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object_detection/index.html

@@ -49,6 +49,21 @@
             </div>
           </div>
         </div>
+        <div class="row mb-2 align-items-center">
+          <div class="col-1 col-md-1">
+            <span class="nowrap">Data Type</span>
+          </div>
+          <div class="col-md-auto">
+            <div class="btn-group-toggle" data-toggle="buttons" id="dataTypeBtns">
+              <label class="btn btn-outline-info" id="float32Label" active>
+                <input type="radio" name="layout" id="float32" autocomplete="off" checked>Float32
+              </label>
+              <label class="btn btn-outline-info" id="float16Label">
+                <input type="radio" name="layout" id="float16" autocomplete="off">Float16
+              </label>
+            </div>
+          </div>
+        </div>
         <div class="row align-items-center">
           <div class="col-1 col-md-1">
             <span>Model</span>

object_detection/main.js

@@ -13,7 +13,9 @@ const imgElement = document.getElementById('feedElement');
 imgElement.src = './images/test.jpg';
 const camElement = document.getElementById('feedMediaElement');
 let modelName = '';
+let modelId = '';
 let layout = 'nhwc';
+let dataType = 'float32';
 let instanceType = modelName + layout;
 let rafReq;
 let isFirstTimeLoad = true;
@@ -33,6 +35,19 @@ let lastBackend = '';
 let stopRender = true;
 let isRendering = false;
 const disabledSelectors = ['#tabs > li', '.btn'];
+const modelIds = ['ssdmobilenetv1', 'tinyyolov2'];
+const modelList = {
+  'cpu': {
+    'float32': modelIds,
+  },
+  'gpu': {
+    'float32': modelIds,
+    'float16': modelIds,
+  },
+  'npu': {
+    'float16': ['ssdmobilenetv1'],
+  },
+};
 
 async function fetchLabels(url) {
   const response = await fetch(url);
@@ -53,18 +68,56 @@ $('#backendBtns .btn').on('change', async (e) => {
   if (inputType === 'camera') {
     await stopCamRender();
   }
-  layout = utils.getDefaultLayout($(e.target).attr('id'));
-  await main();
+  const backendId = $(e.target).attr('id');
+  layout = utils.getDefaultLayout(backendId);
+  [backend, deviceType] = backendId.split('_');
+  // Only show the supported models for each deviceType. Now fp16 nchw models
+  // are only supported on gpu/npu.
+  if (backendId == 'webnn_gpu') {
+    ui.handleBtnUI('#float16Label', false);
+    ui.handleBtnUI('#float32Label', false);
+    utils.displayAvailableModels(modelList, modelIds, deviceType, dataType);
+  } else if (backendId == 'webnn_npu') {
+    ui.handleBtnUI('#float16Label', false);
+    ui.handleBtnUI('#float32Label', true);
+    $('#float16').click();
+    utils.displayAvailableModels(modelList, modelIds, deviceType, 'float16');
+  } else {
+    ui.handleBtnUI('#float16Label', true);
+    ui.handleBtnUI('#float32Label', false);
+    $('#float32').click();
+    utils.displayAvailableModels(modelList, modelIds, deviceType, 'float32');
+  }
+
+  // Uncheck selected model
+  if (modelId != '') {
+    $(`#${modelId}`).parent().removeClass('active');
+  }
 });
 
 $('#modelBtns .btn').on('change', async (e) => {
   if (inputType === 'camera') {
     await stopCamRender();
   }
-  modelName = $(e.target).attr('id');
+
+  modelId = $(e.target).attr('id');
+  modelName = modelId;
+  if (dataType == 'float16') {
+    modelName += 'fp16';
+  }
+
   await main();
 });
 
+$('#dataTypeBtns .btn').on('change', async (e) => {
+  dataType = $(e.target).attr('id');
+  utils.displayAvailableModels(modelList, modelIds, deviceType, dataType);
+  // Uncheck selected model
+  if (modelId != '') {
+    $(`#${modelId}`).parent().removeClass('active');
+  }
+});
+
 // Click trigger to do inference with <img> element
 $('#img').click(async () => {
   if (inputType === 'camera') {
@@ -146,7 +199,7 @@ async function drawOutput(inputElement, outputs, labels) {
   $('#inferenceresult').show();
 
