Fix lint issues

face_recognition/facenet_nchw.js

@@ -142,9 +142,9 @@ export class FaceNetNchw {
     this.context_ = await navigator.ml.createContext(contextOptions);
     this.builder_ = new MLGraphBuilder(this.context_);
     const inputDesc = {
-        dataType: 'float32',
-        dimensions: this.inputOptions.inputShape,
-        shape: this.inputOptions.inputShape,
+      dataType: 'float32',
+      dimensions: this.inputOptions.inputShape,
+      shape: this.inputOptions.inputShape,
     };
     const input = this.builder_.input('input', inputDesc);
     inputDesc.usage = MLTensorUsage.WRITE;

nnotepad/.eslintrc.js

@@ -1,4 +1,5 @@
 module.exports = {
   env: {'es6': true, 'browser': true, 'jquery': false, 'node': true},
   parserOptions: {ecmaVersion: 2021, sourceType: 'module'},
+  globals: {'MLTensorUsage': 'readonly'},
 };

nnotepad/js/nnotepad.js

@@ -597,11 +597,11 @@ export class NNotepad {
       throw new DispatchError(`${ex.name} : ${ex.message}`);
     }
 
-    for (const name in outputBuffers) {
+    for (const [name, outputBuffer] of Object.entries(outputBuffers)) {
       const buffer = await context.readTensor(outputTensors[name]);
-      const instance = new outputBuffers[name].constructor(buffer);
-      outputBuffers[name].set(instance);
-    };
+      const instance = new outputBuffer.constructor(buffer);
+      outputBuffer.set(instance);
+    }
 
     function maybeProxyForFloat16Array(array) {
       return ('proxyForFloat16Array' in self) ?

nsnet2/denoiser.js

@@ -1,6 +1,6 @@
 import {NSNet2} from './nsnet2.js';
 import * as featurelib from './featurelib.js';
-import {sizeOfShape, getUrlParams, weightsOrigin} from '../common/utils.js';
+import {getUrlParams, weightsOrigin} from '../common/utils.js';
 
 export class Denoiser {
   constructor(batchSize, frames, sampleRate) {

nsnet2/nsnet2.js

@@ -61,7 +61,7 @@ export class NSNet2 {
     squeeze95Shape.splice(1, 1);
     const squeeze95 = this.builder_.reshape(gru93, squeeze95Shape);
     const initialState155 = this.builder_.input('initialState155', initialStateDesc);
-    
+
     initialStateDesc.usage = MLTensorUsage.WRITE;
     this.initialState92Tensor_ = await this.context_.createTensor(initialStateDesc);
     this.initialState155Tensor_ = await this.context_.createTensor(initialStateDesc);

object_detection/main.js

@@ -303,7 +303,7 @@ async function main() {
       let medianComputeTime;
 
       // Do warm up
-      let results = await netInstance.compute(inputBuffer);
+      const results = await netInstance.compute(inputBuffer);
 
       for (let i = 0; i < numRuns; i++) {
         start = performance.now();

object_detection/ssd_mobilenetv1_nchw.js

@@ -104,7 +104,7 @@ ${nameArray[1]}_BatchNorm_batchnorm`;
       shape: this.scoresShape_,
       usage: MLTensorUsage.READ,
     });
-    
+
     if (this.targetDataType_ === 'float16') {
       input = this.builder_.cast(input, 'float16');
     }
@@ -299,8 +299,10 @@ ${nameArray[1]}_BatchNorm_batchnorm`;
     };
     this.context_.dispatch(this.graph_, inputs, outputs);
     const results = {
-      'boxes': new Float32Array(await this.context_.readTensor(this.boxesTensor_)),
-      'scores': new Float32Array(await this.context_.readTensor(this.scoresTensor_)),
+      boxes: new Float32Array(
+          await this.context_.readTensor(this.boxesTensor_)),
+      scores: new Float32Array(
+          await this.context_.readTensor(this.scoresTensor_)),
     };
     return results;
   }

object_detection/ssd_mobilenetv1_nhwc.js

@@ -276,8 +276,10 @@ ${nameArray[1]}_BatchNorm_batchnorm`;
     };
     this.context_.dispatch(this.graph_, inputs, outputs);
     const results = {
-      'boxes': new Float32Array(await this.context_.readTensor(this.boxesTensor_)),
-      'scores': new Float32Array(await this.context_.readTensor(this.scoresTensor_)),
+      'boxes': new Float32Array(
+          await this.context_.readTensor(this.boxesTensor_)),
+      'scores': new Float32Array(
+          await this.context_.readTensor(this.scoresTensor_)),
     };
     return results;
   }

