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//
// 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.
std::vector<ImageSet> 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
{"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
{"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
};
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
{ 0, 1, 2 }, // test images to test with as above
[&imageSet](const char* dataDir, const ModelType & model) {
// we need to get the input quantization parameters from
// the parsed model
auto inputBinding = model.GetInputBindingInfo();
return DatabaseType(
dataDir,
224,
224,
imageSet,
inputBinding.second.GetQuantizationScale(),
inputBinding.second.GetQuantizationOffset());
},
&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;
}
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