tests/TfLiteMobileNetQuantizedSoftmax-Armnn/TfLiteMobileNetQuantizedSoftmax-Armnn.cpp

//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "../InferenceTest.hpp"
#include "../ImagePreprocessor.hpp"
#include "armnnTfLiteParser/ITfLiteParser.hpp"

using namespace armnnTfLiteParser;

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.
        // The model we are using incorrectly classifies the images
        // But can still be used for benchmarking the layers.
        std::vector<ImageSet> imageSet =
                {
                        {"Dog.jpg", 789},
                        {"Cat.jpg", 592},
                        {"shark.jpg", 755},
                };

        armnn::TensorShape inputTensorShape({ 1, 128, 128, 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_0.25_128_quant.tflite", // model name
                true,                      // model is binary
                "input",      // input tensor name
                "MobilenetV1/Predictions/Reshape_1",        // output tensor name
                { 0, 1, 2 },               // test images to test with as above
                [&imageSet](const char* dataDir, const ModelType &) {
                    // we need to get the input quantization parameters from
                    // the parsed model
                    return DatabaseType(
                            dataDir,
                            128,
                            128,
                            imageSet,
                            1,
                            {{0, 0, 0}},
                            {{1, 1, 1}},
                            DatabaseType::DataFormat::NCHW,
                            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;
}