diff --git a/object_detection/index.html b/object_detection/index.html index e6f34499..bc496b34 100644 --- a/object_detection/index.html +++ b/object_detection/index.html @@ -49,6 +49,21 @@ +
+
+ Data Type +
+
+
+ + +
+
+
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 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