harmony 鸿蒙@ohos.ai.mindSporeLite (On-device AI Framework)

2025-06-12
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@ohos.ai.mindSporeLite (On-device AI Framework)

MindSpore Lite is a lightweight and high-performance on-device AI engine that provides standard model inference and training APIs and built-in universal high-performance operator libraries. It supports Neural Network Runtime Kit for a higher inference efficiency, empowering intelligent applications in all scenarios.

This topic describes the model inference and training capabilities supported by the MindSpore Lite AI engine.

NOTE

The initial APIs of this module are supported since API version 10. Newly added APIs will be marked with a superscript to indicate their earliest API version. Unless otherwise stated, the MindSpore model is used in the sample code.

The APIs of this module can be used only in the stage model.

Modules to Import

import { mindSporeLite } from '@kit.MindSporeLiteKit';

mindSporeLite.loadModelFromFile

loadModelFromFile(model: string, callback: Callback<Model>): void

Loads the input model from the full path for model inference. This API uses an asynchronous callback to return the result.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
model	string	Yes	Complete path of the input model.
callback	Callback<Model>	Yes	Callback used to return the result, which is a Model object.

Example

let modelFile : string = '/path/to/xxx.ms';
mindSporeLite.loadModelFromFile(modelFile, (mindSporeLiteModel : mindSporeLite.Model) => {
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  console.info(modelInputs[0].name);
})

mindSporeLite.loadModelFromFile

loadModelFromFile(model: string, context: Context, callback: Callback<Model>): void

Loads the input model from the full path for model inference. This API uses an asynchronous callback to return the result.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
model	string	Yes	Complete path of the input model.
context	Context	Yes	Configuration information of the running environment.
callback	Callback<Model>	Yes	Callback used to return the result, which is a Model object.

Example

let context: mindSporeLite.Context = {};
context.target = ['cpu'];
let modelFile : string = '/path/to/xxx.ms';
mindSporeLite.loadModelFromFile(modelFile, context, (mindSporeLiteModel : mindSporeLite.Model) => {
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  console.info(modelInputs[0].name);
})

mindSporeLite.loadModelFromFile

loadModelFromFile(model: string, context?: Context): Promise<Model>

Loads the input model from the full path for model inference. This API uses a promise to return the result.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
model	string	Yes	Complete path of the input model.
context	Context	No	Configuration information of the running environment. By default, CpuDevice is used for initialization.

Return value

Type	Description
Promise<Model>	Promise used to return the result, which is a Model object.

Example

let modelFile = '/path/to/xxx.ms';
mindSporeLite.loadModelFromFile(modelFile).then((mindSporeLiteModel : mindSporeLite.Model) => {
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  console.info(modelInputs[0].name);
})

mindSporeLite.loadModelFromBuffer

loadModelFromBuffer(model: ArrayBuffer, callback: Callback<Model>): void

Loads the input model from the memory for inference. This API uses an asynchronous callback to return the result.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
model	ArrayBuffer	Yes	Memory that contains the input model.
callback	Callback<Model>	Yes	Callback used to return the result, which is a Model object.

Example

import { mindSporeLite } from '@kit.MindSporeLiteKit';
import { common } from '@kit.AbilityKit';
import { UIContext } from '@kit.ArkUI';

let modelFile = 'xxx.ms';
let globalContext = new UIContext().getHostContext() as common.UIAbilityContext;
globalContext.getApplicationContext().resourceManager.getRawFileContent(modelFile).then((buffer: Uint8Array) => {
  let modelBuffer = buffer.buffer;
  mindSporeLite.loadModelFromBuffer(modelBuffer, (mindSporeLiteModel: mindSporeLite.Model) => {
    let modelInputs: mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
    console.info('MS_LITE_LOG: ' + modelInputs[0].name);
  })
})

mindSporeLite.loadModelFromBuffer

loadModelFromBuffer(model: ArrayBuffer, context: Context, callback: Callback<Model>): void

Loads the input model from the memory for inference. This API uses an asynchronous callback to return the result.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
model	ArrayBuffer	Yes	Memory that contains the input model.
context	Context	Yes	Configuration information of the running environment.
callback	Callback<Model>	Yes	Callback used to return the result, which is a Model object.

Example

import { mindSporeLite } from '@kit.MindSporeLiteKit';
import { common } from '@kit.AbilityKit';
import { UIContext } from '@kit.ArkUI';

let modelFile = 'xxx.ms';
let globalContext = new UIContext().getHostContext() as common.UIAbilityContext;
globalContext.getApplicationContext().resourceManager.getRawFileContent(modelFile).then((buffer: Uint8Array) => {
  let modelBuffer = buffer.buffer;
  let context: mindSporeLite.Context = {};
  context.target = ['cpu'];
  mindSporeLite.loadModelFromBuffer(modelBuffer, context, (mindSporeLiteModel: mindSporeLite.Model) => {
    let modelInputs: mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
    console.info('MS_LITE_LOG: ' + modelInputs[0].name);
  })
})

mindSporeLite.loadModelFromBuffer

loadModelFromBuffer(model: ArrayBuffer, context?: Context): Promise<Model>

Loads the input model from the memory for inference. This API uses a promise to return the result.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
model	ArrayBuffer	Yes	Memory that contains the input model.
context	Context	No	Configuration information of the running environment. By default, CpuDevice is used for initialization.