   // Draw output for SSD Mobilenet V1 model
-  if (modelName === 'ssdmobilenetv1') {
+  if (modelName.includes('ssdmobilenetv1')) {
     const anchors = SsdDecoder.generateAnchors({});
     SsdDecoder.decodeOutputBoxTensor({}, outputs.boxes, anchors);
     let [totalDetections, boxesList, scoresList, classesList] =
@@ -181,8 +234,10 @@ function showPerfResult(medianComputeTime = undefined) {
 function constructNetObject(type) {
   const netObject = {
     'tinyyolov2nchw': new TinyYoloV2Nchw(),
+    'tinyyolov2fp16nchw': new TinyYoloV2Nchw('float16'),
     'tinyyolov2nhwc': new TinyYoloV2Nhwc(),
     'ssdmobilenetv1nchw': new SsdMobilenetV1Nchw(),
+    'ssdmobilenetv1fp16nchw': new SsdMobilenetV1Nchw('float16'),
     'ssdmobilenetv1nhwc': new SsdMobilenetV1Nhwc(),
   };
 
@@ -192,8 +247,6 @@ function constructNetObject(type) {
 async function main() {
   try {
     if (modelName === '') return;
-    [backend, deviceType] =
-        $('input[name="backend"]:checked').attr('id').split('_');
     ui.handleClick(disabledSelectors, true);
     if (isFirstTimeLoad) $('#hint').hide();
     let start;
@@ -218,7 +271,7 @@ async function main() {
       netInstance = constructNetObject(instanceType);
       inputOptions = netInstance.inputOptions;
       labels = await fetchLabels(inputOptions.labelUrl);
-      if (modelName === 'tinyyolov2') {
+      if (modelName.includes('tinyyolov2')) {
         outputs = {
           'output': new Float32Array(
               utils.sizeOfShape(netInstance.outputDimensions)),

object_detection/ssd_mobilenetv1_nchw.js

@@ -4,10 +4,10 @@ import {buildConstantByNpy, computePadding2DForAutoPad, weightsOrigin} from '../
 
 // SSD MobileNet V1 model with 'nchw' layout, trained on the COCO dataset.
 export class SsdMobilenetV1Nchw {
-  constructor() {
+  constructor(dataType = 'float32') {
     this.context_ = null;
     this.deviceType_ = null;
-    this.targetDataType_ = 'float32';
+    this.targetDataType_ = dataType;
     this.model_ = null;
     this.builder_ = null;
     this.graph_ = null;
@@ -86,9 +86,6 @@ ${nameArray[1]}_BatchNorm_batchnorm`;
   async load(contextOptions) {
     this.context_ = await navigator.ml.createContext(contextOptions);
     this.deviceType_ = contextOptions.deviceType;
-    if (this.deviceType_ == 'gpu' || this.deviceType_ == 'npu') {
-      this.targetDataType_ = 'float16';
-    }
     this.builder_ = new MLGraphBuilder(this.context_);
     let input = this.builder_.input('input', {
       dataType: 'float32',

object_detection/tiny_yolov2_nchw.js

@@ -4,12 +4,12 @@ import {buildConstantByNpy, computePadding2DForAutoPad, weightsOrigin} from '../
 
 // Tiny Yolo V2 model with 'nchw' layout, trained on the Pascal VOC dataset.
 export class TinyYoloV2Nchw {
-  constructor() {
+  constructor(dataType = 'float32') {
     this.context_ = null;
     this.builder_ = null;
     this.graph_ = null;
     this.deviceType_ = null;
-    this.targetDataType_ = 'float32';
+    this.targetDataType_ = dataType;
     this.weightsUrl_ = weightsOrigin() +
       '/test-data/models/tiny_yolov2_nchw/weights/';
     this.inputOptions = {
@@ -60,9 +60,6 @@ export class TinyYoloV2Nchw {
   async load(contextOptions) {
     this.context_ = await navigator.ml.createContext(contextOptions);
     this.deviceType_ = contextOptions.deviceType;
-    if (this.deviceType_ == 'gpu' || this.deviceType_ == 'npu') {
-      this.targetDataType_ = 'float16';
-    }
     this.builder_ = new MLGraphBuilder(this.context_);
     let image = this.builder_.input('input', {
       dataType: 'float32',

test-data/models/tiny_yolov2_nchw/README.md

@@ -1 +1 @@
-`.npy` files under this folder are downloaded from model of [Tiny YOLO v2](https://github.com/onnx/models/blob/master/vision/object_detection_segmentation/tiny-yolov2/model/tinyyolov2-8.tar.gz), which is licensed under the Apache 2.0.
+`.npy` files under this folder are downloaded from optimized model (fuse Conv + BatchNormalization) of [Tiny YOLO v2](https://github.com/onnx/models/blob/master/vision/object_detection_segmentation/tiny-yolov2/model/tinyyolov2-8.tar.gz), which is licensed under the Apache 2.0.