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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 =
{
// Class number in probability print out offset by 1000 due to batch size fix
{"Dog.jpg", 669},
{"Cat.jpg", 669},
{"shark.jpg", 669},
};
armnn::TensorShape inputTensorShape({ 2, 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,
"vgg_16_u8.tflite", // model name
true, // model is binary
"content_vgg/concat", // input tensor name
"content_vgg/prob", // 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(),
{{0, 0, 0}},
{{1, 1, 1}},
DatabaseType::DataFormat::NCHW,
2);
},
&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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