Hi @lionkunonly, what's the license of hello_classification_electron/main.js you are using if it's from Electron upstream? Thanks

I'll try to use PreProcess interface to optimize the image resizing and color conversion of node.js image classification example.

Test "Core.getVersions should return a map" fails. It turns an object currently.

We can fix it later once n-api supports Map. see nodejs/node-addon-api#472 for details.

It would be good to get the target device list by getAvailableDevices API in the Electron.js example. Refer to #16

@huningxin I think the inference-engine-node should be updated to 0.5 in npmjs.com while it supports OpenVINO 2021.1 and changes some APIs now

It maps to Core::GetAvailableDevices. The JS API sketch could be

partial interface Core {
  sequence<DOMString> getAvailableDevices();
};

The inference-engine-node in npmjs.com is old. Some APIs like core.getAvailabelDevices() is not supported.

I try to use the inference engine to run the network on my CPU. I want to make full use of my logic CPUs, but the fact is that it usually uses half of the logic CPUs. Is there any way to use all logic CPUs with the inference engine? And I want to know if the increasing number of logic CPUs can bring better performance?

System Info: Unbuntu 18.04 64bit
CPU Info: Intel(R) Core(TM) i7-8700 CPU
OpenVINO version: 2020.3

@argretzi

startAsync: https://docs.openvinotoolkit.org/latest/classInferenceEngine_1_1InferRequest.html#a405293e8423d82a5b45f642a3bef0d24

After #39 merged, the createCore API is removed. There are some issues:

• npm test fails, the test cases need to be fixed.

• The API doc also to be fixed.

• The electron example needs to be fixed.

@artyomtugaryov , please help. CC @lionkunonly .

I'm having an issue where instantiating a new network on the NCS2 creates an issue with any existing networks i've instantiated.

I have a pipeline that i'm passing images through several models and want to run all of the inference jobs on the NCS2 but can only seem to get this library to spin up one.

If I run all of the models on the CPU I don't have the same issue -- perhaps an issue in this library?

Please refer to https://github.com/intel/inference-engine-node/tree/master/example/hello_object_detection_ssd_node

I try install the inference-engine-node v0.1.5 in other projects. But the npm reports an error:

npm ERR! code ENOLOCAL
npm ERR! Could not install from "node_modules/inference-engine-node/example/hello_classification_node" as it does not contain a package.json file.

It is a strange error, because there is a package.js in the hello_classification_node. I do not know why this error happen. Could you help solve it? @huningxin

In the file test/core.js, I try to execute the sentence core.getVersions('GPU') to make the GPU load the plugins in advance. This operation leads a error like the following:

 TypeError: Failed to create plugin /opt/intel/openvino_2020.2.120/deployment_tools/inference_engine/lib/intel64/libclDNNPlugin.so for device GPU
Please, check your environment
[CLDNN ERROR]. clGetPlatformIDs error -1001

The node.js API core.getVersions() binds the native Core.GetVersions() API. It seems a problem related with the OpenCL, but I do not find any information for such a error.

It includes loading CPU extensions by AddExtension and GPU extensions by SetConfig.

The native sample code could be found at https://github.com/opencv/dldt/blob/2020/inference-engine/samples/classification_sample_async/main.cpp#L83 for CPU extension and https://github.com/opencv/dldt/blob/2020/inference-engine/samples/classification_sample_async/main.cpp#L89 for GPU extension.

Please check out more details of extension mechanism at: https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Extensibility_DG_Intro.html

For 2020 release, according to comment, all ops are supported by MKLDNNPlugin, so there is no built-in CPU extension. GPU extension is still there.

Test "InferRequest.getBlob should throw for invalid arguments" fails against OpenVINO 2020.1.

It is required to support mean and std values. Please refer to the API doc of PreProcessChannel.

It maps to PreProcessInfo. It allows to setup input color format, resize algorithm and others. The possible usage could be:

input_info.preProcess.setResizeAlgorithm('resize-bilinear');
input_info.preProcess.setColorFormat('rgbx');

The gaps bettwen Node.js API vs Python IE API will be recorded here.

https://github.com/intel/webml-polyfill/tree/master/examples/image_classification

Click the icon should navigate to https://github.com/intel/inference-engine-node