Return value

Type	Description
Promise<Model>	Promise used to return the result, which is a Model object.

Example

import { mindSporeLite } from '@kit.MindSporeLiteKit';
import { common } from '@kit.AbilityKit';
import { UIContext } from '@kit.ArkUI';

let modelFile = 'xxx.ms';
let globalContext = new UIContext().getHostContext() as common.UIAbilityContext;
globalContext.getApplicationContext().resourceManager.getRawFileContent(modelFile).then((buffer: Uint8Array) => {
  let modelBuffer = buffer.buffer;
  mindSporeLite.loadModelFromBuffer(modelBuffer).then((mindSporeLiteModel: mindSporeLite.Model) => {
    let modelInputs: mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
    console.info('MS_LITE_LOG: ' + modelInputs[0].name);
  })
})

mindSporeLite.loadModelFromFd

loadModelFromFd(model: number, callback: Callback<Model>): void

Loads the input model based on the specified file descriptor for inference. This API uses an asynchronous callback to return the result.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
model	number	Yes	File descriptor of the input model.
callback	Callback<Model>	Yes	Callback used to return the result, which is a Model object.

Example

import { fileIo } from '@kit.CoreFileKit';
let modelFile = '/path/to/xxx.ms';
let file = fileIo.openSync(modelFile, fileIo.OpenMode.READ_ONLY);
mindSporeLite.loadModelFromFd(file.fd, (mindSporeLiteModel : mindSporeLite.Model) => {
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  console.info(modelInputs[0].name);
})

mindSporeLite.loadModelFromFd

loadModelFromFd(model: number, context: Context, callback: Callback<Model>): void

Loads the input model based on the specified file descriptor for inference. This API uses an asynchronous callback to return the result.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
model	number	Yes	File descriptor of the input model.
context	Context	Yes	Configuration information of the running environment.
callback	Callback<Model>	Yes	Callback used to return the result, which is a Model object.

Example

import { fileIo } from '@kit.CoreFileKit';
let modelFile = '/path/to/xxx.ms';
let context : mindSporeLite.Context = {};
context.target = ['cpu'];
let file = fileIo.openSync(modelFile, fileIo.OpenMode.READ_ONLY);
mindSporeLite.loadModelFromFd(file.fd, context, (mindSporeLiteModel : mindSporeLite.Model) => {
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  console.info(modelInputs[0].name);
})

mindSporeLite.loadModelFromFd

loadModelFromFd(model: number, context?: Context): Promise<Model>

Loads the input model based on the specified file descriptor for inference. This API uses a promise to return the result.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
model	number	Yes	File descriptor of the input model.
context	Context	No	Configuration information of the running environment. By default, CpuDevice is used for initialization.

Return value

Type	Description
Promise<Model>	Promise used to return the result, which is a Model object.

Example

import { fileIo } from '@kit.CoreFileKit';
let modelFile = '/path/to/xxx.ms';
let file = fileIo.openSync(modelFile, fileIo.OpenMode.READ_ONLY);
mindSporeLite.loadModelFromFd(file.fd).then((mindSporeLiteModel: mindSporeLite.Model) => {
  let modelInputs: mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  console.info(modelInputs[0].name);
})

mindSporeLite.loadTrainModelFromFile¹²⁺

loadTrainModelFromFile(model: string, trainCfg?: TrainCfg, context?: Context): Promise<Model>

Loads the training model file based on the specified path. This API uses a promise to return the result.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
model	string	Yes	Complete path of the input model.
trainCfg	TrainCfg	No	Model training configuration. The default value is an array of the default values of attributes in TrainCfg.
context	Context	No	Configuration information of the running environment. By default, CpuDevice is used for initialization.

Return value

Type	Description
Promise<Model>	Promise used to return the result, which is a Model object.

Example

let modelFile = '/path/to/xxx.ms';
mindSporeLite.loadTrainModelFromFile(modelFile).then((mindSporeLiteModel : mindSporeLite.Model) => {
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  console.info(modelInputs[0].name);
})

mindSporeLite.loadTrainModelFromBuffer¹²⁺

loadTrainModelFromBuffer(model: ArrayBuffer, trainCfg?: TrainCfg, context?: Context): Promise<Model>

Loads a training model from the memory buffer. This API uses a promise to return the result.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
model	ArrayBuffer	Yes	Memory accommodating the training model.
trainCfg	TrainCfg	No	Model training configuration. The default value is an array of the default values of attributes in TrainCfg.
context	Context	No	Configuration information of the running environment. By default, CpuDevice is used for initialization.

