unsigned int32 data
TYPE_FLOAT32	int	4	Float data
TYPE_INT32	int	5	Int32 data
TYPE_INT64	int	6	Int64 data

Inference Implementation Type class ImplementType (Deprecated)

💡 Note

Important: Starting from V2.0.7, this type is deprecated.

Member Variable	Type	Value	Description
TYPE_DEFAULT	int	0	Invalid ImplementType
TYPE_MMKV	int	1	Implemented via MMKV
TYPE_FAST	int	2	Implemented via IPC backend
TYPE_LOCAL	int	3	Implemented via local backend

Model Framework Type class FrameworkType

As mentioned earlier, the AidLite SDK integrates multiple deep learning inference frameworks. Therefore, in the usage flow, it is necessary to specify which framework’s model is being used, requiring this framework type.

Member Variable	Type	Value	Description
TYPE_DEFAULT	int	0	Invalid FrameworkType
TYPE_SNPE	int	1	SNPE 1.x (DLC) model type
TYPE_TFLite	int	2	TFLite model type
TYPE_RKNN	int	3	RKNN model type
TYPE_QNN	int	4	QNN model type
TYPE_SNPE2	int	5	SNPE 2.x (DLC) model type
TYPE_NCNN	int	6	NCNN model type
TYPE_MNN	int	7	MNN model type
TYPE_TNN	int	8	TNN model type
TYPE_PADDLE	int	9	Paddle model type

Inference Acceleration Hardware Type class AccelerateType

For each deep learning inference framework, it may support running on different acceleration hardware units (e.g., SNPE models running on Qualcomm DSP units, RKNN models running on Rockchip NPU units). Thus, in the usage flow, it is necessary to specify which computing unit the model is expected to run on, requiring this acceleration hardware type.

Member Variable	Type	Value	Description
TYPE_DEFAULT	int	0	Invalid AccelerateType
TYPE_CPU	int	1	CPU general acceleration unit
TYPE_GPU	int	2	GPU general acceleration unit
TYPE_DSP	int	3	Qualcomm DSP acceleration unit
TYPE_NPU	int	4	NPU general acceleration unit

Log Level class LogLevel

The AidLite SDK provides an interface for setting logs (introduced later), which requires specifying the log level to be used, necessitating this log level type.

Member Variable	Type	Value	Description
INFO	int	0	Information
WARNING	int	1	Warning
ERROR	int	2	Error
FATAL	int	3	Fatal error

Model Class class Model

As mentioned earlier, before creating an inference interpreter, detailed parameters related to the specific model need to be set. The Model class is primarily used to record the model’s file information, structural information, and related content during model execution.

Create Model Instance create_instance()

To set detailed model information, a model instance object is required first. This function is used to create a Model instance object.

API	create_instance
Description	Constructs a Model instance object by passing the model file’s path and name.
Parameters	model_path: Path and name of the model file.
Return Value	Normal: Model instance object.
	Exception: None.

# Create a model object using the inceptionv3_float32.dlc file in the current path; report an error if the return value is None
model = aidlite.Model.create_instance(model_path=r"./inceptionv3.dlc")
if model is None:
    print("Create model failed !")
    return False

Set Model Properties set_model_properties()

After successfully creating the model instance object, it is necessary to set the input and output data types and the shape information of the input and output tensors.

API	set_model_properties
Description	Sets the model’s properties, including input and output data shapes and data types.
Parameters	input_shapes: Array of input tensor shapes, in a 2D array structure.
	input_data_type: Input tensor data type, an enumeration of DataType.
	output_shapes: Array of output tensor shapes, in a 2D array structure.
	output_data_type: Output tensor data type, an enumeration of DataType.
Return Value	The model file path string corresponding to the Model object.

