leleamol 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_r252445587
########## File path: cpp-package/example/inference/sentiment_analysis_rnn.cpp ########## @@ -0,0 +1,464 @@ +/* + * 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: @szha , I updated the example to create shared executors. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. 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