Return value

Type	Description
Promise<Model>	Promise used to return the result, which is a Model object.

Example

import { mindSporeLite } from '@kit.MindSporeLiteKit';
import { common } from '@kit.AbilityKit';
import { UIContext } from '@kit.ArkUI';

let modelFile = 'xxx.ms';
let globalContext = new UIContext().getHostContext() as common.UIAbilityContext;
globalContext.getApplicationContext().resourceManager.getRawFileContent(modelFile).then((buffer: Uint8Array) => {
  let modelBuffer = buffer.buffer;
  mindSporeLite.loadTrainModelFromBuffer(modelBuffer).then((mindSporeLiteModel: mindSporeLite.Model) => {
    console.info("MSLITE trainMode: ", mindSporeLiteModel.trainMode);
  })
})

mindSporeLite.loadTrainModelFromFd¹²⁺

loadTrainModelFromFd(model: number, trainCfg?: TrainCfg, context?: Context): Promise<Model>

Loads the training model file from the file descriptor. This API uses a promise to return the result.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
model	number	Yes	File descriptor of the training model.
trainCfg	TrainCfg	No	Model training configuration. The default value is an array of the default values of attributes in TrainCfg.
context	Context	No	Configuration information of the running environment. By default, CpuDevice is used for initialization.

Return value

Type	Description
Promise<Model>	Promise used to return the result, which is a Model object.

Example

import { fileIo } from '@kit.CoreFileKit';
let modelFile = '/path/to/xxx.ms';
let file = fileIo.openSync(modelFile, fileIo.OpenMode.READ_ONLY);
mindSporeLite.loadTrainModelFromFd(file.fd).then((mindSporeLiteModel: mindSporeLite.Model) => {
  console.info("MSLITE trainMode: ", mindSporeLiteModel.trainMode);
});

mindSporeLite.getAllNNRTDeviceDescriptions¹²⁺

getAllNNRTDeviceDescriptions() : NNRTDeviceDescription[]

Obtains all device descriptions in NNRt.

System capability: SystemCapability.AI.MindSporeLite

Return value

Type	Description
NNRTDeviceDescription[]	NNRt device description array.

Example

let allDevices = mindSporeLite.getAllNNRTDeviceDescriptions();
if (allDevices == null) {
  console.error('MS_LITE_LOG: getAllNNRTDeviceDescriptions is NULL.');
}

Context

Defines the configuration information of the running environment.

Attributes

System capability: SystemCapability.AI.MindSporeLite

Name	Type	Read Only	Optional	Description
target	string[]	No	Yes	Target backend. The value can be cpu or nnrt. The default value is cpu.
cpu	CpuDevice	No	Yes	CPU backend device option. Set this parameter set only when target is set to cpu. The default value is an array of the default values of attributes in CpuDevice.
nnrt	NNRTDevice	No	Yes	NNRt backend device option. Set this parameter set only when target is set to nnrt. The default value is an array of the default values of attributes in NNRTDevice.

Example

let context: mindSporeLite.Context = {};
context.target = ['cpu','nnrt'];

CpuDevice

Defines the CPU backend device option.

Attributes

System capability: SystemCapability.AI.MindSporeLite

Name	Type	Read Only	Optional	Description
threadNum	number	No	Yes	Number of runtime threads. The default value is 2.
threadAffinityMode	ThreadAffinityMode	No	Yes	Affinity mode for binding runtime threads to CPU cores. The default value is mindSporeLite.ThreadAffinityMode.NO_AFFINITIES.
threadAffinityCoreList	number[]	No	Yes	List of CPU cores bound to runtime threads. Set this parameter only when threadAffinityMode is set. If threadAffinityMode is set to mindSporeLite.ThreadAffinityMode.NO_AFFINITIES, this parameter is empty. The number in the list indicates the SN of the CPU core. The default value is [].
precisionMode	string	No	Yes	Whether to enable the Float16 inference mode. The value preferred_fp16 means to enable half-precision inference and the default value enforce_fp32 means to disable half-precision inference. Other settings are not supported.

Float16 inference mode: a mode that uses half-precision inference. Float16 uses 16 bits to represent a number and therefore it is also called half-precision.

Example

let context: mindSporeLite.Context = {};
context.cpu = {};
context.target = ['cpu'];
context.cpu.threadNum = 2;
context.cpu.threadAffinityMode = 0;
context.cpu.precisionMode = 'preferred_fp16';
context.cpu.threadAffinityCoreList = [0, 1, 2];

ThreadAffinityMode

Specifies the affinity mode for binding runtime threads to CPU cores.

System capability: SystemCapability.AI.MindSporeLite

Name	Value	Description
NO_AFFINITIES	0	No affinities.
BIG_CORES_FIRST	1	Big cores first.
LITTLE_CORES_FIRST	2	Medium cores first.

