110 lines
5.3 KiB
C++
110 lines
5.3 KiB
C++
// Copyright (C) 2018-2019 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include <vector>
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#include <memory>
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#include <string>
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#include <samples/common.hpp>
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#ifdef UNICODE
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#include <tchar.h>
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#endif
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#include <inference_engine.hpp>
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#include <samples/ocv_common.hpp>
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#include <samples/classification_results.h>
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using namespace InferenceEngine;
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#ifndef UNICODE
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#define tcout std::cout
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#define _T(STR) STR
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#else
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#define tcout std::wcout
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#endif
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#ifndef UNICODE
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int main(int argc, char *argv[]) {
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#else
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int wmain(int argc, wchar_t *argv[]) {
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#endif
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try {
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// ------------------------------ Parsing and validation of input args ---------------------------------
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if (argc != 4) {
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tcout << _T("Usage : ./hello_classification <path_to_model> <path_to_image> <device_name>") << std::endl;
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return EXIT_FAILURE;
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}
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const file_name_t input_model{argv[1]};
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const file_name_t input_image_path{argv[2]};
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const std::string device_name{argv[3]};
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 1. Load inference engine instance -------------------------------------
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Core ie;
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 2. Read IR Generated by ModelOptimizer (.xml and .bin files) ------------
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CNNNetReader network_reader;
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network_reader.ReadNetwork(fileNameToString(input_model));
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network_reader.ReadWeights(fileNameToString(input_model).substr(0, input_model.size() - 4) + ".bin");
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network_reader.getNetwork().setBatchSize(1);
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CNNNetwork network = network_reader.getNetwork();
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 3. Configure input & output ---------------------------------------------
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// --------------------------- Prepare input blobs -----------------------------------------------------
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InputInfo::Ptr input_info = network.getInputsInfo().begin()->second;
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std::string input_name = network.getInputsInfo().begin()->first;
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/* Mark input as resizable by setting of a resize algorithm.
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* In this case we will be able to set an input blob of any shape to an infer request.
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* Resize and layout conversions are executed automatically during inference */
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input_info->getPreProcess().setResizeAlgorithm(RESIZE_BILINEAR);
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input_info->setLayout(Layout::NHWC);
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input_info->setPrecision(Precision::U8);
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// --------------------------- Prepare output blobs ----------------------------------------------------
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DataPtr output_info = network.getOutputsInfo().begin()->second;
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std::string output_name = network.getOutputsInfo().begin()->first;
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output_info->setPrecision(Precision::FP32);
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 4. Loading model to the device ------------------------------------------
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ExecutableNetwork executable_network = ie.LoadNetwork(network, device_name);
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 5. Create infer request -------------------------------------------------
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InferRequest infer_request = executable_network.CreateInferRequest();
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 6. Prepare input --------------------------------------------------------
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/* Read input image to a blob and set it to an infer request without resize and layout conversions. */
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cv::Mat image = cv::imread(input_image_path);
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Blob::Ptr imgBlob = wrapMat2Blob(image); // just wrap Mat data by Blob::Ptr without allocating of new memory
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infer_request.SetBlob(input_name, imgBlob); // infer_request accepts input blob of any size
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 7. Do inference --------------------------------------------------------
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/* Running the request synchronously */
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infer_request.Infer();
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 8. Process output ------------------------------------------------------
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Blob::Ptr output = infer_request.GetBlob(output_name);
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// Print classification results
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ClassificationResult classificationResult(output, {fileNameToString(input_image_path)});
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classificationResult.print();
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// -----------------------------------------------------------------------------------------------------
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} catch (const std::exception & ex) {
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std::cerr << ex.what() << std::endl;
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return EXIT_FAILURE;
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}
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std::cout << "This sample is an API example, for any performance measurements "
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"please use the dedicated benchmark_app tool" << std::endl;
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return EXIT_SUCCESS;
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}
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