refactoring

.gitignore

@@ -1,2 +1,3 @@
 node_modules
 pnpm-lock.yaml
+test

demo/index.js

@@ -20,8 +20,8 @@ function str(json) {
 function log(...txt) {
   // eslint-disable-next-line no-console
   console.log(...txt);
-  // @ts-ignore
-  document.getElementById('log').innerHTML += `<br>${txt}`;
+  const div = document.getElementById('log');
+  if (div) div.innerHTML += `<br>${txt}`;
 }
 
 // helper function to draw detected faces

demo/webcam.js

@@ -19,8 +19,8 @@ function str(json) {
 function log(...txt) {
   // eslint-disable-next-line no-console
   console.log(...txt);
-  // @ts-ignore
-  document.getElementById('log').innerHTML += `<br>${txt}`;
+  const div = document.getElementById('log');
+  if (div) div.innerHTML += `<br>${txt}`;
 }
 
 // helper function to draw detected faces

package.json

@@ -22,7 +22,7 @@
   },
   "scripts": {
     "start": "node --trace-warnings demo/node-singleprocess.js",
-    "dev": "npm install && node server/serve.js",
+    "dev": "node --trace-warnings server/serve.js",
     "build": "rimraf dist/* types/* typedoc/* && node server/build.js",
     "lint": "eslint src/**/* demo/*.js server/*.js",
     "test": "eslint src/**/* demo/*.js server/*.js"
@@ -38,7 +38,6 @@
     "emotion-detection",
     "face-recognition"
   ],
-  "peerDependencies": {},
   "devDependencies": {
     "@tensorflow/tfjs": "^3.3.0",
     "@tensorflow/tfjs-backend-wasm": "^3.3.0",
@@ -50,7 +49,7 @@
     "@vladmandic/pilogger": "^0.2.15",
     "chokidar": "^3.5.1",
     "dayjs": "^1.10.4",
-    "esbuild": "^0.9.3",
+    "esbuild": "^0.9.5",
     "eslint": "^7.22.0",
     "eslint-config-airbnb-base": "^14.2.1",
     "eslint-plugin-import": "^2.22.1",

server/build.js

@@ -209,7 +209,7 @@ async function build(f, msg, dev = false) {
     for (const [targetGroupName, targetGroup] of Object.entries(targets)) {
       for (const [targetName, targetOptions] of Object.entries(targetGroup)) {
         // if triggered from watch mode, rebuild only browser bundle
-        if ((require.main !== module) && (targetGroupName !== 'browserBundle')) continue;
+        // if ((require.main !== module) && (targetGroupName !== 'browserBundle')) continue;
         // @ts-ignore
         const meta = await esbuild.build({ ...common, ...targetOptions });
         const stats = await getStats(meta);

src/ageGenderNet/AgeGenderNet.ts

@@ -5,9 +5,7 @@ import { seperateWeightMaps } from '../faceProcessor/util';
 import { TinyXception } from '../xception/TinyXception';
 import { extractParams } from './extractParams';
 import { extractParamsFromWeightMap } from './extractParamsFromWeightMap';
-import {
-  AgeAndGenderPrediction, Gender, NetOutput, NetParams,
-} from './types';
+import { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';
 import { NeuralNetwork } from '../NeuralNetwork';
 import { NetInput, TNetInput, toNetInput } from '../dom/index';
 

src/ageGenderNet/extractParamsFromWeightMap.ts

@@ -1,8 +1,6 @@
 import * as tf from '../../dist/tfjs.esm';
 
-import {
-  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,
-} from '../common/index';
+import { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';
 import { NetParams } from './types';
 
 export function extractParamsFromWeightMap(

src/classes/Box.ts

@@ -107,9 +107,7 @@ export class Box<BoxType = any> implements IBoundingBox, IRect {
       height += diff;
     }
 
-    return new Box({
-      x, y, width, height,
-    });
+    return new Box({ x, y, width, height });
   }
 
   public rescale(s: IDimensions | number): Box<BoxType> {
@@ -136,9 +134,7 @@ export class Box<BoxType = any> implements IBoundingBox, IRect {
   }
 
   public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {
-    const {
-      x, y, right, bottom,
-    } = this;
+    const { x, y, right, bottom } = this;
     const clippedX = Math.max(x, 0);
     const clippedY = Math.max(y, 0);
 

