@hoff97/tensor-js/dist/lib/onnx/nodes/nary/mean.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 MeanNode extends OnnxNode {
    constructor(attributes, inputs, outputs, constants, onnxVersion, mode) {
        super(attributes, inputs, outputs, constants, onnxVersion, mode);
    }
    forward(inputs) {
        return __awaiter(this, void 0, void 0, function* () {
            if (inputs.length > 2) {
                // This logging seems to slow down the operation more than the operation itself
                //console.warn(`Sum with more than 2 tensors is currently slow. Doing concat with ${inputs.length} tensors`);
            }
            let result = inputs[0];
            for (let i = 1; i < inputs.length; i++) {
                const newRes = result.add(inputs[i]);
                if (i > 1) {
                    result.delete();
                }
                result = newRes;
            }
            const mean = result.multiplyScalar(1 / inputs.length);
            if (inputs.length > 2) {
                result.delete();
            }
            return [mean];
        });
    }
    getType() {
        return 'Mean';
    }
    delete() { }
}
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