[GitHub] szha commented on a change in pull request #13680: [MXNET-1121] Example to demonstrate the inference workflow using RNN

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szha commented on a change in pull request #13680: [MXNET-1121] Example to demonstrate the inference workflow using RNN
URL: https://github.com/apache/incubator-mxnet/pull/13680#discussion_r251659739
 
 

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 File path: cpp-package/example/inference/sentiment_analysis_rnn.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 sentiment prediction workflow with pre-trained RNN model using MXNet C++ API.
+ * The example performs following tasks.
+ * 1. Load the pre-trained RNN model,
+ * 2. Load the dictionary file that contains word to index mapping.
+ * 3. Create executors for pre-determined input lengths.
+ * 4. Convert each line in the input to the vector of indices.
+ * 5. Predictor finds the right executor for each line.
+ * 4. Run the forward pass for each line and predicts the sentiment scores.
+ * The example uses a pre-trained RNN model that is trained with the IMDB dataset.
+ */
+
+#include <sys/stat.h>
+#include <iostream>
+#include <fstream>
+#include <cstdlib>
+#include <map>
+#include <string>
+#include <vector>
+#include <sstream>
+#include "mxnet-cpp/MxNetCpp.h"
+
+using namespace mxnet::cpp;
+
+static const int DEFAULT_BUCKET_KEYS[] = {5, 10, 15, 20, 25, 30};
+static const char DEFAULT_S3_URL[] = "https://s3.amazonaws.com/mxnet-cpp/RNN_model/";
+
+/*
+ * 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 std::string& input_dictionary,
+              const std::vector<int>& bucket_keys,
+              bool use_gpu = false);
+    float PredictSentiment(const std::string &input_review);
+    ~Predictor();
+
+ private:
+    void LoadModel(const std::string& model_json_file);
+    void LoadParameters(const std::string& model_parameters_file);
+    void LoadDictionary(const std::string &input_dictionary);
+    inline bool FileExists(const std::string& name) {
+        struct stat buffer;
+        return (stat(name.c_str(), &buffer) == 0);
+    }
+    float PredictSentimentForOneLine(const std::string &input_line);
+    int ConvertToIndexVector(const std::string& input,
+                      std::vector<float> *input_vector);
+    int GetIndexForOutputSymbolName(const std::string& output_symbol_name);
+    float GetIndexForWord(const std::string& word);
+    int GetClosestBucketKey(int num_words);
+    std::map<std::string, NDArray> args_map;
+    std::map<std::string, NDArray> aux_map;
+    std::map<std::string, int>  wordToIndex;
+    Symbol net;
+    std::map<int, Executor*> executor_buckets;
+    Context global_ctx = Context::cpu();
+};
+
+
+/*
+ * The constructor takes the following parameters as input:
+ * 1. model_json:  The RNN model in json formatted file.
+ * 2. model_params: File containing model parameters
+ * 3. input_dictionary: File containing the word and associated index.
+ * 4. num_words: Number of words which will be used to predict the sentiment.
+ *
+ * The constructor:
+ *  1. Loads the model and parameter files.
+ *  2. Loads the dictionary file to create index to word and word to index maps.
+ *  3. For each bucket key in the input vector of bucket keys, it invokes the SimpleBind to
+ *     create the executor. The bucket key determines the length of input data required
+ *     for that executor.
+ *  4. Creates a map of bucket key to corresponding executor.
+ *  5. The model is loaded only once. The executors share the memory for the parameters.
+ */
+Predictor::Predictor(const std::string& model_json,
+                     const std::string& model_params,
+                     const std::string& input_dictionary,
+                     const std::vector<int>& bucket_keys,
+                     bool use_gpu) {
+  if (use_gpu) {
+    global_ctx = Context::gpu();
+  }
+
+  /*
+   * Load the dictionary file that contains the word and its index.
+   * The function creates word to index and index to word map. The maps are used to create index
+   * vector for the input sentence.
+   */
+  LoadDictionary(input_dictionary);
+
+  // Load the model
+  LoadModel(model_json);
+
+  // Load the model parameters.
+  LoadParameters(model_params);
+
+  // Create the executors for each bucket key. The bucket key represents the shape of input data.
+  for (int bucket : bucket_keys) {
+    args_map["data0"] = NDArray(Shape(bucket, 1), global_ctx, false);
+    args_map["data1"] = NDArray(Shape(1), global_ctx, false);
+    Executor* executor = net.SimpleBind(global_ctx, args_map, std::map<std::string, NDArray>(),
+                                std::map<std::string, OpReqType>(), aux_map);
+    executor_buckets[bucket] = executor;
+  }
 
 Review comment:
   This won't share the memory across executors, but will allocate excessive space from which only `1 / bucket_keys.Size()` is used at any time.

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