rnnoise/rnnoise.js

@@ -80,15 +80,17 @@ export class RNNoise {
         vadGruBData,
         [0, 3 * this.vadGruHiddenSize],
         [1, 3 * this.vadGruHiddenSize]);
-    
+
     const vadGruInitialHDesc = {
       dataType: 'float32',
       dimensions: [1, this.batchSize_, this.vadGruHiddenSize],
       shape: [1, this.batchSize_, this.vadGruHiddenSize],
     };
-    const vadGruInitialH = this.builder_.input('vadGruInitialH', vadGruInitialHDesc);
+    const vadGruInitialH = this.builder_.input(
+        'vadGruInitialH', vadGruInitialHDesc);
     vadGruInitialHDesc.usage = MLTensorUsage.WRITE;
-    this.vadGruInitialHTensor_ = await this.context_.createTensor(vadGruInitialHDesc);
+    this.vadGruInitialHTensor_ = await this.context_.createTensor(
+        vadGruInitialHDesc);
 
     const [vadGruYH, vadGruY] = this.builder_.gru(vadGruX,
         vadGruW, vadGruR, this.frames_, this.vadGruHiddenSize, {
@@ -119,9 +121,11 @@ export class RNNoise {
       dimensions: [1, this.batchSize_, this.noiseGruHiddenSize],
       shape: [1, this.batchSize_, this.noiseGruHiddenSize],
     };
-    const noiseGruInitialH = this.builder_.input('noiseGruInitialH', noiseGruInitialHDesc);
+    const noiseGruInitialH = this.builder_.input(
+        'noiseGruInitialH', noiseGruInitialHDesc);
     noiseGruInitialHDesc.usage = MLTensorUsage.WRITE;
-    this.noiseGruInitialHTensor_ = await this.context_.createTensor(noiseGruInitialHDesc);
+    this.noiseGruInitialHTensor_ = await this.context_.createTensor(
+        noiseGruInitialHDesc);
 
     const [noiseGruYH, noiseGruY] = this.builder_.gru(noiseGruX,
         noiseGruW, noiseGruR, this.frames_, this.noiseGruHiddenSize, {
@@ -146,15 +150,17 @@ export class RNNoise {
         denoiseGruBData,
         [0, 3 * this.denoiseGruHiddenSize],
         [1, 3 * this.denoiseGruHiddenSize]);
-    
+
     const denoiseGruInitialHDesc = {
       dataType: 'float32',
       dimensions: [1, this.batchSize_, this.denoiseGruHiddenSize],
       shape: [1, this.batchSize_, this.denoiseGruHiddenSize],
     };
-    const denoiseGruInitialH = this.builder_.input('denoiseGruInitialH', denoiseGruInitialHDesc);
+    const denoiseGruInitialH = this.builder_.input(
+        'denoiseGruInitialH', denoiseGruInitialHDesc);
     denoiseGruInitialHDesc.usage = MLTensorUsage.WRITE;
-    this.denoiseGruInitialHTensor_ = await this.context_.createTensor(denoiseGruInitialHDesc);
+    this.denoiseGruInitialHTensor_ = await this.context_.createTensor(
+        denoiseGruInitialHDesc);
 
     const [denoiseGruYH, denoiseGruY] = this.builder_.gru(denoiseGruX,
         denoiseGruW, denoiseGruR, this.frames_, this.denoiseGruHiddenSize, {
@@ -175,28 +181,39 @@ export class RNNoise {
         denoiseOutput0, denoiseOutputBias0);
     const denoiseOutput = this.builder_.sigmoid(biasedTensorName);
 
