@ohos.ai.mindSporeLite (Inference)

MindSpore Lite is an AI engine that implements AI model inference for different hardware devices. It has been used in a wide range of fields, such as image classification, target recognition, facial recognition, and character recognition. The mindSporeLite module provides APIs for the MindSpore Lite inference engine to implement model inference.

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 '@ohos.ai.mindSporeLite';

Context

Defines the configuration information of the running environment.

Attributes

System capability: SystemCapability.AI.MindSporeLite

Example

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

CpuDevice

Defines the CPU backend device option.

Attributes

System capability: SystemCapability.AI.MindSporeLite

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.threadAffinityMode = 0;
context.cpu.precisionMode = 'preferred_fp16';
context.cpu.threadAffinityCoreList = [0, 1, 2];

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. Currently, this API is not supported.

System capability: SystemCapability.AI.MindSporeLite

ThreadAffinityMode

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

System capability: SystemCapability.AI.MindSporeLite

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

Example

let model_file : string = '/path/to/xxx.ms';
mindSporeLite.loadModelFromFile(model_file, (result : mindSporeLite.Model) => {
  let modelInputs : mindSporeLite.MSTensor[] = result.getInputs();
  console.log(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

Example

let context: mindSporeLite.Context = {};
context.target = ['cpu'];
let model_file : string = '/path/to/xxx.ms';
mindSporeLite.loadModelFromFile(model_file, context, (result : mindSporeLite.Model) => {
  let modelInputs : mindSporeLite.MSTensor[] = result.getInputs();
  console.log(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

Return value

Example

let model_file = '/path/to/xxx.ms';
mindSporeLite.loadModelFromFile(model_file).then((result : mindSporeLite.Model) => {
  let modelInputs : mindSporeLite.MSTensor[] = result.getInputs();
  console.log(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

Example

// Construct a singleton object.
export class GlobalContext {
  private constructor() {}
  private static instance: GlobalContext;
  private _objects = new Map<string, Object>();

  public static getContext(): GlobalContext {
    if (!GlobalContext.instance) {
      GlobalContext.instance = new GlobalContext();
    }
    return GlobalContext.instance;
  }

  getObject(value: string): Object|undefined {
    return this._objects.get(value);
  }

  setObject(key: string, objectClass: Object): void {
    this._objects.set(key, objectClass);
  }

}

import resourceManager from '@ohos.resourceManager'
import { GlobalContext } from '../GlobalContext';
import mindSporeLite from '@ohos.ai.mindSporeLite';
import common from '@ohos.app.ability.common';
export class Test {
  value:number = 0;
  foo(): void {
    GlobalContext.getContext().setObject("value", this.value);
  }
}
let globalContext = GlobalContext.getContext().getObject("value") as common.UIAbilityContext;

let modelName = '/path/to/xxx.ms';
globalContext.resourceManager.getRawFileContent(modelName).then((buffer : Uint8Array) => {
  let modelBuffer : ArrayBuffer = buffer.buffer;
  mindSporeLite.loadModelFromBuffer(modelBuffer, (result : mindSporeLite.Model) => {
    let modelInputs : mindSporeLite.MSTensor[] = result.getInputs();
    console.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

Example

import resourceManager from '@ohos.resourceManager'
import { GlobalContext } from '../GlobalContext';
import mindSporeLite from '@ohos.ai.mindSporeLite';
import common from '@ohos.app.ability.common';
let modelName = '/path/to/xxx.ms';
export class Test {
  value:number = 0;
  foo(): void {
    GlobalContext.getContext().setObject("value", this.value);
  }
}
let globalContext= GlobalContext.getContext().getObject("value") as common.UIAbilityContext;

globalContext.resourceManager.getRawFileContent(modelName).then((buffer : Uint8Array) => {
  let modelBuffer : ArrayBuffer = buffer.buffer;
  let context: mindSporeLite.Context = {};
  context.target = ['cpu'];
  mindSporeLite.loadModelFromBuffer(modelBuffer, context, (result : mindSporeLite.Model) => {
    let modelInputs : mindSporeLite.MSTensor[] = result.getInputs();
    console.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

Return value

Example

import resourceManager from '@ohos.resourceManager'
import { GlobalContext } from '../GlobalContext';
import mindSporeLite from '@ohos.ai.mindSporeLite';
import common from '@ohos.app.ability.common';
let modelName = '/path/to/xxx.ms';
export class Test {
  value:number = 0;
  foo(): void {
    GlobalContext.getContext().setObject("value", this.value);
  }
}
let globalContext = GlobalContext.getContext().getObject("value") as common.UIAbilityContext;

globalContext.resourceManager.getRawFileContent(modelName).then((buffer : Uint8Array) => {
  let modelBuffer : ArrayBuffer = buffer.buffer;
  mindSporeLite.loadModelFromBuffer(modelBuffer).then((result : mindSporeLite.Model) => {
    let modelInputs : mindSporeLite.MSTensor[] = result.getInputs();
    console.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

Example

import fs from '@ohos.file.fs';
let model_file = '/path/to/xxx.ms';
let file = fs.openSync(model_file, fs.OpenMode.READ_ONLY);
mindSporeLite.loadModelFromFd(file.fd, (result : mindSporeLite.Model) => {
  let modelInputs : mindSporeLite.MSTensor[] = result.getInputs();
  console.log(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

Example

import fs from '@ohos.file.fs';
let model_file = '/path/to/xxx.ms';
let context : mindSporeLite.Context = {};
context.target = ['cpu'];
let file = fs.openSync(model_file, fs.OpenMode.READ_ONLY);
mindSporeLite.loadModelFromFd(file.fd, context, (result : mindSporeLite.Model) => {
  let modelInputs : mindSporeLite.MSTensor[] = result.getInputs();
  console.log(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

Return value

Example

import fs from '@ohos.file.fs';
let model_file = '/path/to/xxx.ms';
let file = fs.openSync(model_file, fs.OpenMode.READ_ONLY);
let mindSporeLiteModel : mindSporeLite.Model = await mindSporeLite.loadModelFromFd(file.fd);
let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
console.log(modelInputs[0].name);

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.

getInputs

getInputs(): MSTensor[]

Obtains the model input for inference.

