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_r252445321
########## 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. Review comment: Done ---------------------------------------------------------------- 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
