Publishing R3

inference-engine/samples/hello_classification/main.cpp

@@ -0,0 +1,121 @@
+/*
+// Copyright (c) 2018 Intel Corporation
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+*/
+
+#include <iomanip>
+#include <vector>
+#include <memory>
+#include <string>
+#include <cstdlib>
+
+#include <opencv2/opencv.hpp>
+#include <inference_engine.hpp>
+
+using namespace InferenceEngine;
+
+int main(int argc, char *argv[]) {
+    try {
+        // ------------------------------ Parsing and validation of input args ---------------------------------
+        if (argc != 3) {
+            std::cout << "Usage : ./hello_classification <path_to_model> <path_to_image>" << std::endl;
+            return EXIT_FAILURE;
+        }
+
+        const std::string input_model{argv[1]};
+        const std::string input_image_path{argv[2]};
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 1. Load Plugin for inference engine -------------------------------------
+        PluginDispatcher dispatcher({"../../../lib/intel64", ""});
+        InferencePlugin plugin(dispatcher.getSuitablePlugin(TargetDevice::eCPU));
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 2. Read IR Generated by ModelOptimizer (.xml and .bin files) ------------
+        CNNNetReader network_reader;
+        network_reader.ReadNetwork(input_model);
+        network_reader.ReadWeights(input_model.substr(0, input_model.size() - 4) + ".bin");
+        network_reader.getNetwork().setBatchSize(1);
+        CNNNetwork network = network_reader.getNetwork();
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 3. Configure input & output ---------------------------------------------
+        // --------------------------- Prepare input blobs -----------------------------------------------------
+        InputInfo::Ptr input_info = network.getInputsInfo().begin()->second;
+        std::string input_name = network.getInputsInfo().begin()->first;
+
+        input_info->setLayout(Layout::NCHW);
+        input_info->setPrecision(Precision::U8);
+
+        // --------------------------- Prepare output blobs ----------------------------------------------------
+        DataPtr output_info = network.getOutputsInfo().begin()->second;
+        std::string output_name = network.getOutputsInfo().begin()->first;
+
+        output_info->setPrecision(Precision::FP32);
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 4. Loading model to the plugin ------------------------------------------
+        ExecutableNetwork executable_network = plugin.LoadNetwork(network, {});
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 5. Create infer request -------------------------------------------------
+        InferRequest infer_request = executable_network.CreateInferRequest();
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 6. Prepare input --------------------------------------------------------
+
+        cv::Mat image = cv::imread(input_image_path);
+
+        /* Resize manually and copy data from the image to the input blob */
+        Blob::Ptr input = infer_request.GetBlob(input_name);
+        auto input_data = input->buffer().as<PrecisionTrait<Precision::U8>::value_type *>();
+
+        cv::resize(image, image, cv::Size(input_info->getTensorDesc().getDims()[3], input_info->getTensorDesc().getDims()[2]));
+
+        size_t channels_number = input->getTensorDesc().getDims()[1];
+        size_t image_size = input->getTensorDesc().getDims()[3] * input->getTensorDesc().getDims()[2];
+
+        for (size_t pid = 0; pid < image_size; ++pid) {
+            for (size_t ch = 0; ch < channels_number; ++ch) {
+                input_data[ch * image_size + pid] = image.at<cv::Vec3b>(pid)[ch];
+            }
+        }
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 7. Do inference --------------------------------------------------------
+        /* Running the request synchronously */
+        infer_request.Infer();
+        // -----------------------------------------------------------------------------------------------------
+
+        // --------------------------- 8. Process output ------------------------------------------------------
+        Blob::Ptr output = infer_request.GetBlob(output_name);
+        auto output_data = output->buffer().as<PrecisionTrait<Precision::FP32>::value_type*>();
+
+        std::vector<unsigned> results;
+        /*  This is to sort output probabilities and put it to results vector */
+        TopResults(10, *output, results);
+
+        std::cout << std::endl << "Top 10 results:" << std::endl << std::endl;
+        for (size_t id = 0; id < 10; ++id) {
+            std::cout.precision(7);
+            auto result = output_data[results[id]];
+            std::cout << std::left << std::fixed << result << " label #" << results[id] << std::endl;
+        }
+        // -----------------------------------------------------------------------------------------------------
+    } catch (const std::exception & ex) {
+        std::cerr << ex.what() << std::endl;
+        return EXIT_FAILURE;
+    }
+    return EXIT_SUCCESS;
+}