System capability: SystemCapability.AI.MindSporeLite

Return value

Example

let model_file = '/path/to/xxx.ms';
mindSporeLite.loadModelFromFile(model_file).then((result : mindSporeLite.Model) => {
  let modelInputs : mindSporeLite.MSTensor[] = result.getInputs();
  console.log(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

Example

import resourceManager from '@ohos.resourceManager'
import { GlobalContext } from '../GlobalContext';
import mindSporeLite from '@ohos.ai.mindSporeLite';
import common from '@ohos.app.ability.common';
export class Test {
  value:number = 0;
  foo(): void {
    GlobalContext.getContext().setObject("value", this.value);
  }
}
let globalContext = GlobalContext.getContext().getObject("value") as common.UIAbilityContext;

let inputName = 'input_data.bin';
globalContext.resourceManager.getRawFileContent(inputName).then(async (buffer : Uint8Array) => {
  let modelBuffer : ArrayBuffer = buffer.buffer;
  let model_file : string = '/path/to/xxx.ms';
  let mindSporeLiteModel : mindSporeLite.Model = await mindSporeLite.loadModelFromFile(model_file);
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();

  modelInputs[0].setData(modelBuffer);
  mindSporeLiteModel.predict(modelInputs, (result : mindSporeLite.MSTensor[]) => {
    let output = new Float32Array(result[0].getData());
    for (let i = 0; i < output.length; i++) {
      console.log(output[i].toString());
    }
  })
})

predict

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

Executes the inference model. 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

Return value

Example

import resourceManager from '@ohos.resourceManager'
import { GlobalContext } from '../GlobalContext';
import mindSporeLite from '@ohos.ai.mindSporeLite';
import common from '@ohos.app.ability.common';
export class Test {
    value:number = 0;
    foo(): void {
    GlobalContext.getContext().setObject("value", this.value);
}
}
let globalContext = GlobalContext.getContext().getObject("value") as common.UIAbilityContext;;
let inputName = 'input_data.bin';
globalContext.resourceManager.getRawFileContent(inputName).then(async (buffer : Uint8Array) => {
  let inputBuffer = buffer.buffer;
  let model_file = '/path/to/xxx.ms';
  let mindSporeLiteModel : mindSporeLite.Model = await mindSporeLite.loadModelFromFile(model_file);
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  modelInputs[0].setData(modelBuffer);
  mindSporeLiteModel.predict(modelInputs).then((result : mindSporeLite.MSTensor[]) => {
    let output = new Float32Array(result[0].getData());
    for (let i = 0; i < output.length; i++) {
      console.log(output[i].toString());
    }
  })
})

resize

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

Resets the tensor size.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Return value

Example

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

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

Example

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

getData

getData(): ArrayBuffer

Obtains tensor data.

System capability: SystemCapability.AI.MindSporeLite

Return value

Example

import resourceManager from '@ohos.resourceManager'
import { GlobalContext } from '../GlobalContext';
import mindSporeLite from '@ohos.ai.mindSporeLite';
import common from '@ohos.app.ability.common';
export class Test {
  value:number = 0;
  foo(): void {
    GlobalContext.getContext().setObject("value", this.value);
  }
}
let globalContext = GlobalContext.getContext().getObject("value") as common.UIAbilityContext;
let inputName = 'input_data.bin';
globalContext.resourceManager.getRawFileContent(inputName).then(async (buffer : Uint8Array) => {
  let inputBuffer = buffer.buffer;
  let model_file = '/path/to/xxx.ms';
  let mindSporeLiteModel : mindSporeLite.Model = await mindSporeLite.loadModelFromFile(model_file);
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  modelInputs[0].setData(inputBuffer);
  mindSporeLiteModel.predict(modelInputs).then((result : mindSporeLite.MSTensor[]) => {
    let output = new Float32Array(result[0].getData());
    for (let i = 0; i < output.length; i++) {
      console.log(output[i].toString());
    }
  })
})

setData

setData(inputArray: ArrayBuffer): void

Sets the tensor data.

System capability: SystemCapability.AI.MindSporeLite

Parameters

Example

import resourceManager from '@ohos.resourceManager'
import { GlobalContext } from '../GlobalContext';
import mindSporeLite from '@ohos.ai.mindSporeLite';
import common from '@ohos.app.ability.common';
export class Test {
  value:number = 0;
  foo(): void {
    GlobalContext.getContext().setObject("value", this.value);
  }
}
let globalContext = GlobalContext.getContext().getObject("value") as common.UIAbilityContext;
let inputName = 'input_data.bin';
globalContext.resourceManager.getRawFileContent(inputName).then(async (buffer : Uint8Array) => {
  let inputBuffer = buffer.buffer;
  let model_file = '/path/to/xxx.ms';
  let mindSporeLiteModel : mindSporeLite.Model = await mindSporeLite.loadModelFromFile(model_file);
  let modelInputs : mindSporeLite.MSTensor[] = mindSporeLiteModel.getInputs();
  modelInputs[0].setData(inputBuffer);
})

DataType

Tensor data type.

System capability: SystemCapability.AI.MindSporeLite

Format

Enumerates tensor data formats.

System capability: SystemCapability.AI.MindSporeLite

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