NNRTDevice

Represents an NNRt device. Neural Network Runtime (NNRt) is a bridge that connects the upper-layer AI inference framework to the bottom-layer acceleration chip to implement cross-chip inference and computing of AI models. An NNRt backend can be configured for MindSpore Lite.

Attributes

System capability: SystemCapability.AI.MindSporeLite

Name	Type	Read Only	Optional	Description
deviceID¹²⁺	bigint	No	Yes	NNRt device ID. The default value is 0.
performanceMode¹²⁺	PerformanceMode	No	Yes	NNRt device performance mode. The default value is PERFORMANCE_NONE.
priority¹²⁺	Priority	No	Yes	NNRt inference task priority. The default value is PRIORITY_MEDIUM.
extensions¹²⁺	Extension[]	No	Yes	Extended NNRt device configuration. This parameter is left empty by default.

PerformanceMode¹²⁺

Enumerates NNRt device performance modes.

System capability: SystemCapability.AI.MindSporeLite

Name	Value	Description
PERFORMANCE_NONE	0	No special settings.
PERFORMANCE_LOW	1	Low power consumption.
PERFORMANCE_MEDIUM	2	Power consumption and performance balancing.
PERFORMANCE_HIGH	3	High performance.
PERFORMANCE_EXTREME	4	Ultimate performance.

Priority¹²⁺

Enumerates NNRt inference task priorities.

System capability: SystemCapability.AI.MindSporeLite

Name	Value	Description
PRIORITY_NONE	0	No priority preference.
PRIORITY_LOW	1	Low priority.
PRIORITY_MEDIUM	2	Medium priority.
PRIORITY_HIGH	3	High priority.

Extension¹²⁺

Defines the extended NNRt device configuration.

Attributes

System capability: SystemCapability.AI.MindSporeLite

Name	Type	Read Only	Optional	Description
name¹²⁺	string	No	No	Configuration name.
value¹²⁺	ArrayBuffer	No	No	Memory accommodating the extended configuration.

NNRTDeviceDescription¹²⁺

Defines NNRt device information, including the device ID and device name.

System capability: SystemCapability.AI.MindSporeLite

deviceID

deviceID() : bigint

Obtains the NNRt device ID.

System capability: SystemCapability.AI.MindSporeLite

Return value

Type	Description
bigint	NNRt device ID.

Example

let allDevices = mindSporeLite.getAllNNRTDeviceDescriptions();
if (allDevices == null) {
  console.error('getAllNNRTDeviceDescriptions is NULL.');
}
let context: mindSporeLite.Context = {};
context.target = ["nnrt"];
context.nnrt = {};
for (let i: number = 0; i < allDevices.length; i++) {
  console.info(allDevices[i].deviceID().toString());
}

deviceType

deviceType() : NNRTDeviceType

Obtains the device model.

System capability: SystemCapability.AI.MindSporeLite

Return value

Type	Description
NNRTDeviceType	NNRt device type.

Example

let allDevices = mindSporeLite.getAllNNRTDeviceDescriptions();
if (allDevices == null) {
  console.error('getAllNNRTDeviceDescriptions is NULL.');
}
let context: mindSporeLite.Context = {};
context.target = ["nnrt"];
context.nnrt = {};
for (let i: number = 0; i < allDevices.length; i++) {
  console.info(allDevices[i].deviceType().toString());
}

deviceName

deviceName() : string

Obtains the NNRt device name.

System capability: SystemCapability.AI.MindSporeLite

Return value

Type	Description
string	NNRt device name.

Example

let allDevices = mindSporeLite.getAllNNRTDeviceDescriptions();
if (allDevices == null) {
  console.error('getAllNNRTDeviceDescriptions is NULL.');
}
let context: mindSporeLite.Context = {};
context.target = ["nnrt"];
context.nnrt = {};
for (let i: number = 0; i < allDevices.length; i++) {
  console.info(allDevices[i].deviceName().toString());
}

NNRTDeviceType¹²⁺

Enumerates NNRt device types.

System capability: SystemCapability.AI.MindSporeLite

Name	Value	Description
NNRTDEVICE_OTHERS	0	Others (any device type except the following three types).
NNRTDEVICE_CPU	1	CPU.
NNRTDEVICE_GPU	2	GPU.
NNRTDEVICE_ACCELERATOR	3	Specific acceleration device.

TrainCfg¹²⁺

Defines the configuration for on-device training.

Attributes

System capability: SystemCapability.AI.MindSporeLite

Name	Type	Read Only	Optional	Description
lossName¹²⁺	string[]	No	Yes	List of loss functions. The default value is [“loss_fct”, “_loss_fn”, “SigmoidCrossEntropy”].
optimizationLevel¹²⁺	OptimizationLevel	No	Yes	Network optimization level for on-device training. The default value is O0.

