onnxruntime
Version:
Node.js binding of ONNXRuntime
173 lines (172 loc) • 7.07 kB
TypeScript
import { OnnxValue } from './onnx-value';
/**
* Represent a runtime instance of an ONNX model.
*/
export interface InferenceSession {
/**
* Execute the model asynchronously with the given feeds and options.
*
* @param feeds Representation of the model input. See type description of `InferenceSession.InputType` for detail.
* @param options Optional. A set of options that controls the behavior of model inference.
*/
run(feeds: InferenceSession.FeedsType, options?: InferenceSession.RunOptions): Promise<InferenceSession.ReturnType>;
/**
* Execute the model asynchronously with the given feeds, fetches and options.
*
* @param feeds Representation of the model input. See type description of `InferenceSession.InputType` for detail.
* @param fetches Representation of the model output. See type description of `InferenceSession.OutputType` for
* detail.
* @param options Optional. A set of options that controls the behavior of model inference.
* @returns A promise that resolves to a map, which uses output names as keys and OnnxValue as corresponding values.
*/
run(feeds: InferenceSession.FeedsType, fetches: InferenceSession.FetchesType, options?: InferenceSession.RunOptions): Promise<InferenceSession.ReturnType>;
/**
* Get input names of the loaded model.
*/
readonly inputNames: readonly string[];
/**
* Get output names of the loaded model.
*/
readonly outputNames: readonly string[];
}
export declare namespace InferenceSession {
type OnnxValueMapType = {
readonly [name: string]: OnnxValue;
};
type NullableOnnxValueMapType = {
readonly [name: string]: OnnxValue | null;
};
/**
* A feeds (model inputs) is an object that use input names as keys and OnnxValue as corresponding values.
*/
type FeedsType = OnnxValueMapType;
/**
* A fetches (model outputs) could be one of the following:
*
* - Omitted. Use model's output names definition.
* - An array of string indicating the output names.
* - An object that use output names as keys and OnnxValue or null as corresponding values.
*
* REMARK: different from input argument, in output, OnnxValue is optional. If an OnnxValue is present it will be
* used as a pre-allocated value by the inference engine; if omitted, inference engine will allocate buffer
* internally.
*/
type FetchesType = readonly string[] | NullableOnnxValueMapType;
type ReturnType = {
readonly [name: string]: OnnxValue;
};
/**
* A set of configurations for session behavior.
*/
interface SessionOptions {
/**
* An array of execution provider options.
*
* An execution provider option can be a string indicating the name of the execution provider,
* or an object of corresponding type.
*/
executionProviders?: readonly SessionOptions.ExecutionProviderConfig[];
/**
* The intra OP threads number.
*/
intraOpNumThreads?: number;
/**
* The inter OP threads number.
*/
interOpNumThreads?: number;
/**
* The optimization level.
*/
graphOptimizationLevel?: 'disabled' | 'basic' | 'extended' | 'all';
/**
* Whether enable CPU memory arena.
*/
enableCpuMemArena?: boolean;
/**
* Whether enable memory pattern.
*/
enableMemPattern?: boolean;
/**
* Execution mode.
*/
executionMode?: 'sequential' | 'parallel';
/**
* Log ID.
*/
logId?: string;
/**
* Log severity level. See
* https://github.com/microsoft/onnxruntime/blob/master/include/onnxruntime/core/common/logging/severity.h
*/
logSeverityLevel?: 0 | 1 | 2 | 3 | 4;
}
namespace SessionOptions {
interface ExecutionProviderOptionMap {
cpu: CpuExecutionProviderOption;
cuda: CudaExecutionProviderOption;
}
type ExecutionProviderName = keyof ExecutionProviderOptionMap;
type ExecutionProviderConfig = ExecutionProviderOptionMap[ExecutionProviderName] | ExecutionProviderName;
interface CpuExecutionProviderOption {
readonly name: 'cpu';
useArena?: boolean;
}
interface CudaExecutionProviderOption {
readonly name: 'cuda';
deviceId?: number;
}
}
/**
* A set of configurations for inference run behavior
*/
interface RunOptions {
/**
* Log severity level. See
* https://github.com/microsoft/onnxruntime/blob/master/include/onnxruntime/core/common/logging/severity.h
*/
logSeverityLevel?: 0 | 1 | 2 | 3 | 4;
/**
* A tag for the Run() calls using this
*/
tag?: string;
}
interface ValueMetadata {
}
}
export interface InferenceSessionFactory {
/**
* Create a new inference session and load model asynchronously from an ONNX model file.
*
* @param path The file path of the model to load.
* @param options specify configuration for creating a new inference session.
* @returns A promise that resolves to an InferenceSession object.
*/
create(path: string, options?: InferenceSession.SessionOptions): Promise<InferenceSession>;
/**
* Create a new inference session and load model asynchronously from an array bufer.
*
* @param buffer An ArrayBuffer representation of an ONNX model.
* @param options specify configuration for creating a new inference session.
* @returns A promise that resolves to an InferenceSession object.
*/
create(buffer: ArrayBufferLike, options?: InferenceSession.SessionOptions): Promise<InferenceSession>;
/**
* Create a new inference session and load model asynchronously from segment of an array bufer.
*
* @param buffer An ArrayBuffer representation of an ONNX model.
* @param byteOffset The beginning of the specified portion of the array buffer.
* @param byteLength The length in bytes of the array buffer.
* @param options specify configuration for creating a new inference session.
* @returns A promise that resolves to an InferenceSession object.
*/
create(buffer: ArrayBufferLike, byteOffset: number, byteLength?: number, options?: InferenceSession.SessionOptions): Promise<InferenceSession>;
/**
* Create a new inference session and load model asynchronously from a Uint8Array.
*
* @param buffer A Uint8Array representation of an ONNX model.
* @param options specify configuration for creating a new inference session.
* @returns A promise that resolves to an InferenceSession object.
*/
create(buffer: Uint8Array, options?: InferenceSession.SessionOptions): Promise<InferenceSession>;
}
export declare const InferenceSession: InferenceSessionFactory;