indexCORE.js

/**
 * @license
 * Copyright 2018 Google LLC
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */

import * as tfc from '@tensorflow/tfjs-core';
import {loadFrozenModel, FrozenModel} from '@tensorflow/tfjs-converter';
// import {SCAVENGER_CLASSES} from './scavenger_classes';


// type TensorMap = {[name: string]: tfc.Tensor};

const MODEL_FILE_URL = './model/web_model.pb';
const WEIGHT_MANIFEST_FILE_URL = './model/weights_manifest.json';
// const INPUT_NODE_NAME = 'input';
// const OUTPUT_NODE_NAME = 'final_result';
// const PREPROCESS_DIVISOR = tfc.scalar(255 / 2);


export class MobileNet {

  // model: FrozenModel;

  // constructor(model){
  //   this.model = model;
  // }

  async load() {
    this.model = await loadFrozenModel(
      MODEL_FILE_URL,
      WEIGHT_MANIFEST_FILE_URL
    );
  }



  // dispose() {
  //   if (this.model) {
  //     this.model.dispose();
  //   }
  // }
  /**
   * Infer through MobileNet, assumes variables have been loaded. This does
   * standard ImageNet pre-processing before inferring through the model. This
   * method returns named activations as well as softmax logits.
   *
   * @param input un-preprocessed input Array.
   * @return The softmax logits.
   */
  // predict(input: tfc.Tensor): tfc.Tensor1D {
  //   const preprocessedInput = tfc.div(
  //       tfc.sub(input.asType('float32'), PREPROCESS_DIVISOR),
  //       PREPROCESS_DIVISOR);
  //   const reshapedInput =
  //       preprocessedInput.reshape([1, ...preprocessedInput.shape]);
  //   const dict: TensorMap = {};
  //   dict[INPUT_NODE_NAME] = reshapedInput;
  //   return this.model.execute(dict, OUTPUT_NODE_NAME) as tfc.Tensor1D;
  // }

  // getTopKClasses(predictions: tfc.Tensor1D, topK: number) {
  //   const values = predictions.dataSync();
  //   predictions.dispose();

  //   let predictionList = [];
  //   for (let i = 0; i < values.length; i++) {
  //     predictionList.push({value: values[i], index: i});
  //   }
  //   predictionList = predictionList.sort((a, b) => {
  //     return b.value - a.value;
  //   }).slice(0, topK);

  //   return predictionList.map(x => {
  //     return {label: SCAVENGER_CLASSES[x.index], value: x.value};
  //   });
  // }
}

async function load(){
  this.model = await loadFrozenModel(
    MODEL_FILE_URL,
    WEIGHT_MANIFEST_FILE_URL
  );
}


load();