Example

let cfg: mindSporeLite.TrainCfg = {};
cfg.lossName = ["loss_fct", "_loss_fn", "SigmoidCrossEntropy"];
cfg.optimizationLevel = mindSporeLite.OptimizationLevel.O0;

OptimizationLevel¹²⁺

Enumerates network optimization levels for on-device training.

System capability: SystemCapability.AI.MindSporeLite

Name	Value	Description
O0	0	No optimization level.
O2	2	Converts the precision type of the network to float16 and keeps the precision type of the batch normalization layer and loss function as float32.
O3	3	Converts the precision type of the network (including the batch normalization layer) to float16.
AUTO	4	Selects an optimization level based on the device.

QuantizationType¹²⁺

Enumerates quantization types.

System capability: SystemCapability.AI.MindSporeLite

Name	Value	Description
NO_QUANT	0	No quantification.
WEIGHT_QUANT	1	Weight quantization.
FULL_QUANT	2	Full quantization.

Model

Represents a Model instance, with properties and APIs defined.

In the following sample code, you first need to use loadModelFromFile(), loadModelFromBuffer(), or loadModelFromFd() to obtain a Model instance before calling related APIs.

Attributes

System capability: SystemCapability.AI.MindSporeLite

Name	Type	Read Only	Optional	Description
learningRate¹²⁺	number	No	Yes	Learning rate of a training model. The default value is read from the loaded model.
trainMode¹²⁺	boolean	No	Yes	Training mode. The value true indicates the training mode, and the value false indicates the non-training mode. The default value is true for a training model and false for an inference model.

getInputs

getInputs(): MSTensor[]

Obtains the model input for inference.

System capability: SystemCapability.AI.MindSporeLite

Return value

Type	Description
MSTensor[]	MSTensor object.

Example

let modelFile = '/path/to/xxx.ms';
mindSporeLite.loadModelFromFile(modelFile).then((mindSporeLiteModel : mindSporeLite.Model) => {
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  console.info(modelInputs[0].name);
})

predict

predict(inputs: MSTensor[], callback: Callback<MSTensor[]>): void

Executes the inference model. This API uses an asynchronous callback to return the result. Ensure that the model object is not reclaimed when being invoked.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
inputs	MSTensor[]	Yes	List of input models.
callback	Callback<MSTensor[]>	Yes	Callback used to return the result, which is a list of MSTensor objects.

Example

import { mindSporeLite } from '@kit.MindSporeLiteKit';
import { common } from '@kit.AbilityKit';
import { UIContext } from '@kit.ArkUI';

let inputName = 'input_data.bin';
let globalContext = new UIContext().getHostContext() as common.UIAbilityContext;
globalContext.getApplicationContext().resourceManager.getRawFileContent(inputName).then(async (buffer : Uint8Array) => {
  let inputBuffer = buffer.buffer;
  let modelFile : string = '/path/to/xxx.ms';
  let mindSporeLiteModel : mindSporeLite.Model = await mindSporeLite.loadModelFromFile(modelFile);
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();

  modelInputs[0].setData(inputBuffer);
  mindSporeLiteModel.predict(modelInputs, (mindSporeLiteTensor : mindSporeLite.MSTensor[]) => {
    let output = new Float32Array(mindSporeLiteTensor[0].getData());
    for (let i = 0; i < output.length; i++) {
      console.info('MS_LITE_LOG: ' + output[i].toString());
    }
  })
})

predict

predict(inputs: MSTensor[]): Promise<MSTensor[]>

Executes model inference. This API uses a promise to return the result. Ensure that the model object is not reclaimed when being invoked.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
inputs	MSTensor[]	Yes	List of input models.

Return value

Type	Description
Promise<MSTensor[]>	Promise used to return the result, List of MSTensor objects.

Example

import { mindSporeLite } from '@kit.MindSporeLiteKit';
import { common } from '@kit.AbilityKit';
import { UIContext } from '@kit.ArkUI';

let inputName = 'input_data.bin';
let globalContext = new UIContext().getHostContext() as common.UIAbilityContext;
globalContext.getApplicationContext().resourceManager.getRawFileContent(inputName).then(async (buffer : Uint8Array) => {
  let inputBuffer = buffer.buffer;
  let modelFile = '/path/to/xxx.ms';
  let mindSporeLiteModel : mindSporeLite.Model = await mindSporeLite.loadModelFromFile(modelFile);
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  modelInputs[0].setData(inputBuffer);
  mindSporeLiteModel.predict(modelInputs).then((mindSporeLiteTensor : mindSporeLite.MSTensor[]) => {
    let output = new Float32Array(mindSporeLiteTensor[0].getData());
    for (let i = 0; i < output.length; i++) {
      console.info(output[i].toString());
    }
  })
})

resize

resize(inputs: MSTensor[], dims: Array<Array<number>>): boolean

Resets the tensor size.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
inputs	MSTensor[]	Yes	List of input models.
dims	Array<Array<number>>	Yes	Target tensor size.

