@hoff97/tensor-js/dist/lib/onnx/nodes/reduce/reduceNode.js

PyTorch like deep learning inferrence library

var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
    function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
    return new (P || (P = Promise))(function (resolve, reject) {
        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
        function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
        step((generator = generator.apply(thisArg, _arguments || [])).next());
    });
};
import { OnnxNode } from '../../node';
export class ReduceNode extends OnnxNode {
    constructor(attributes, inputs, outputs, constants, onnxVersion, name, mode) {
        super(attributes, inputs, outputs, constants, onnxVersion, mode);
        this.axes = this.getAttributeInts('axes');
        const keep = this.getAttributeInt('keepdims');
        this.keepDims = keep === 1 || keep === undefined;
        this.name = name;
    }
    getAxes(input) {
        if (this.axes !== undefined) {
            return this.axes;
        }
        else {
            const rank = input.getShape().length;
            const res = new Array(rank);
            for (let i = 0; i < rank; i++) {
                res[i] = i;
            }
            return res;
        }
    }
    forward(inputs) {
        return __awaiter(this, void 0, void 0, function* () {
            if (this.onnxVersion < 13) {
                return [this.calc(inputs[0])];
            }
            throw new Error(`${this.name} is not implemented for onnx version ${this.onnxVersion}`);
        });
    }
    getType() {
        return this.name;
    }
    delete() { }
}
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