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Merge pull request openvinotoolkit#62 from almilosz/almilosz/infer-async
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Rename inferAsync()to infer()
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@almilosz
almilosz authored Nov 6, 2023
2 parents 20cb0f7 + 582736e commit 224d17a
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Showing 11 changed files with 70 additions and 70 deletions.
2 changes: 1 addition & 1 deletion samples/js/node/classification_sample_async/classification_sample_async.js
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Expand Up @@ -88,7 +88,7 @@ async function main(modelPath, images, deviceName) {
const inferRequest = compiledModel.createInferRequest();

const promises = preprocessedImages.map((tensorData, i) => {
const inferPromise = inferRequest.inferAsync([new ov.Tensor(ov.element.u8, tensorShape, tensorData)]);
const inferPromise = inferRequest.infer([new ov.Tensor(ov.element.u8, tensorShape, tensorData)]);

inferPromise.then(result =>
completionCallback(result[outputName], images[i]));
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2 changes: 1 addition & 1 deletion samples/js/node/hello_classification/hello_classification.js
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Expand Up @@ -59,7 +59,7 @@ async function main(modelPath, imagePath, deviceName) {
console.log('Starting inference in synchronous mode');
const inferRequest = compiledModel.createInferRequest();
inferRequest.setInputTensor(inputTensor);
inferRequest.infer();
inferRequest.inferSync();

//----------------- Step 7. Process output -----------------------------------
const outputLayer = compiledModel.outputs[0];
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2 changes: 1 addition & 1 deletion samples/js/node/hello_reshape_ssd/hello_reshape_ssd.js
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Expand Up @@ -68,7 +68,7 @@ async function main(modelPath, imagePath, deviceName) {
console.log('Starting inference in synchronous mode');
const inferRequest = compiledModel.createInferRequest();
inferRequest.setInputTensor(inputTensor);
inferRequest.infer();
inferRequest.inferSync();

//----------------- Step 7. Process output -----------------------------------
const outputLayer = compiledModel.outputs[0];
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16 changes: 8 additions & 8 deletions samples/js/node/notebooks/001-hello-world.nnb
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Expand Up @@ -17,7 +17,7 @@
{
"language": "javascript",
"source": [
"const { cv } = require('opencv-wasm');\nconst { display } = require('node-kernel');\nconst { getImageData, displayImage, downloadFile } = require('../helpers.js');\n\nconst { addon: ov } = require('openvinojs-node');"
"const { cv } = require('opencv-wasm');\nconst { display } = require('node-kernel');\nconst { getImageData, displayImage, downloadFile } = require('../helpers.js');\n\nconst { addon: ov } = require('openvinojs-node');\n"
],
"outputs": []
},
Expand All @@ -31,7 +31,7 @@
{
"language": "javascript",
"source": [
"const baseArtifactsDir = '../../assets/models';\n\nconst modelName = 'v3-small_224_1.0_float';\nconst modelXMLName = `${modelName}.xml`;\nconst modelBINName = `${modelName}.bin`;\n\nconst modelXMLPath = baseArtifactsDir + '/' + modelXMLName;\n\nconst baseURL = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/mobelinet-v3-tf/FP32/';\n\nawait downloadFile(baseURL + modelXMLName, modelXMLName, baseArtifactsDir);\nawait downloadFile(baseURL + modelBINName, modelBINName, baseArtifactsDir);"