Return value

Type	Description
boolean	Result indicating whether the setting is successful. The value true indicates that the tensor size is successfully reset, and the value false indicates the opposite.

Example

let modelFile = '/path/to/xxx.ms';
mindSporeLite.loadModelFromFile(modelFile).then((mindSporeLiteModel : mindSporeLite.Model) => {
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  let new_dim = new Array([1,32,32,1]);
  mindSporeLiteModel.resize(modelInputs, new_dim);
})

runStep¹²⁺

runStep(inputs: MSTensor[]): boolean

Defines a single-step training model. This API is used only for on-device training.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
inputs	MSTensor[]	Yes	List of input models.

Return value

Type	Description
boolean	Result indicating whether the operation is successful. The value true indicates that the operation is successful, and the value false indicates the opposite.

Example

let modelFile = '/path/to/xxx.ms';
mindSporeLite.loadTrainModelFromFile(modelFile).then((mindSporeLiteModel: mindSporeLite.Model) => {
  mindSporeLiteModel.trainMode = true;
  const modelInputs = mindSporeLiteModel.getInputs();
  let ret = mindSporeLiteModel.runStep(modelInputs);
  if (ret == false) {
    console.error('MS_LITE_LOG: runStep failed.')
  }
})

getWeights¹²⁺

getWeights(): MSTensor[]

Obtains all weight tensors of a model. This API is used only for on-device training.

System capability: SystemCapability.AI.MindSporeLite

Return value

Type	Description
MSTensor[]	Weight tensor of the training model.

Example

import { mindSporeLite } from '@kit.MindSporeLiteKit';
import { common } from '@kit.AbilityKit';
import { UIContext } from '@kit.ArkUI';

let modelFile = 'xxx.ms';
let globalContext = new UIContext().getHostContext() as common.UIAbilityContext;
globalContext.getApplicationContext().resourceManager.getRawFileContent(modelFile).then((modelBuffer : Uint8Array) => {
  mindSporeLite.loadTrainModelFromBuffer(modelBuffer.buffer.slice(0)).then((mindSporeLiteModel: mindSporeLite.Model) => {
    mindSporeLiteModel.trainMode = true;
    const weights = mindSporeLiteModel.getWeights();
    for (let i = 0; i < weights.length; i++) {
      let printStr = weights[i].name + ", ";
      printStr += weights[i].shape + ", ";
      printStr += weights[i].dtype + ", ";
      printStr += weights[i].dataSize + ", ";
      printStr += weights[i].getData();
      console.info("MS_LITE weights: ", printStr);
    }
  })
})

updateWeights¹²⁺

updateWeights(weights: MSTensor[]): boolean

Weight of the updated model, which is used only for on-device training.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
weights	MSTensor[]	Yes	List of weight tensors.

Return value

Type	Description
boolean	Result indicating whether the operation is successful. The value true indicates that the operation is successful, and the value false indicates the opposite.

Example

import { mindSporeLite } from '@kit.MindSporeLiteKit';
import { common } from '@kit.AbilityKit';
import { UIContext } from '@kit.ArkUI';

let modelFile = 'xxx.ms';
let globalContext = new UIContext().getHostContext() as common.UIAbilityContext;
globalContext.getApplicationContext().resourceManager.getRawFileContent(modelFile).then((modelBuffer : Uint8Array) => {
  mindSporeLite.loadTrainModelFromBuffer(modelBuffer.buffer.slice(0)).then((mindSporeLiteModel: mindSporeLite.Model) => {
    mindSporeLiteModel.trainMode = true;
    const weights = mindSporeLiteModel.getWeights();
    let ret = mindSporeLiteModel.updateWeights(weights);
    if (ret == false) {
      console.error('MS_LITE_LOG: updateWeights failed.')
    }
  })
})

setupVirtualBatch¹²⁺

setupVirtualBatch(virtualBatchMultiplier: number, lr: number, momentum: number): boolean

Sets the virtual batch for training. This API is used only for on-device training.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
virtualBatchMultiplier	number	Yes	Virtual batch multiplier. If the value is less than 1, the virtual batch is disabled.
lr	number	Yes	Learning rate.
momentum	number	Yes	Momentum.

Return value

Type	Description
boolean	Result indicating whether the operation is successful. The value true indicates that the operation is successful, and the value false indicates the opposite.