src/dom/NetInput.ts

@@ -3,9 +3,7 @@ import * as tf from '../../dist/tfjs.esm';
 import { Dimensions } from '../classes/Dimensions';
 import { env } from '../env/index';
 import { padToSquare } from '../ops/padToSquare';
-import {
-  computeReshapedDimensions, isTensor3D, isTensor4D, range,
-} from '../utils/index';
+import { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils/index';
 import { createCanvasFromMedia } from './createCanvas';
 import { imageToSquare } from './imageToSquare';
 import { TResolvedNetInput } from './types';
@@ -23,10 +21,7 @@ export class NetInput {
 
   private _inputSize: number
 
-  constructor(
-    inputs: Array<TResolvedNetInput>,
-    treatAsBatchInput: boolean = false,
-  ) {
+  constructor(inputs: Array<TResolvedNetInput>, treatAsBatchInput: boolean = false) {
     if (!Array.isArray(inputs)) {
       throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);
     }
@@ -131,13 +126,11 @@ export class NetInput {
         const input = this.getInput(batchIdx);
 
         if (input instanceof tf.Tensor) {
-          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.
-          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>();
-          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.
+          let imgTensor = isTensor4D(input) ? input : tf.expandDims(input);
           imgTensor = padToSquare(imgTensor, isCenterInputs);
 
           if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
-            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);
+            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize], false, false);
           }
 
           return imgTensor.as3D(inputSize, inputSize, 3);
@@ -150,9 +143,7 @@ export class NetInput {
         throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);
       });
 
-      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)
       const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);
-      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);
 
       return batchTensor;
     });

src/dom/extractFaces.ts

@@ -14,10 +14,7 @@ import { TNetInput } from './types';
  * @param detections The face detection results or face bounding boxes for that image.
  * @returns The Canvases of the corresponding image region for each detected face.
  */
-export async function extractFaces(
-  input: TNetInput,
-  detections: Array<FaceDetection | Rect>,
-): Promise<HTMLCanvasElement[]> {
+export async function extractFaces(input: TNetInput, detections: Array<FaceDetection | Rect>): Promise<HTMLCanvasElement[]> {
   const { Canvas } = env.getEnv();
 
   let canvas = input as HTMLCanvasElement;
@@ -36,16 +33,13 @@ export async function extractFaces(
   }
 
   const ctx = getContext2dOrThrow(canvas);
-  const boxes = detections.map(
-    (det) => (det instanceof FaceDetection
+  const boxes = detections
+    .map((det) => (det instanceof FaceDetection
       ? det.forSize(canvas.width, canvas.height).box.floor()
-      : det),
-  )
+      : det))
     .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));
 
-  return boxes.map(({
-    x, y, width, height,
-  }) => {
+  return boxes.map(({ x, y, width, height }) => {
     const faceImg = createCanvas({ width, height });
     if (width > 0 && height > 0) getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);
     return faceImg;

src/draw/DrawBox.ts

@@ -1,9 +1,7 @@
 /* eslint-disable max-classes-per-file */
 import { Box, IBoundingBox, IRect } from '../classes/index';
 import { getContext2dOrThrow } from '../dom/getContext2dOrThrow';
-import {
-  AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,
-} from './DrawTextField';
+import { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';
 
 export interface IDrawBoxOptions {
   boxColor?: string

src/env/isNodejs.ts

@@ -2,7 +2,5 @@ export function isNodejs(): boolean {
   return typeof global === 'object'
     && typeof require === 'function'
     && typeof module !== 'undefined'
-    // issues with gatsby.js: module.exports is undefined
-    // && !!module.exports
     && typeof process !== 'undefined' && !!process.version;
 }

src/faceFeatureExtractor/extractorsFactory.ts

@@ -1,9 +1,4 @@
-import {
-  extractConvParamsFactory,
-  extractSeparableConvParamsFactory,
-  ExtractWeightsFunction,
-  ParamMapping,
-} from '../common/index';
+import { extractConvParamsFactory, extractSeparableConvParamsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';
 import { DenseBlock3Params, DenseBlock4Params } from './types';
 
 export function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {

src/faceProcessor/FaceProcessor.ts

@@ -2,11 +2,7 @@ import * as tf from '../../dist/tfjs.esm';
 
 import { fullyConnectedLayer } from '../common/fullyConnectedLayer';
 import { NetInput } from '../dom/index';
-import {
-  FaceFeatureExtractorParams,
-  IFaceFeatureExtractor,
-  TinyFaceFeatureExtractorParams,
-} from '../faceFeatureExtractor/types';
+import { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';
 import { NeuralNetwork } from '../NeuralNetwork';
 import { extractParams } from './extractParams';
 import { extractParamsFromWeightMap } from './extractParamsFromWeightMap';

src/faceProcessor/extractParamsFromWeightMap.ts

@@ -1,8 +1,6 @@
 import * as tf from '../../dist/tfjs.esm';
 