"const baseArtifactsDir = '../../assets/models';\n\nconst modelName = 'v3-small_224_1.0_float';\nconst modelXMLName = `${modelName}.xml`;\nconst modelBINName = `${modelName}.bin`;\n\nconst modelXMLPath = baseArtifactsDir + '/' + modelXMLName;\n\nconst baseURL = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/mobelinet-v3-tf/FP32/';\n\nawait downloadFile(baseURL + modelXMLName, modelXMLName, baseArtifactsDir);\nawait downloadFile(baseURL + modelBINName, modelBINName, baseArtifactsDir);\n"
],
"outputs": [
{
Expand Down Expand Up @@ -59,7 +59,7 @@
{
"language": "javascript",
"source": [
"const imgUrl = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg';\nconst classesUrl = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/datasets/imagenet/imagenet_class_index.json';\n\nawait downloadFile(imgUrl, 'coco.jpg', '../../assets/images');\nawait downloadFile(classesUrl, 'imagenet_class_index.json', '../../assets/datasets');"
"const imgUrl = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg';\nconst classesUrl = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/datasets/imagenet/imagenet_class_index.json';\n\nawait downloadFile(imgUrl, 'coco.jpg', '../../assets/images');\nawait downloadFile(classesUrl, 'imagenet_class_index.json', '../../assets/datasets');\n"
],
"outputs": [
{
Expand Down Expand Up @@ -87,7 +87,7 @@
{
"language": "javascript",
"source": [
"const core = new ov.Core();\nconst model = await core.readModel(modelXMLPath);\nconst compiledModel = await core.compileModel(model, 'CPU');\n\nconst outputLayer = compiledModel.outputs[0];"
"const core = new ov.Core();\nconst model = await core.readModel(modelXMLPath);\nconst compiledModel = await core.compileModel(model, 'CPU');\n\nconst outputLayer = compiledModel.outputs[0];\n"
],
"outputs": []
},
Expand All @@ -101,7 +101,7 @@
{
"language": "javascript",
"source": [
"const imgData = await getImageData('../../assets/images/coco.jpg');\n\n// Use opencv-wasm to preprocess image.\nconst originalImage = cv.matFromImageData(imgData);\nconst image = new cv.Mat();\n// The MobileNet model expects images in RGB format.\ncv.cvtColor(originalImage, image, cv.COLOR_RGBA2RGB);\n// Resize to MobileNet image shape.\ncv.resize(image, image, new cv.Size(224, 224));\n\ndisplayImage(imgData, display);"
"const imgData = await getImageData('../../assets/images/coco.jpg');\n\n// Use opencv-wasm to preprocess image.\nconst originalImage = cv.matFromImageData(imgData);\nconst image = new cv.Mat();\n// The MobileNet model expects images in RGB format.\ncv.cvtColor(originalImage, image, cv.COLOR_RGBA2RGB);\n// Resize to MobileNet image shape.\ncv.resize(image, image, new cv.Size(224, 224));\n\ndisplayImage(imgData, display);\n"
],
"outputs": [
{
Expand All @@ -124,7 +124,7 @@
{
"language": "javascript",
"source": [
"const tensorData = new Float32Array(image.data);\nconst tensor = new ov.Tensor(ov.element.f32, Int32Array.from([1, 224, 224, 3]), tensorData);"
"const tensorData = new Float32Array(image.data);\nconst tensor = new ov.Tensor(ov.element.f32, Int32Array.from([1, 224, 224, 3]), tensorData);\n"
],
"outputs": []
},
Expand All @@ -138,14 +138,14 @@
{
"language": "javascript",
"source": [
"const inferRequest = compiledModel.createInferRequest();\ninferRequest.setInputTensor(tensor);\ninferRequest.infer();\n\nconst resultInfer = inferRequest.getTensor(outputLayer);\nconst resultIndex = resultInfer.data.indexOf(Math.max(...resultInfer.data));"
"const inferRequest = compiledModel.createInferRequest();\ninferRequest.setInputTensor(tensor);\ninferRequest.inferSync();\n\nconst resultInfer = inferRequest.getTensor(outputLayer);\nconst resultIndex = resultInfer.data.indexOf(Math.max(...resultInfer.data));\n"
],
"outputs": []
},
{
"language": "javascript",
"source": [
"const imagenetClassesMap = require('../../assets/datasets/imagenet_class_index.json');\nconst imagenetClasses = ['background', ...Object.values(imagenetClassesMap)];\n\nconsole.log(`Result: ${imagenetClasses[resultIndex][1]}`);"