Example

import { mindSporeLite } from '@kit.MindSporeLiteKit';
import { common } from '@kit.AbilityKit';
import { UIContext } from '@kit.ArkUI';

let modelFile = 'xxx.ms';
let globalContext = new UIContext().getHostContext() as common.UIAbilityContext;
globalContext.getApplicationContext().resourceManager.getRawFileContent(modelFile).then((modelBuffer : Uint8Array) => {
  mindSporeLite.loadTrainModelFromBuffer(modelBuffer.buffer.slice(0)).then((mindSporeLiteModel: mindSporeLite.Model) => {
    mindSporeLiteModel.trainMode = true;
    let ret = mindSporeLiteModel.setupVirtualBatch(2,-1,-1);
    if (ret == false) {
      console.error('MS_LITE setupVirtualBatch failed.')
    }
  })
})

exportModel¹²⁺

exportModel(modelFile: string, quantizationType?: QuantizationType, exportInferenceOnly?: boolean, outputTensorName?: string[]): boolean

Exports a training model. This API is used only for on-device training.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
modelFile	string	Yes	File path of the training models.
quantizationType	QuantizationType	No	Quantization type. The default value is NO_QUANT.
exportInferenceOnly	boolean	No	Whether to export inference models only. The value true means to export only inference models, and the value false means to export both training and inference models. The default value is true.
outputTensorName	string[]	No	Name of the output tensor of the exported training model. The default value is an empty string array, which indicates full export.

Return value

Type	Description
boolean	Result indicating whether the operation is successful. The value true indicates that the operation is successful, and the value false indicates the opposite.

Example

let modelFile = '/path/to/xxx.ms';
let newPath = '/newpath/to';
mindSporeLite.loadTrainModelFromFile(modelFile).then((mindSporeLiteModel: mindSporeLite.Model) => {
  mindSporeLiteModel.trainMode = true;
  let ret = mindSporeLiteModel.exportModel(newPath + "/new_model.ms", mindSporeLite.QuantizationType.NO_QUANT, true);
  if (ret == false) {
    console.error('MS_LITE exportModel failed.')
  }
})

exportWeightsCollaborateWithMicro¹²⁺

exportWeightsCollaborateWithMicro(weightFile: string, isInference?: boolean, enableFp16?: boolean, changeableWeightsName?: string[]): boolean

Exports model weights for micro inference. This API is available only for on-device training.

Micro inference is a ultra-lightweight micro AI deployment solution provided by MindSpore Lite to deploy hardware backends for Micro Controller Units (MCUs). This solution directly converts models into lightweight code in offline mode, eliminating the need for online model parsing and graph compilation.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
weightFile	string	Yes	Path of the weight file.
isInference	boolean	No	Whether to export weights from the inference model. The value true means to export weights from the inference model. The default value is true. Currently, only true is supported.
enableFp16	boolean	No	Whether to store floating-point weights in float16 format. The value true means to store floating-point weights in float16 format, and the value false means the opposite. The default value is false.
changeableWeightsName	string[]	No	Name of the variable weight. The default value is an empty string array.

Return value

Type	Description
boolean	Result indicating whether the operation is successful. The value true indicates that the operation is successful, and the value false indicates the opposite.

Example

let modelFile = '/path/to/xxx.ms';
let microWeight = '/path/to/xxx.bin';
mindSporeLite.loadTrainModelFromFile(modelFile).then((mindSporeLiteModel: mindSporeLite.Model) => {
  let ret = mindSporeLiteModel.exportWeightsCollaborateWithMicro(microWeight);
  if (ret == false) {
    console.error('MSLITE exportWeightsCollaborateWithMicro failed.')
  }
})

MSTensor

Represents an MSTensor instance, with properties and APIs defined. It is a special data structure similar to arrays and matrices. It is the basic data structure used in MindSpore Lite network operations.

In the following sample code, you first need to use getInputs() to obtain an MSTensor instance before calling related APIs.

Attributes

System capability: SystemCapability.AI.MindSporeLite

Name	Type	Read Only	Optional	Description
name	string	No	No	Tensor name.
shape	number[]	No	No	Tensor dimension array.
elementNum	number	No	No	Length of the tensor dimension array.
dataSize	number	No	No	Length of tensor data.
dtype	DataType	No	No	Tensor data type.
format	Format	No	No	Tensor data format.

Example

let modelFile = '/path/to/xxx.ms';
mindSporeLite.loadModelFromFile(modelFile).then((mindSporeLiteModel : mindSporeLite.Model) => {
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  console.info(modelInputs[0].name);
  console.info(modelInputs[0].shape.toString());
  console.info(modelInputs[0].elementNum.toString());
  console.info(modelInputs[0].dtype.toString());
  console.info(modelInputs[0].format.toString());
  console.info(modelInputs[0].dataSize.toString());
})

getData

getData(): ArrayBuffer

Obtains tensor data.

System capability: SystemCapability.AI.MindSporeLite

Return value

Type	Description
ArrayBuffer	Pointer to the tensor data.