-import {
-  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,
-} from '../common/index';
+import { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';
 import { NetParams } from './types';
 
 export function extractParamsFromWeightMap(

src/faceRecognitionNet/extractParams.ts

@@ -1,12 +1,8 @@
 import * as tf from '../../dist/tfjs.esm';
 
-import {
-  ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,
-} from '../common/index';
+import { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';
 import { isFloat } from '../utils/index';
-import {
-  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,
-} from './types';
+import { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';
 
 function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {
   function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {

src/globalApi/ComputeFaceDescriptorsTasks.ts

@@ -6,14 +6,8 @@ import { WithFaceLandmarks } from '../factories/WithFaceLandmarks';
 import { ComposableTask } from './ComposableTask';
 import { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';
 import { nets } from './nets';
-import {
-  PredictAllAgeAndGenderWithFaceAlignmentTask,
-  PredictSingleAgeAndGenderWithFaceAlignmentTask,
-} from './PredictAgeAndGenderTask';
-import {
-  PredictAllFaceExpressionsWithFaceAlignmentTask,
-  PredictSingleFaceExpressionsWithFaceAlignmentTask,
-} from './PredictFaceExpressionsTask';
+import { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';
+import { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';
 
 export class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {
   constructor(

src/globalApi/DetectFaceLandmarksTasks.ts

@@ -10,14 +10,8 @@ import { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFac
 import { ComposableTask } from './ComposableTask';
 import { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';
 import { nets } from './nets';
-import {
-  PredictAllAgeAndGenderWithFaceAlignmentTask,
-  PredictSingleAgeAndGenderWithFaceAlignmentTask,
-} from './PredictAgeAndGenderTask';
-import {
-  PredictAllFaceExpressionsWithFaceAlignmentTask,
-  PredictSingleFaceExpressionsWithFaceAlignmentTask,
-} from './PredictFaceExpressionsTask';
+import { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';
+import { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';
 
 export class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {
   constructor(

src/globalApi/PredictAgeAndGenderTask.ts

@@ -11,12 +11,7 @@ import { ComposableTask } from './ComposableTask';
 import { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';
 import { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';
 import { nets } from './nets';
-import {
-  PredictAllFaceExpressionsTask,
-  PredictAllFaceExpressionsWithFaceAlignmentTask,
-  PredictSingleFaceExpressionsTask,
-  PredictSingleFaceExpressionsWithFaceAlignmentTask,
-} from './PredictFaceExpressionsTask';
+import { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';
 
 export class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {
   constructor(

src/globalApi/PredictFaceExpressionsTask.ts

@@ -10,12 +10,7 @@ import { ComposableTask } from './ComposableTask';
 import { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';
 import { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';
 import { nets } from './nets';
-import {
-  PredictAllAgeAndGenderTask,
-  PredictAllAgeAndGenderWithFaceAlignmentTask,
-  PredictSingleAgeAndGenderTask,
-  PredictSingleAgeAndGenderWithFaceAlignmentTask,
-} from './PredictAgeAndGenderTask';
+import { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';
 
 export class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {
   constructor(

src/ops/padToSquare.ts

@@ -41,7 +41,6 @@ export function padToSquare(
       paddingTensorAppend,
     ]
       .filter((t) => !!t)
-      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]
       .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];
     return tf.concat(tensorsToStack, paddingAxis);
   });

src/ssdMobilenetv1/SsdMobilenetv1.ts

@@ -27,7 +27,7 @@ export class SsdMobilenetv1 extends NeuralNetwork<NetParams> {
 
     return tf.tidy(() => {
       const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');
-      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;
+      const x = tf.sub(tf.div(batchTensor, 127.5), 1) as tf.Tensor4D; // input is normalized -1..1
       const features = mobileNetV1(x, params.mobilenetv1);
 
       const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);
@@ -40,10 +40,7 @@ export class SsdMobilenetv1 extends NeuralNetwork<NetParams> {
     return this.forwardInput(await toNetInput(input));
   }
 
-  public async locateFaces(
-    input: TNetInput,
-    options: ISsdMobilenetv1Options = {},
-  ): Promise<FaceDetection[]> {
+  public async locateFaces(input: TNetInput, options: ISsdMobilenetv1Options = {}): Promise<FaceDetection[]> {
     const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);
 
     const netInput = await toNetInput(input);

src/ssdMobilenetv1/extractParams.ts

@@ -1,11 +1,7 @@
 import * as tf from '../../dist/tfjs.esm';
 