+    const denoiseOutputShape =
+        [this.batchSize_, this.frames_, this.gainsSize_];
     this.denoiseOutputTensor_ = await this.context_.createTensor({
       dataType: 'float32',
-      dimensions: [this.batchSize_, this.frames_, this.gainsSize_],
-      shape: [this.batchSize_, this.frames_, this.gainsSize_],
+      dimensions: denoiseOutputShape,
+      shape: denoiseOutputShape,
       usage: MLTensorUsage.READ,
     });
+    const vadGruYHOutputShape =
+        [this.vadGruNumDirections, this.batchSize_, this.vadGruHiddenSize];
     this.vadGruYHTensor_ = await this.context_.createTensor({
       dataType: 'float32',
-      dimensions: [this.vadGruNumDirections, this.batchSize_, this.vadGruHiddenSize],
-      shape: [this.vadGruNumDirections, this.batchSize_, this.vadGruHiddenSize],
+      dimensions: vadGruYHOutputShape,
+      shape: vadGruYHOutputShape,
       usage: MLTensorUsage.READ,
     });
+    const noiseGruYHOutputShape =
+        [this.noiseGruNumDirections, this.batchSize_, this.noiseGruHiddenSize];
     this.noiseGruYHTensor_ = await this.context_.createTensor({
       dataType: 'float32',
-      dimensions: [this.noiseGruNumDirections, this.batchSize_, this.noiseGruHiddenSize], 
-      shape: [this.noiseGruNumDirections, this.batchSize_, this.noiseGruHiddenSize],
+      dimensions: noiseGruYHOutputShape,
+      shape: noiseGruYHOutputShape,
       usage: MLTensorUsage.READ,
     });
+    const denoiseGruYHOutputShape = [
+      this.denoiseGruNumDirections,
+      this.batchSize_,
+      this.denoiseGruHiddenSize,
+    ];
     this.denoiseGruYHTensor_ = await this.context_.createTensor({
       dataType: 'float32',
-      dimensions: [this.denoiseGruNumDirections, this.batchSize_, this.denoiseGruHiddenSize],
-      shape: [this.denoiseGruNumDirections, this.batchSize_, this.denoiseGruHiddenSize],
+      dimensions: denoiseGruYHOutputShape,
+      shape: denoiseGruYHOutputShape,
       usage: MLTensorUsage.READ,
     });
 
@@ -209,9 +226,12 @@ export class RNNoise {
 
   async compute(inputs) {
     this.context_.writeTensor(this.inputTensor_, inputs.input);
-    this.context_.writeTensor(this.vadGruInitialHTensor_, inputs.vadGruInitialH);
-    this.context_.writeTensor(this.noiseGruInitialHTensor_, inputs.noiseGruInitialH);
-    this.context_.writeTensor(this.denoiseGruInitialHTensor_, inputs.denoiseGruInitialH);
+    this.context_.writeTensor(
+        this.vadGruInitialHTensor_, inputs.vadGruInitialH);
+    this.context_.writeTensor(
+        this.noiseGruInitialHTensor_, inputs.noiseGruInitialH);
+    this.context_.writeTensor(
+        this.denoiseGruInitialHTensor_, inputs.denoiseGruInitialH);
     const inputTensors = {
       'input': this.inputTensor_,
       'vadGruInitialH': this.vadGruInitialHTensor_,
@@ -226,10 +246,14 @@ export class RNNoise {
     };
     this.context_.dispatch(this.graph_, inputTensors, outputTensors);
     const results = {
-      'denoiseOutput': new Float32Array(await this.context_.readTensor(this.denoiseOutputTensor_)),
-      'vadGruYH': new Float32Array(await this.context_.readTensor(this.vadGruYHTensor_)),
-      'noiseGruYH': new Float32Array(await this.context_.readTensor(this.noiseGruYHTensor_)),
-      'denoiseGruYH': new Float32Array(await this.context_.readTensor(this.denoiseGruYHTensor_)),
+      'denoiseOutput': new Float32Array(
+          await this.context_.readTensor(this.denoiseOutputTensor_)),
+      'vadGruYH': new Float32Array(
+          await this.context_.readTensor(this.vadGruYHTensor_)),
+      'noiseGruYH': new Float32Array(
+          await this.context_.readTensor(this.noiseGruYHTensor_)),
+      'denoiseGruYH': new Float32Array(
+          await this.context_.readTensor(this.denoiseGruYHTensor_)),
     };
     return results;
   }

semantic_segmentation/deeplabv3_mnv2_nchw.js

@@ -104,7 +104,7 @@ export class DeepLabV3MNV2Nchw {
       shape: this.outputShape,
       usage: MLTensorUsage.READ,
     });
-  
+
     const conv0 = this.buildConv_(
         input, ['MobilenetV2_Conv_Conv2D', '', '551'], 'relu6', {strides, padding: [1, 1, 1, 1]});
     const conv1 = this.buildConv_(