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_r251586366
########## File path: cpp-package/example/inference/sentiment_analysis_rnn.cpp ########## @@ -0,0 +1,398 @@ +/* + * 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. Convert the input string to vector of indices and padded to match the input data length. + * 4. Run the forward pass and predict the output string. + * 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_NUM_WORDS = 10; +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, + bool use_gpu = false, + int num_words = DEFAULT_NUM_WORDS); + float PredictSentiment(const std::string &input_sequence); + ~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); + } + int ConverToIndexVector(const std::string& input, + std::vector<float> *input_vector); + int GetIndexForOutputSymbolName(const std::string& output_symbol_name); + float GetIndexForWord(const std::string& word); + std::map<std::string, NDArray> args_map; + std::map<std::string, NDArray> aux_map; + std::map<std::string, int> wordToIndex; + Symbol net; + Executor *executor; + Context global_ctx = Context::cpu(); + int num_words; +}; + + +/* + * 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. Invokes 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 std::string& input_dictionary, + bool use_gpu, + int num_words):num_words(num_words) { + 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); + + args_map["data0"] = NDArray(Shape(num_words, 1), global_ctx, false); + args_map["data1"] = NDArray(Shape(1), global_ctx, false); + + executor = net.SimpleBind(global_ctx, args_map, std::map<std::string, NDArray>(), + std::map<std::string, OpReqType>(), aux_map); +} + + +/* + * The following function loads the model from json file. + */ +void Predictor::LoadModel(const std::string& model_json_file) { + if (!FileExists(model_json_file)) { + LG << "Model file " << model_json_file << " does not exist"; + throw std::runtime_error("Model file does not exist"); + } + LG << "Loading the model from " << model_json_file << std::endl; + net = Symbol::Load(model_json_file); +} + + +/* + * The following function loads the model parameters. + */ +void Predictor::LoadParameters(const std::string& model_parameters_file) { + if (!FileExists(model_parameters_file)) { + LG << "Parameter file " << model_parameters_file << " does not exist"; + throw std::runtime_error("Model parameters does not exist"); + } + LG << "Loading the model parameters from " << model_parameters_file << std::endl; + std::map<std::string, NDArray> parameters; + NDArray::Load(model_parameters_file, 0, ¶meters); + for (const auto &k : parameters) { + if (k.first.substr(0, 4) == "aux:") { + auto name = k.first.substr(4, k.first.size() - 4); + aux_map[name] = k.second.Copy(global_ctx); + } + if (k.first.substr(0, 4) == "arg:") { + auto name = k.first.substr(4, k.first.size() - 4); + args_map[name] = k.second.Copy(global_ctx); + } + } + /*WaitAll is need when we copy data between GPU and the main memory*/ + NDArray::WaitAll(); +} + + +/* + * The following function loads the dictionary file. + * The function constructs the word to index and index to word maps. + * These maps will be used to represent words in the input sentence to their indices. + * Ensure to use the same dictionary file that was used for training the network. + */ +void Predictor::LoadDictionary(const std::string& input_dictionary) { + if (!FileExists(input_dictionary)) { + LG << "Dictionary file " << input_dictionary << " does not exist"; + throw std::runtime_error("Dictionary file does not exist"); + } + LG << "Loading the dictionary file."; + std::ifstream fi(input_dictionary.c_str()); + if (!fi.is_open()) { + std::cerr << "Error opening dictionary file " << input_dictionary << std::endl; + assert(false); + } + + std::string line; + std::string word; + int index; + while (std::getline(fi, line)) { + std::istringstream stringline(line); + stringline >> word >> index; + wordToIndex[word] = index; + } + fi.close(); +} + + +/* + * The function returns the index associated with the word in the dictionary. + * If the word is not present, the index representing "<unk>" is returned. + * If the "<unk>" is not present then 0 is returned. + */ +float Predictor::GetIndexForWord(const std::string& word) { + if (wordToIndex.find(word) == wordToIndex.end()) { + if (wordToIndex.find("<unk>") == wordToIndex.end()) + return 0; + else + return static_cast<float>(wordToIndex["<unk>"]); + } + return static_cast<float>(wordToIndex[word]); +} + +/* + * The function populates the input vector with indices from the dictionary that + * correspond to the words in the input string. + */ +int Predictor::ConverToIndexVector(const std::string& input, std::vector<float> *input_vector) { + std::istringstream input_string(input); + input_vector->clear(); + const char delimiter = ' '; + std::string token; + size_t words = 0; + while (std::getline(input_string, token, delimiter) && (words <= input_vector->size())) { + input_vector->push_back(GetIndexForWord(token)); + words++; + } + return words; +} + + +/* + * The function returns the index at which the given symbol name will appear + * in the output vector of NDArrays obtained after running the forward pass on the executor. + */ +int Predictor::GetIndexForOutputSymbolName(const std::string& output_symbol_name) { + int index = 0; + for (const std::string op : net.ListOutputs()) { + if (op == output_symbol_name) { + return index; + } else { + index++; + } + } + throw std::runtime_error("The output symbol name can not be found"); +} + + +/* + * The following function runs the forward pass on the model. + * The executor is created in the constructor. + */ +float Predictor::PredictSentiment(const std::string& input_text) { + /* + * Initialize a vector of length equal to 'num_words' with index corresponding to <eos>. + * Convert the input string to a vector of indices that represent + * the words in the input string. + */ + std::vector<float> index_vector(num_words, GetIndexForWord("<eos>")); + int num_words = ConverToIndexVector(input_text, &index_vector); Review comment: @Isa-rentacs I have updated the example to handle variable length input. I have addressed your review comments in the updated example. The model expects 2 inputs viz. "data0" and "data1". The "data0" represents the review line for which the score is generated and "data1" represents the number of words in the sentence that are considered for the prediction. For both the inputs the model expects "NDArray" input. The sample code line in the [Gluon Tutorial here](http://gluon-nlp.mxnet.io/examples/sentiment_analysis/sentiment_analysis.html#) in python also uses and NDArray of dimension 1. Regarding the names "data0" and "data1", the model is developed and trained in python by following the above tutorial. As per the documentation [here](http://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.export) when Gluon model is exported it automatically names the inputs as data0, data1, etc. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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