# Use the previously defined DataType; input and output shapes are 2D arrays
input_shapes=[[1,640,640,3]]
output_shapes=[[1,10,10,255], [1,20,20,255], [1,40,40,255]]
model.set_model_properties(input_shapes=input_shapes, input_data_type=aidlite.DataType.TYPE_FLOAT32, output_shapes=output_shapes, output_data_type=aidlite.DataType.TYPE_FLOAT32)

Get Model Path get_model_absolute_path()

API	get_model_absolute_path
Description	Retrieves the existing path of the model file.
Parameters	None
Return Value	The model file path string corresponding to the Model object.

model_path = model.get_model_absolute_path()

Get Model Type get_model_type()

API	get_model_type
Description	Retrieves the model type identifier, such as DLC, RKNN, etc.
Parameters	None
Return Value	The model file path string corresponding to the Model object.

model_type = model.get_model_type()

Configuration Class class Config

As mentioned earlier, before creating an inference interpreter, in addition to setting specific Model information, some configuration information for inference is also required. The Config class is used to record configuration options that need to be preset and will be used during runtime.

Create Config Instance create_instance()

To set runtime configuration information, a configuration instance object is required first. This function is used to create a Config instance object.

API	create_instance
Description	Constructs a Config class instance object.
Parameters	snpe_out_names: List of model output node names for FAST (optional).
	number_of_threads: Number of threads, effective if greater than 0 (optional).
	is_quantify_model: Whether it is a quantized model, 1 for quantized model for FAST (optional).
	fast_timeout: Interface timeout in milliseconds, effective if greater than 0 for FAST (optional).
	accelerate_type: Type of acceleration hardware (optional).
	framework_type: Type of underlying deep learning framework (optional).
Return Value	Normal: Config instance object.
	Exception: None.

# Create a config instance object; report an error if the return value is None
config = aidlite.Config.create_instance()
if config is None:
    print("Create config failed !")
    return False

Member Variables List

The Config object is used to set runtime configuration information, including the following parameters:

Member Variable	accelerate_type
Type	class AccelerateType
Default Value	AccelerateType.TYPE_CPU
Description	Type of acceleration hardware.

---

💡 Note

Important: Starting from V2.0.7, this type is deprecated.

Member Variable	implement_type (Deprecated)
Type	class ImplementType
Default Value	ImplementType.TYPE_LOCAL
Description	Distinguishes the underlying implementation method.

---

Member Variable	framework_type
Type	class FrameworkType
Default Value	FrameworkType.TYPE_DEFAULT
Description	Type of underlying inference framework.

---

Member Variable	number_of_threads
Type	int
Default Value	-1
Description	Number of threads, effective if greater than 0.

---

Member Variable	SNPE_out_names
Type	list
Default Value	None
Description	List of model output node names for FAST.

---

Member Variable	is_quantify_model
Type	int
Default Value	0
Description	Whether it is a quantized model, 1 for quantized model for FAST.

---

Member Variable	fast_timeout
Type	int
Default Value	-1
Description	Interface timeout in milliseconds, effective if greater than 0 for FAST.

---

config.framework_type = aidlite.FrameworkType.TYPE_SNPE
config.accelerate_type = aidlite.AccelerateType.TYPE_DSP
config.is_quantify_model = 1
config.SNPE_out_names = ["InceptionV3/Softmax"]
config.fast_timeout = 1000

Context Class class Context

Used to store the runtime context involved in the execution process, including the Model object and Config object. Additional runtime data may be extended in the future.

Constructor Context()

API	Context
Description	Constructs a Context instance object.
Parameters	model: Model instance object.
	config: Config instance object.
Return Value	Normal: Context instance object.
	Exception: None.

context = aidlite.Context(model=model, config=config)

Get Model Member Variable get_model()

API	get_model
Description	Retrieves the Model object managed by the context.
Parameters	None
Return Value	Model object.

model = context.get_model()

Get Config Member Variable get_config()

API	get_config
Description	Retrieves the Config object managed by the context.
Parameters	None
Return Value	Config object.

config = context.get_config()

Interpreter Class class Interpreter

The Interpreter type object instance is the main entity for performing inference operations, used to execute the specific inference process. As mentioned in the inference flow, after creating the interpreter object, all operations are performed based on the interpreter object, making it the absolute core of the AidLite SDK.