"const imagenetClassesMap = require('../../assets/datasets/imagenet_class_index.json');\nconst imagenetClasses = ['background', ...Object.values(imagenetClassesMap)];\n\nconsole.log(`Result: ${imagenetClasses[resultIndex][1]}`);\n"
],
"outputs": [
{
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14 changes: 7 additions & 7 deletions samples/js/node/notebooks/003-hello-segmentation.nnb
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Expand Up @@ -17,7 +17,7 @@
{
"language": "typescript",
"source": [
"const {\n getImageData, \n displayArrayAsImage, \n arrayToImageData,\n transform,\n downloadFile,\n} = require('../helpers');\n\nconst { cv } = require('opencv-wasm');\nconst { display } = require('node-kernel');\n\nconst { addon: ov } = require('openvinojs-node'); "
"const {\n getImageData, \n displayArrayAsImage, \n arrayToImageData,\n transform,\n downloadFile,\n} = require('../helpers');\n\nconst { cv } = require('opencv-wasm');\nconst { display } = require('node-kernel');\n\nconst { addon: ov } = require('openvinojs-node'); \n"
],
"outputs": []
},
Expand All @@ -31,7 +31,7 @@
{
"language": "typescript",
"source": [
"const baseArtifactsDir = '../../assets/models';\n\nconst modelName = 'road-segmentation-adas-0001';\nconst modelXMLName = `${modelName}.xml`;\nconst modelBINName = `${modelName}.bin`;\n\nconst modelXMLPath = baseArtifactsDir + '/' + modelXMLName;\n\nconst baseURL = 'https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.3/models_bin/1/road-segmentation-adas-0001/FP32/';\n\n\nawait downloadFile(baseURL + modelXMLName, modelXMLName, baseArtifactsDir);\nawait downloadFile(baseURL + modelBINName, modelBINName, baseArtifactsDir);"
"const baseArtifactsDir = '../../assets/models';\n\nconst modelName = 'road-segmentation-adas-0001';\nconst modelXMLName = `${modelName}.xml`;\nconst modelBINName = `${modelName}.bin`;\n\nconst modelXMLPath = baseArtifactsDir + '/' + modelXMLName;\n\nconst baseURL = 'https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.3/models_bin/1/road-segmentation-adas-0001/FP32/';\n\n\nawait downloadFile(baseURL + modelXMLName, modelXMLName, baseArtifactsDir);\nawait downloadFile(baseURL + modelBINName, modelBINName, baseArtifactsDir);\n"
],
"outputs": [
{
Expand Down Expand Up @@ -59,7 +59,7 @@
{
"language": "typescript",
"source": [
"const baseImagesDir = '../../assets/images';\nconst imgUrl = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/empty_road_mapillary.jpg';\n\nawait downloadFile(imgUrl, 'empty_road_mapillary.jpg', baseImagesDir);"
"const baseImagesDir = '../../assets/images';\nconst imgUrl = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/empty_road_mapillary.jpg';\n\nawait downloadFile(imgUrl, 'empty_road_mapillary.jpg', baseImagesDir);\n"
],
"outputs": [
{
Expand All @@ -86,7 +86,7 @@
{
"language": "typescript",
"source": [
"const core = new ov.Core();\nconst model = await core.readModel(modelXMLPath);\nconst compiledModel = await core.compileModel(model, 'CPU');\n\nconst inputLayer = compiledModel.input(0);\nconst outputLayer = compiledModel.output(0);"
"const core = new ov.Core();\nconst model = await core.readModel(modelXMLPath);\nconst compiledModel = await core.compileModel(model, 'CPU');\n\nconst inputLayer = compiledModel.input(0);\nconst outputLayer = compiledModel.output(0);\n"
],
"outputs": []
},
Expand All @@ -100,7 +100,7 @@
{
"language": "typescript",
"source": [
"const imgData = await getImageData('../../assets/images/empty_road_mapillary.jpg');\n\nconst originalImage = cv.matFromImageData(imgData);\nconst { cols: originalWidth, rows: originalHeight } = originalImage;\n\nconst image = new cv.Mat();\ncv.cvtColor(originalImage, image, cv.COLOR_RGBA2RGB);\ncv.cvtColor(image, image, cv.COLOR_BGR2RGB); \n\nconst [B, C, H, W] = inputLayer.shape;\n\ncv.resize(image, image, new cv.Size(W, H));\n\nconst inputImage = transform(image.data, { width: W, height: H }, [0, 1, 2]); // NHWC to NCHW\n\ndisplayArrayAsImage(originalImage.data, originalWidth, originalHeight, display);"
