| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #include "../InferenceTest.hpp" |
| #include "../ImagePreprocessor.hpp" |
| #include "armnnTfLiteParser/ITfLiteParser.hpp" |
| |
| #include <cxxopts/cxxopts.hpp> |
| #include <fstream> |
| |
| using namespace armnnTfLiteParser; |
| |
| std::vector<ImageSet> ParseDataset(const std::string& filename) |
| { |
| std::ifstream read(filename); |
| std::vector<ImageSet> imageSet; |
| if (read.is_open()) |
| { |
| // Get the images and the correct corresponding label from the given file |
| for (std::string line; std::getline(read, line);) |
| { |
| stringstream ss(line); |
| std::string image_name; |
| std::string label; |
| getline(ss, image_name, ' '); |
| getline(ss, label, ' '); |
| imageSet.push_back(ImageSet(image_name, std::stoi(label))); |
| } |
| } |
| else |
| { |
| // Use the default images |
| imageSet.push_back(ImageSet("Dog.jpg", 209)); |
| // top five predictions in tensorflow: |
| // ----------------------------------- |
| // 209:Labrador retriever 0.949995 |
| // 160:Rhodesian ridgeback 0.0270182 |
| // 208:golden retriever 0.0192866 |
| // 853:tennis ball 0.000470382 |
| // 239:Greater Swiss Mountain dog 0.000464451 |
| imageSet.push_back(ImageSet("Cat.jpg", 283)); |
| // top five predictions in tensorflow: |
| // ----------------------------------- |
| // 283:tiger cat 0.579016 |
| // 286:Egyptian cat 0.319676 |
| // 282:tabby, tabby cat 0.0873346 |
| // 288:lynx, catamount 0.011163 |
| // 289:leopard, Panthera pardus 0.000856755 |
| imageSet.push_back(ImageSet("shark.jpg", 3)); |
| // top five predictions in tensorflow: |
| // ----------------------------------- |
| // 3:great white shark, white shark, ... 0.996926 |
| // 4:tiger shark, Galeocerdo cuvieri 0.00270528 |
| // 149:killer whale, killer, orca, ... 0.000121848 |
| // 395:sturgeon 7.78977e-05 |
| // 5:hammerhead, hammerhead shark 6.44127e-055 |
| }; |
| return imageSet; |
| } |
| |
| std::string GetLabelsFilenameFromOptions(int argc, char* argv[]) |
| { |
| cxxopts::Options options("TfLiteMobilenetQuantized-Armnn","Validation Options"); |
| |
| std::string fileName; |
| try |
| { |
| options |
| .allow_unrecognised_options() |
| .add_options() |
| ("l,labels", |
| "Filename of a text file where in each line contains an image " |
| "filename and the correct label the network should predict when fed that image", |
| cxxopts::value<std::string>(fileName)); |
| |
| auto result = options.parse(argc, argv); |
| } |
| catch (const cxxopts::OptionException& e) |
| { |
| std::cerr << e.what() << std::endl; |
| exit(EXIT_FAILURE); |
| } |
| catch (const std::exception& e) |
| { |
| std::cerr << "Fatal internal error: [" << e.what() << "]" << std::endl; |
| exit(EXIT_FAILURE); |
| } |
| |
| return fileName; |
| } |
| |
| |
| int main(int argc, char* argv[]) |
| { |
| int retVal = EXIT_FAILURE; |
| try |
| { |
| // Coverity fix: The following code may throw an exception of type std::length_error. |
| const std::string labels_file = GetLabelsFilenameFromOptions(argc,argv); |
| std::vector<ImageSet> imageSet = ParseDataset(labels_file); |
| std::vector<unsigned int> indices(imageSet.size()); |
| std::generate(indices.begin(), indices.end(), [n = 0] () mutable { return n++; }); |
| |
| armnn::TensorShape inputTensorShape({ 1, 224, 224, 3 }); |
| |
| using DataType = uint8_t; |
| using DatabaseType = ImagePreprocessor<DataType>; |
| using ParserType = armnnTfLiteParser::ITfLiteParser; |
| using ModelType = InferenceModel<ParserType, DataType>; |
| |
| // Coverity fix: ClassifierInferenceTestMain() may throw uncaught exceptions. |
| retVal = armnn::test::ClassifierInferenceTestMain<DatabaseType, |
| ParserType>( |
| argc, argv, |
| "mobilenet_v1_1.0_224_quant.tflite", // model name |
| true, // model is binary |
| "input", // input tensor name |
| "MobilenetV1/Predictions/Reshape_1", // output tensor name |
| indices, // vector of indices to select which images to validate |
| [&imageSet](const char* dataDir, const ModelType &) { |
| // we need to get the input quantization parameters from |
| // the parsed model |
| return DatabaseType( |
| dataDir, |
| 224, |
| 224, |
| imageSet, |
| 1); |
| }, |
| &inputTensorShape); |
| } |
| catch (const std::exception& e) |
| { |
| // Coverity fix: BOOST_LOG_TRIVIAL (typically used to report errors) may throw an |
| // exception of type std::length_error. |
| // Using stderr instead in this context as there is no point in nesting try-catch blocks here. |
| std::cerr << "WARNING: " << *argv << ": An error has occurred when running " |
| "the classifier inference tests: " << e.what() << std::endl; |
| } |
| return retVal; |
| } |