Example

import { mindSporeLite } from '@kit.MindSporeLiteKit';
import { common } from '@kit.AbilityKit';
import { UIContext } from '@kit.ArkUI';

let inputName = 'input_data.bin';
let globalContext = new UIContext().getHostContext() as common.UIAbilityContext;
globalContext.getApplicationContext().resourceManager.getRawFileContent(inputName).then(async (buffer : Uint8Array) => {
  let inputBuffer = buffer.buffer;
  let modelFile = '/path/to/xxx.ms';
  let mindSporeLiteModel : mindSporeLite.Model = await mindSporeLite.loadModelFromFile(modelFile);
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  modelInputs[0].setData(inputBuffer);
  mindSporeLiteModel.predict(modelInputs).then((mindSporeLiteTensor : mindSporeLite.MSTensor[]) => {
    let output = new Float32Array(mindSporeLiteTensor[0].getData());
    for (let i = 0; i < output.length; i++) {
      console.info(output[i].toString());
    }
  })
})

setData

setData(inputArray: ArrayBuffer): void

Sets the tensor data.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Name	Type	Mandatory	Description
inputArray	ArrayBuffer	Yes	Input data buffer of the tensor.

Example

import { mindSporeLite } from '@kit.MindSporeLiteKit';
import { common } from '@kit.AbilityKit';
import { UIContext } from '@kit.ArkUI';

let inputName = 'input_data.bin';
let globalContext = new UIContext().getHostContext() as common.UIAbilityContext;
globalContext.getApplicationContext().resourceManager.getRawFileContent(inputName).then(async (buffer : Uint8Array) => {
  let inputBuffer = buffer.buffer;
  let modelFile = '/path/to/xxx.ms';
  let mindSporeLiteModel : mindSporeLite.Model = await mindSporeLite.loadModelFromFile(modelFile);
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  modelInputs[0].setData(inputBuffer);
})

DataType

Tensor data type.

System capability: SystemCapability.AI.MindSporeLite

Name	Value	Description
TYPE_UNKNOWN	0	Unknown type.
NUMBER_TYPE_INT8	32	Int8 type.
NUMBER_TYPE_INT16	33	Int16 type.
NUMBER_TYPE_INT32	34	Int32 type.
NUMBER_TYPE_INT64	35	Int64 type.
NUMBER_TYPE_UINT8	37	UInt8 type.
NUMBER_TYPE_UINT16	38	UInt16 type.
NUMBER_TYPE_UINT32	39	UInt32 type.
NUMBER_TYPE_UINT64	40	UInt64 type.
NUMBER_TYPE_FLOAT16	42	Float16 type.
NUMBER_TYPE_FLOAT32	43	Float32 type.
NUMBER_TYPE_FLOAT64	44	Float64 type.

Format

Enumerates tensor data formats.

System capability: SystemCapability.AI.MindSporeLite

Name	Value	Description
DEFAULT_FORMAT	-1	Unknown data format.
NCHW	0	NCHW format.
NHWC	1	NHWC format.
NHWC4	2	NHWC4 format.
HWKC	3	HWKC format.
HWCK	4	HWCK format.
KCHW	5	KCHW format.

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harmony 鸿蒙@ohos.ai.mindSporeLite (On-device AI Framework)

@ohos.ai.mindSporeLite (On-device AI Framework)

Modules to Import

mindSporeLite.loadModelFromFile

mindSporeLite.loadModelFromFile

mindSporeLite.loadModelFromFile

mindSporeLite.loadModelFromBuffer

mindSporeLite.loadModelFromBuffer

mindSporeLite.loadModelFromBuffer

mindSporeLite.loadModelFromFd

mindSporeLite.loadModelFromFd

mindSporeLite.loadModelFromFd

mindSporeLite.loadTrainModelFromFile12+

mindSporeLite.loadTrainModelFromBuffer12+

mindSporeLite.loadTrainModelFromFd12+

mindSporeLite.getAllNNRTDeviceDescriptions12+

Context

Attributes

CpuDevice

Attributes

ThreadAffinityMode

NNRTDevice

Attributes

PerformanceMode12+

Priority12+

Extension12+

Attributes

NNRTDeviceDescription12+

deviceID

deviceType

deviceName

NNRTDeviceType12+

TrainCfg12+

Attributes

OptimizationLevel12+

QuantizationType12+

Model

Attributes

getInputs

predict

predict

resize

runStep12+

getWeights12+

updateWeights12+

setupVirtualBatch12+

exportModel12+

exportWeightsCollaborateWithMicro12+

MSTensor

Attributes

getData

setData

DataType

Format

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本文目录

mindSporeLite.loadTrainModelFromFile¹²⁺

mindSporeLite.loadTrainModelFromBuffer¹²⁺

mindSporeLite.loadTrainModelFromFd¹²⁺

mindSporeLite.getAllNNRTDeviceDescriptions¹²⁺

PerformanceMode¹²⁺

Priority¹²⁺

Extension¹²⁺

NNRTDeviceDescription¹²⁺

NNRTDeviceType¹²⁺

TrainCfg¹²⁺

OptimizationLevel¹²⁺

QuantizationType¹²⁺

runStep¹²⁺

getWeights¹²⁺

updateWeights¹²⁺

setupVirtualBatch¹²⁺

exportModel¹²⁺

exportWeightsCollaborateWithMicro¹²⁺