"const imgData = await getImageData('../../assets/images/empty_road_mapillary.jpg');\n\nconst originalImage = cv.matFromImageData(imgData);\nconst { cols: originalWidth, rows: originalHeight } = originalImage;\n\nconst image = new cv.Mat();\ncv.cvtColor(originalImage, image, cv.COLOR_RGBA2RGB);\ncv.cvtColor(image, image, cv.COLOR_BGR2RGB); \n\nconst [B, C, H, W] = inputLayer.shape;\n\ncv.resize(image, image, new cv.Size(W, H));\n\nconst inputImage = transform(image.data, { width: W, height: H }, [0, 1, 2]); // NHWC to NCHW\n\ndisplayArrayAsImage(originalImage.data, originalWidth, originalHeight, display);\n"
],
"outputs": [
{
Expand All @@ -123,7 +123,7 @@
{
"language": "typescript",
"source": [
"const tensor_data = new Float32Array(inputImage);\nconst tensor = new ov.Tensor(ov.element.f32, inputLayer.shape, tensor_data);\n\nconst inferRequest = compiledModel.createInferRequest();\ninferRequest.setInputTensor(tensor);\ninferRequest.infer();\n\nconst output = inferRequest.getTensor(outputLayer);\n\nconst { data: outputData } = output;\nconst layers = { bg: [], c: [], h: [], w: [] };\nconst resultLayer = [];\nconst colormap = [[68, 1, 84, 255], [48, 103, 141, 255], [53, 183, 120, 255], [199, 216, 52, 255]];\n\nconst size = outputData.length/4;\n\nfor (let i = 0; i < size; i++) {\n const valueAt = (i, number) => outputData[i + number*size];\n\n const currentValues = { \n bg: valueAt(i, 0),\n c: valueAt(i, 1),\n h: valueAt(i, 2),\n w: valueAt(i, 3),\n };\n const values = Object.values(currentValues);\n const maxIndex = values.indexOf(Math.max(...values));\n\n resultLayer.push(maxIndex);\n}\n\nconst pixels = [];\nresultLayer.forEach(i => pixels.push(...colormap[i]));\n\ndisplayArrayAsImage(pixels, W, H, display);\n"
"const tensor_data = new Float32Array(inputImage);\nconst tensor = new ov.Tensor(ov.element.f32, inputLayer.shape, tensor_data);\n\nconst inferRequest = compiledModel.createInferRequest();\ninferRequest.setInputTensor(tensor);\ninferRequest.inferSync();\n\nconst output = inferRequest.getTensor(outputLayer);\n\nconst { data: outputData } = output;\nconst layers = { bg: [], c: [], h: [], w: [] };\nconst resultLayer = [];\nconst colormap = [[68, 1, 84, 255], [48, 103, 141, 255], [53, 183, 120, 255], [199, 216, 52, 255]];\n\nconst size = outputData.length/4;\n\nfor (let i = 0; i < size; i++) {\n const valueAt = (i, number) => outputData[i + number*size];\n\n const currentValues = { \n bg: valueAt(i, 0),\n c: valueAt(i, 1),\n h: valueAt(i, 2),\n w: valueAt(i, 3),\n };\n const values = Object.values(currentValues);\n const maxIndex = values.indexOf(Math.max(...values));\n\n resultLayer.push(maxIndex);\n}\n\nconst pixels = [];\nresultLayer.forEach(i => pixels.push(...colormap[i]));\n\ndisplayArrayAsImage(pixels, W, H, display);\n"
],
"outputs": [
{
Expand All @@ -146,7 +146,7 @@
{
"language": "typescript",
"source": [
"const alpha = 0.3;\n\nconst pixelsAsImageData = arrayToImageData(pixels, W, H);\nconst mask = cv.matFromImageData(pixelsAsImageData);\n\ncv.resize(mask, mask, new cv.Size(originalWidth, originalHeight));\n\ncv.addWeighted(mask, alpha, originalImage, 1 - alpha, 0, mask);\n\ndisplayArrayAsImage(mask.data, originalWidth, originalHeight, display);"
"const alpha = 0.3;\n\nconst pixelsAsImageData = arrayToImageData(pixels, W, H);\nconst mask = cv.matFromImageData(pixelsAsImageData);\n\ncv.resize(mask, mask, new cv.Size(originalWidth, originalHeight));\n\ncv.addWeighted(mask, alpha, originalImage, 1 - alpha, 0, mask);\n\ndisplayArrayAsImage(mask.data, originalWidth, originalHeight, display);\n"
],
"outputs": [
{
