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

PyTorch like deep learning inferrence library

import Tensor from '../../types';
import { NodeId } from '../model';
import { OnnxNode } from '../node';
import { Constants, OnnxModelI } from '../types';
export declare abstract class Optimization {
    /**
     * Finds possible places in the graph for application
     *
     * @param graph The graph to search for optimization applications
     *
     * @Returns a list of possible applications
     *          Each application consists of a list of nodes that will be replaced
     */
    abstract findApplications(model: OnnxModelI): NodeId[][];
    abstract apply(nodes: OnnxNode[], resolveConstant: (name: string) => Tensor<any> | undefined, constants: Constants, onnxVersion: number): OnnxNode;
}
export declare abstract class SequenceOptimization extends Optimization {
    protected nodeTypes: string[];
    constructor(nodeTypes: string[]);
    findApplications(model: OnnxModelI): NodeId[][];
    checkApplication(model: OnnxModelI, nodeId: string): string[] | undefined;
    canApply(nodes: OnnxNode[]): boolean;
}