Create Interpreter Instance create_instance()

To perform inference-related operations, an inference interpreter is essential. This function is used to construct an instance of the inference interpreter.

API	create_instance
Description	Constructs an Interpreter type object using the data managed by the Context object.
Parameters	None
Return Value	Normal: Interpreter instance object.
	Exception: None.

# Create an interpreter object; report an error if the return value is None
interpreter = aidlite.Interpreter.create_instance()
if interpreter is None:
    print("Create Interpreter failed !")
    return False

Initialize init()

After creating the interpreter object, some initialization operations (e.g., environment checks, resource construction) need to be performed.

API	init
Description	Constructs an Interpreter type object using the data managed by the Context object.
Parameters	context: Context object instance, which manages Model and Config objects containing model data, configuration data, etc.
Return Value	Normal: 0.
	Exception: Non-zero.

# Initialize the interpreter; report an error if the return value is non-zero
result = aidlite.interpreter.init()
if result != 0:
    print("sample : interpreter->init() failed !")
    return False

Load Model load_model()

After the interpreter object completes initialization, the required model file can be loaded for the interpreter, completing the model loading process. Subsequent inference processes will use the loaded model resources.

API	load_model
Description	Completes model loading operations. Since the model file path is already set in the Model object, the model loading operation can be executed directly.
Parameters	None
Return Value	Normal: 0.
	Exception: Non-zero.

# Load the model for the interpreter; report an error if the return value is non-zero
result = interpreter.load_model()
if result != 0:
    print("sample : interpreter->load_model() failed !")
    return False

Set Input Data set_input_tensor()

As mentioned in the flow introduction, before setting input data, different preprocessing operations are required for different models to adapt to the specific model.

API	set_input_tensor
Description	Sets the input data for inference.
Parameters	in_tensor_idx: Index value of the input tensor, type int.
	input_data: Binary data of the input tensor, type depends on the actual case.
Return Value	Normal: 0.
	Exception: Non-zero.

# Set the input data for inference; report an error if the return value is non-zero
result = interpreter.set_input_tensor(in_tensor_idx=1, input_data=obj)
if result != 0:
    print("interpreter->set_input_tensor() failed !")
    return False

Execute Inference invoke()

As mentioned in the flow introduction, after setting the input data, the next step is to execute the inference process on the input data.

API	invoke
Description	Executes the inference computation process.
Parameters	None
Return Value	Normal: 0.
	Exception: Non-zero.

# Execute the inference operation; report an error if the return value is non-zero
result = interpreter.invoke()
if result != 0:
    print("sample : interpreter->invoke() failed !")
    return False

Get Output Data get_output_tensor()

After inference is complete, the resulting data needs to be retrieved. As mentioned in the flow introduction, after retrieving the result data, it can be processed to determine if the results are correct.

API	get_output_tensor
Description	Retrieves the inference result data after successful inference.
Parameters	out_tensor_idx: Index value of the output tensor.
	output_type: Output data type, optional, defaults to aidlite.DataType.TYPE_FLOAT32.
Return Value	Normal: Float result data.
	Exception: None.

# Retrieve the inference result data; report an error if the return value is None
out_data = interpreter.get_output_tensor(out_tensor_idx=1, output_type=aidlite.DataType.TYPE_INT32)
if out_data is None:
    print("interpreter->get_output_tensor() failed !")
    return False

Resource Release destroy()

As mentioned earlier, the interpreter object requires initialization (init) and model loading operations. Correspondingly, the interpreter also needs to perform release operations to destroy previously created resources.