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10 changes: 5 additions & 5 deletions samples/js/node/notebooks/004-hello-detection.nnb
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Expand Up @@ -17,7 +17,7 @@
{
"language": "typescript",
"source": [
"const { cv } = require('opencv-wasm');\nconst { display } = require('node-kernel');\nconst { transform, getImageData, displayArrayAsImage, downloadFile } = require('../helpers.js');\n\nconst { addon: ov } = require('openvinojs-node'); "
"const { cv } = require('opencv-wasm');\nconst { display } = require('node-kernel');\nconst { transform, getImageData, displayArrayAsImage, downloadFile } = require('../helpers.js');\n\nconst { addon: ov } = require('openvinojs-node'); \n"
],
"outputs": []
},
Expand All @@ -31,7 +31,7 @@
{
"language": "typescript",
"source": [
"const baseArtifactsDir = '../../assets/models';\n\nconst modelName = 'horizontal-text-detection-0001';\nconst modelXMLName = `${modelName}.xml`;\nconst modelBINName = `${modelName}.bin`;\n\nconst modelXMLPath = baseArtifactsDir + '/' + modelXMLName;\n\nconst baseURL = 'https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.3/models_bin/1/horizontal-text-detection-0001/FP32/';\n\nawait downloadFile(baseURL + modelXMLName, modelXMLName, baseArtifactsDir);\nawait downloadFile(baseURL + modelBINName, modelBINName, baseArtifactsDir);"
"const baseArtifactsDir = '../../assets/models';\n\nconst modelName = 'horizontal-text-detection-0001';\nconst modelXMLName = `${modelName}.xml`;\nconst modelBINName = `${modelName}.bin`;\n\nconst modelXMLPath = baseArtifactsDir + '/' + modelXMLName;\n\nconst baseURL = 'https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.3/models_bin/1/horizontal-text-detection-0001/FP32/';\n\nawait downloadFile(baseURL + modelXMLName, modelXMLName, baseArtifactsDir);\nawait downloadFile(baseURL + modelBINName, modelBINName, baseArtifactsDir);\n"
],
"outputs": [
{
Expand Down Expand Up @@ -59,7 +59,7 @@
{
"language": "typescript",
"source": [
"const baseImagesDir = '../../assets/images';\nconst imgUrl = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/intel_rnb.jpg';\n\nawait downloadFile(imgUrl, 'intel_rnb.jpg', baseImagesDir);"
"const baseImagesDir = '../../assets/images';\nconst imgUrl = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/intel_rnb.jpg';\n\nawait downloadFile(imgUrl, 'intel_rnb.jpg', baseImagesDir);\n"
],
"outputs": [
{
Expand All @@ -86,7 +86,7 @@
{
"language": "typescript",
"source": [
"const core = new ov.Core();\nconst model = await core.readModel(modelXMLPath);\nconst compiledModel = await core.compileModel(model, 'CPU');\n\nconst inputLayer = compiledModel.input(0);\nconst outputLayer = compiledModel.output('boxes');"
"const core = new ov.Core();\nconst model = await core.readModel(modelXMLPath);\nconst compiledModel = await core.compileModel(model, 'CPU');\n\nconst inputLayer = compiledModel.input(0);\nconst outputLayer = compiledModel.output('boxes');\n"
],
"outputs": []
},
Expand Down Expand Up @@ -123,7 +123,7 @@
{
"language": "typescript",
"source": [
"const tensorData = new Float32Array(inputImage);\nconst tensor = new ov.Tensor(ov.element.f32, Int32Array.from(inputLayer.shape), tensorData);\n\nconst inferRequest = compiledModel.createInferRequest();\ninferRequest.setInputTensor(tensor);\ninferRequest.infer();\n\nconst output = inferRequest.getTensor(outputLayer);\nconst { data: boxes } = output;\n"
"const tensorData = new Float32Array(inputImage);\nconst tensor = new ov.Tensor(ov.element.f32, Int32Array.from(inputLayer.shape), tensorData);\n\nconst inferRequest = compiledModel.createInferRequest();\ninferRequest.setInputTensor(tensor);\ninferRequest.inferSync();\n\nconst output = inferRequest.getTensor(outputLayer);\nconst { data: boxes } = output;\n"
],
"outputs": []
},
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