@hoff97/tensor-js/dist/lib/onnx/node.d.ts

PyTorch like deep learning inferrence library

import { onnx } from 'onnx-proto';
import { Tensor } from '../library';
import { Mode, Module } from '../model/module';
import { DType } from '../types';
import { Attributes, Constants } from './types';
export declare abstract class OnnxNode extends Module {
    protected onnxVersion: number;
    inputs: string[];
    outputs: string[];
    variableInputs: number;
    attributes: {
        [name: string]: onnx.IAttributeProto;
    };
    constructor(attributes: Attributes, inputs: string[], outputs: string[], constants: Constants, onnxVersion: number, mode: Mode);
    initialize(resolveConstant: (name: string) => Tensor<any> | undefined): void;
    getAttribute(name: string): onnx.IAttributeProto;
    getAttributeString(name: string): string | undefined;
    getAttributeInts(name: string): number[] | undefined;
    getAttributeInt(name: string): number | null | undefined;
    getAttributeFloat(name: string): number | null | undefined;
    getAttributeFloats(name: string): number[] | null | undefined;
    getAttributeTensor(name: string): onnx.ITensorProto | null | undefined;
    toValues<DTpe extends DType>(tensor: Tensor<DTpe>): Promise<number[]>;
    toCPU(): Promise<void>;
    toWASM(): Promise<void>;
    toGPU(): Promise<void>;
    abstract forward(inputs: Tensor<any>[]): Promise<Tensor<any>[]>;
    abstract getType(): string;
    abstract delete(): void;
}