API	destroy
Description	Performs necessary release operations.
Parameters	None
Return Value	Normal: 0.
	Exception: Non-zero.

# Execute the interpreter release process; report an error if the return value is non-zero
result = interpreter.destroy()
if result != 0:
    print("sample : interpreter->destroy() failed !")
    return False

Interpreter Builder Class class InterpreterBuilder

A unified creation function for Interpreter objects, used to create the required interpreter objects through this class.

Build Interpreter build_interpreter_from_path()

Builds an inference interpreter object, allowing different parameters to be provided. The simplest approach is to provide only the model file’s path and name.

API	build_interpreter_from_path
Description	Directly creates the corresponding interpreter object using the model file’s path and name. All related parameters use default values.
Parameters	path: Path and name of the model file.
Return Value	Normal: Interpreter object instance.
	Exception: None.

# Build an interpreter by passing the model file path; report an error if the return value is None
interpreter = aidlite.InterpreterBuilder.build_interpreter_from_path(path=r"./640.dlc")
if interpreter is None:
    print("Create Interpreter failed !")
    return False

Build Interpreter build_interpreter_from_model()

Builds an inference interpreter object. In addition to providing the model file path, a Model object can also be provided, allowing not only the model file path but also the input and output data types and shape information to be set.

API	build_interpreter_from_model
Description	Creates the corresponding interpreter object by passing a Model object. All Config-related parameters use default values.
Parameters	model: Model type object containing model-related data.
Return Value	Normal: Interpreter object instance.
	Exception: None.

# Build an interpreter by passing a Model object; report an error if the return value is None
interpreter = aidlite.InterpreterBuilder.build_interpreter_from_model(model=model)
if interpreter is None:
    print("Create Interpreter failed !")
    return False

Build Interpreter build_interpreter_from_model_and_config()

Builds an inference interpreter object. In addition to the methods mentioned above, both a Model object and a Config object can be provided, allowing not only model-related information but also additional runtime configuration parameters to be specified.

API	build_interpreter_from_model_and_config
Description	Creates the corresponding interpreter object by passing Model and Config objects.
Parameters	model: Model type object containing model-related data.
	config: Config type object containing configuration parameters.
Return Value	Normal: Interpreter object instance.
	Exception: None.

# Build an interpreter by passing Model and Config objects; report an error if the return value is None
interpreter = aidlite.InterpreterBuilder.build_interpreter_from_model_and_config(model=model, config=config)
if interpreter is None:
    print("Create Interpreter failed !")
    return False

Other Methods

Get SDK Version Information get_library_version()

API	get_library_version
Description	Retrieves version-related information for the current AidLite-SDK.
Parameters	None
Return Value	Version information string for the current AidLite-SDK.

Get Python SDK Version Information get_py_library_version()

API	get_py_library_version
Description	Retrieves version-related information for the current Py-Aidlite-SDK.
Parameters	None
Return Value	Version information string for the current Py-Aidlite-SDK.

Set Log Level set_log_level()

API	set_log_level
Description	Sets the minimum log level, outputting log data greater than or equal to this level. By default, logs at WARNING and above are printed.
Parameters	log_level: Value of type LogLevel.
Return Value	Default return value is 0.

Log Output to Standard Terminal log_to_stderr()

API	log_to_stderr
Description	Sets log information to be output to the standard error terminal.
Parameters	None
Return Value	Default return value is 0.

Log Output to Text File log_to_file()

API	log_to_file
Description	Sets log information to be output to a specified text file.
Parameters	path_and_prefix: Path and prefix name for the log file.
	also_to_stderr: Indicates whether to also output logs to stderr terminal, default is False.
Return Value	Normal: 0.
	Exception: Non-zero.

Get Latest Log Information last_log_msg()

API	last_log_msg
Description	Retrieves the latest log information for a specific log level, typically the latest error information.
Parameters	log_level: Value of type LogLevel.
Return Value	Latest log information.