-import {
-  ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,
-} from '../common/index';
-import {
-  MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,
-} from './types';
+import { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common/index';
+import { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';
 
 function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {
   function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {

src/ssdMobilenetv1/extractParamsFromWeightMap.ts

@@ -1,12 +1,8 @@
 import * as tf from '../../dist/tfjs.esm';
 
-import {
-  ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,
-} from '../common/index';
+import { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';
 import { isTensor3D } from '../utils/index';
-import {
-  BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,
-} from './types';
+import { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';
 
 function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
   const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);

src/ssdMobilenetv1/mobileNetV1.ts

@@ -5,11 +5,7 @@ import { MobileNetV1 } from './types';
 
 const epsilon = 0.0010000000474974513;
 
-function depthwiseConvLayer(
-  x: tf.Tensor4D,
-  params: MobileNetV1.DepthwiseConvParams,
-  strides: [number, number],
-) {
+function depthwiseConvLayer(x: tf.Tensor4D, params: MobileNetV1.DepthwiseConvParams, strides: [number, number]) {
   return tf.tidy(() => {
     let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');
     out = tf.batchNorm<tf.Rank.R4>(

src/ssdMobilenetv1/nonMaxSuppression.ts

@@ -12,15 +12,12 @@ function IOU(boxes: tf.Tensor2D, i: number, j: number) {
   const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);
   const areaI = (ymaxI - yminI) * (xmaxI - xminI);
   const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
-  if (areaI <= 0 || areaJ <= 0) {
-    return 0.0;
-  }
+  if (areaI <= 0 || areaJ <= 0) return 0.0;
   const intersectionYmin = Math.max(yminI, yminJ);
   const intersectionXmin = Math.max(xminI, xminJ);
   const intersectionYmax = Math.min(ymaxI, ymaxJ);
   const intersectionXmax = Math.min(xmaxI, xmaxJ);
-  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)
-      * Math.max(intersectionXmax - intersectionXmin, 0.0);
+  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) * Math.max(intersectionXmax - intersectionXmin, 0.0);
   return intersectionArea / (areaI + areaJ - intersectionArea);
 }
 
@@ -32,10 +29,7 @@ export function nonMaxSuppression(
   scoreThreshold: number,
 ): number[] {
   const numBoxes = boxes.shape[0];
-  const outputSize = Math.min(
-    maxOutputSize,
-    numBoxes,
-  );
+  const outputSize = Math.min(maxOutputSize, numBoxes);
 
   const candidates = scores
     .map((score, boxIndex) => ({ score, boxIndex }))

src/ssdMobilenetv1/outputLayer.ts

@@ -13,17 +13,11 @@ function getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {
     tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),
     tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),
   ];
-  return {
-    sizes,
-    centers,
-  };
+  return { sizes, centers };
 }
 
 function decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {
-  const {
-    sizes,
-    centers,
-  } = getCenterCoordinatesAndSizesLayer(x0);
+  const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0);
 
   const vec = tf.unstack(tf.transpose(x1, [1, 0]));
   const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));
@@ -42,11 +36,7 @@ function decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {
   );
 }
 
-export function outputLayer(
-  boxPredictions: tf.Tensor4D,
-  classPredictions: tf.Tensor4D,
-  params: OutputLayerParams,
-) {
+export function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams) {
   return tf.tidy(() => {
     const batchSize = boxPredictions.shape[0];
 
@@ -54,25 +44,16 @@ export function outputLayer(
       tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,
       tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,
     );
-    boxes = tf.reshape(
-      boxes,
-      [batchSize, (boxes.shape[0] / batchSize), 4],
-    );
+    boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);
 
     const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
     let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;
 
-    scores = tf.reshape(
-      scores,
-      [batchSize, scores.shape[1] as number],
-    );
+    scores = tf.reshape(scores, [batchSize, scores.shape[1] as number]);
 
     const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];
     const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];
 
-    return {
-      boxes: boxesByBatch,
-      scores: scoresByBatch,
-    };
+    return { boxes: boxesByBatch, scores: scoresByBatch };
   });
 }

src/xception/extractParamsFromWeightMap.ts

@@ -1,11 +1,6 @@
 import * as tf from '../../dist/tfjs.esm';
 
-import {
-  disposeUnusedWeightTensors,
-  extractWeightEntryFactory,
-  loadSeparableConvParamsFactory,
-  ParamMapping,
-} from '../common/index';
+import { disposeUnusedWeightTensors, extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';
 import { loadConvParamsFactory } from '../common/loadConvParamsFactory';
 import { range } from '../utils/index';
 import { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';