[GitHub] leleamol commented on a change in pull request #13294: [MXNET-1083] Add the example to demonstrate the inference workflow using C++ API

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leleamol commented on a change in pull request #13294: [MXNET-1083] Add the example to demonstrate the inference workflow using C++ API
URL: https://github.com/apache/incubator-mxnet/pull/13294#discussion_r236454020
 
 

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 File path: cpp-package/example/inference/inception_inference.cpp
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you 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.
+ */
+
+/*
+ * This example demonstrates image classification workflow with pre-trained models using MXNet C++ API.
+ * The example performs following tasks.
+ * 1. Load the pre-trained model,
+ * 2. Load the parameters of pre-trained model,
+ * 3. Load the image to be classified  in to NDArray.
+ * 4. Normalize the image using the mean of images that were used for training.
+ * 5. Run the forward pass and predict the input image.
+ */
+
+#include <iostream>
+#include <fstream>
+#include <map>
+#include <string>
+#include <vector>
+#include "mxnet-cpp/MxNetCpp.h"
+#include <opencv2/opencv.hpp>
+using namespace std;
+using namespace mxnet::cpp;
+
+
+/*
+ * class Predictor
+ *
+ * This class encapsulates the functionality to load the model, process input image and run the forward pass.
+ */
+
+class Predictor {
+ public:
+    Predictor() {}
+    Predictor(const std::string& model_json,
+              const std::string& model_params,
+              const Shape& input_shape,
+              bool gpu_context_type = false,
+              const std::string& synset_file = "",
+              const std::string& mean_image_file = "");
+    void PredictImage(const std::string& image_file);
+    ~Predictor();
+
+ private:
+    void LoadModel(const std::string& model_json_file);
+    void LoadParameters(const std::string& model_parameters_file);
+    void LoadSynset(const std::string& synset_file);
+    NDArray LoadInputImage(const std::string& image_file);
+    void LoadMeanImageData();
+    void NormalizeInput(const std::string& mean_image_file);
+
+    NDArray mean_img;
+    map<string, NDArray> args_map;
+    map<string, NDArray> aux_map;
+    std::vector<std::string> output_labels;
+    Symbol net;
+    Executor *executor;
+    Shape input_shape;
+    NDArray mean_image_data;
+    Context global_ctx = Context::cpu();
+    string mean_image_file;
+};
+
+
+/*
+ * The constructor takes following parameters as input:
+ * 1. model_json:  The model in json formatted file.
+ * 2. model_params: File containing model parameters
+ * 3. synset_file: File containing the list of image labels
+ * 4. input_shape: Shape of input data to the model. Since this class will be running one inference at a time,
+ *                 the input shape is required to be in format Shape(1, number_of_channels, height, width)
+ * The input image will be resized to (height x width) size before running the inference.
+ * The constructor will:
+ *  1. Load the model and parameter files.
+ *  2. Load the synset file.
+ *  3. Invoke the SimpleBind to bind the input argument to the model and create an executor.
+ *
+ *  The SimpleBind is expected to be invoked only once.
+ */
+Predictor::Predictor(const std::string& model_json,
+                     const std::string& model_params,
+                     const Shape& input_shape,
+                     bool gpu_context_type,
+                     const std::string& synset_file,
+                     const std::string& mean_image_file):
+                     input_shape(input_shape),
+                     mean_image_file(mean_image_file) {
+  if (gpu_context_type) {
+    global_ctx = Context::gpu();
+  }
+  // Load the model
+  LoadModel(model_json);
+
+  // Load the model parameters.
+  LoadParameters(model_params);
+
+  /*
+   * Load the synset file containing the image labels, if provided.
+   * The data will be used to output the exact label that matches highest output of the model.
+   */
+  if (!synset_file.empty()) {
+    LoadSynset(synset_file);
+  }
+
+  /*
+   * Load the mean image data if specified.
+   */
+  if (!mean_image_file.empty()) {
+    LoadMeanImageData();
+  }
+
+  // Create an executor after binding the model to input parameters.
+  args_map["data"] = NDArray(input_shape, global_ctx, false);
+  executor = net.SimpleBind(global_ctx, args_map, map<string, NDArray>(),
+                              map<string, OpReqType>(), aux_map);
+}
+
+/*
+ * The following function loads the model from json file.
+ */
+void Predictor::LoadModel(const std::string& model_json_file) {
+  LG << "Loading the model from " << model_json_file << std::endl;
+  net = Symbol::Load(model_json_file);
 
 Review comment:
   Will do.

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