OpenVINO - Image classification

Question

I tried to use OpenVINO Inference Engine to accelerate my DL inference. It works with one image. But I want to create a batch of two images and then do a inference. This is my code:

InferenceEngine::Core core;
InferenceEngine::CNNNetwork network = core.ReadNetwork("path/to/model.xml");
    
InferenceEngine::InputInfo::Ptr input_info = network.getInputsInfo().begin()->second;
std::string input_name = network.getInputsInfo().begin()->first;
    
InferenceEngine::DataPtr output_info = network.getOutputsInfo().begin()->second;
std::string output_name = network.getOutputsInfo().begin()->first;
    
InferenceEngine::ExecutableNetwork executableNetwork = core.LoadNetwork(network, "CPU");
    
InferenceEngine::InferRequest inferRequest = executableNetwork.CreateInferRequest();

std::string input_image_01 = "path/to/image_01.png";
cv::Mat image_01 = cv::imread(input_image_01 );
InferenceEngine::Blob::Ptr imgBlob_01 = wrapMat2Blob(image_01);

std::string input_image_02 = "path/to/image_02.png";
cv::Mat image_02 = cv::imread(input_image_02 );
InferenceEngine::Blob::Ptr imgBlob_02 = wrapMat2Blob(image_02);

InferenceEngine::BlobMap imgBlobMap;
std::pair<std::string, InferenceEngine::Blob::Ptr> pair01(input_image_01, imgBlob_01);
imgBlobMap.insert(pair01);
std::pair<std::string, InferenceEngine::Blob::Ptr> pair02(input_image_02, imgBlob_02);
imgBlobMap.insert(pair02);
inferRequest.SetInput(imgBlobMap);

inferRequest.StartAsync();
inferRequest.Wait(InferenceEngine::IInferRequest::WaitMode::RESULT_READY);

InferenceEngine::Blob::Ptr output = inferRequest.GetBlob(output_name);
std::vector<unsigned> class_results;
ClassificationResult cls(output, {"x", "y"}, 2, 3);
class_results = cls.getResults();

Unfortunately, I received the following error message from the command

inferRequest.SetInput(imgBlobMap);

[NOT_FOUND] Failed to find input or output with name: 'path/to/image_02.png' C:\j\workspace\private-ci\ie\build-windows-vs2019@2\b\repos\openvino\inference-engine\src\plugin_api\cpp_interfaces/impl/ie_infer_request_internal.hpp:303 C:\Program Files (x86)\Intel\openvino_2021.3.394\inference_engine\include\details/ie_exception_conversion.hpp:66

How can I create a batch of more than image, do a inference and get the information for classification class and confidence? Is the confidence and class located in the received variable of GetBlob()? Should I need the call of ClassificationResult cls(output, {"x", "y"}, 2, 3);?

score 0 · Answer 1 · answered Jun 23 '21 at 10:44

I'd recommend you to review Using Shape Inference article from OpenVINO online documentation to be aware of the limitations of using batches. It also refers to Open Model Zoo smart_classroom_demo, where dynamic batching is used in processing multiple previously detected faces. Basically, when you have batch enabled in the model, the memory buffer of your input blob will be allocated to have a room for all batch of images, and your responsibility is to fill data in input blob for each image in batch from your data. You may take a look at function CnnDLSDKBase::InferBatch, of smart_classroom_demo, which is located at file smart_classroom_demo/cpp/src/cnn.cpp, line 51. As you can see, in the loop over num_imgs an auxiliary function matU8ToBlob fills the input blob with data for current_batch_size of images, then set batch size for infer request and run inference.

for (size_t batch_i = 0; batch_i < num_imgs; batch_i += batch_size) {
    const size_t current_batch_size = std::min(batch_size, num_imgs - batch_i);
    for (size_t b = 0; b < current_batch_size; b++) {
        matU8ToBlob<uint8_t>(frames[batch_i + b], input, b);
    }

    if (config_.max_batch_size != 1)
        infer_request_.SetBatch(current_batch_size);
    infer_request_.Infer();

score 0 · Answer 2 · answered Oct 18 '21 at 03:09

there is a similar sample using the batch inputs as input into model within the OpenVINO. You can refer to below link.

https://github.com/openvinotoolkit/openvino/blob/ae2913d3b5970ce0d3112cc880d03be1708f13eb/inference-engine/samples/hello_nv12_input_classification/main.cpp#L236

OpenVINO - Image classification

2 Answers2