Model
diff --git a/object_detection/main.js b/object_detection/main.js
index 8ed9b8fe..504ad73e 100644
--- a/object_detection/main.js
+++ b/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
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)),
diff --git a/object_detection/ssd_mobilenetv1_nchw.js b/object_detection/ssd_mobilenetv1_nchw.js
index 2edb0e14..a8726525 100644
--- a/object_detection/ssd_mobilenetv1_nchw.js
+++ b/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',
diff --git a/object_detection/tiny_yolov2_nchw.js b/object_detection/tiny_yolov2_nchw.js
index 6ddcbcdb..32b35eb3 100644
--- a/object_detection/tiny_yolov2_nchw.js
+++ b/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',
diff --git a/test-data/models/tiny_yolov2_nchw/README.md b/test-data/models/tiny_yolov2_nchw/README.md
index 50748dee..b69eb920 100644
--- a/test-data/models/tiny_yolov2_nchw/README.md
+++ b/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.
\ No newline at end of file
+`.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.
\ No newline at end of file
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