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61 lines
2.3 KiB
61 lines
2.3 KiB
// |
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// Copyright © 2017 Arm Ltd. All rights reserved. |
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// SPDX-License-Identifier: MIT |
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// |
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#include "../InferenceTest.hpp" |
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#include "../ImagePreprocessor.hpp" |
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#include "armnnTfLiteParser/ITfLiteParser.hpp" |
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using namespace armnnTfLiteParser; |
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int main(int argc, char* argv[]) |
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{ |
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int retVal = EXIT_FAILURE; |
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try |
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{ |
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std::vector<ImageSet> imageSet = |
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{ |
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{"Dog.jpg", 209}, |
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{"Cat.jpg", 283}, |
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{"shark.jpg", 3}, |
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}; |
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armnn::TensorShape inputTensorShape({ 1, 299, 299, 3 }); |
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using DataType = uint8_t; |
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using DatabaseType = ImagePreprocessor<DataType>; |
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using ParserType = armnnTfLiteParser::ITfLiteParser; |
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using ModelType = InferenceModel<ParserType, DataType>; |
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// Coverity fix: ClassifierInferenceTestMain() may throw uncaught exceptions. |
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retVal = armnn::test::ClassifierInferenceTestMain<DatabaseType, |
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ParserType>( |
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argc, argv, |
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"inception_v4_299_quant.tflite", // model name |
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true, // model is binary |
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"input", // input tensor name |
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"InceptionV4/Logits/Predictions", // output tensor name |
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{ 0, 1, 2 }, // test images to test with as above |
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[&imageSet](const char* dataDir, const ModelType &) { |
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// we need to get the input quantization parameters from |
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// the parsed model |
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return DatabaseType( |
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dataDir, |
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299, |
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299, |
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imageSet, |
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1); |
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}, |
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&inputTensorShape); |
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} |
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catch (const std::exception& e) |
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{ |
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// Coverity fix: BOOST_LOG_TRIVIAL (typically used to report errors) may throw an |
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// exception of type std::length_error. |
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// Using stderr instead in this context as there is no point in nesting try-catch blocks here. |
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std::cerr << "WARNING: " << *argv << ": An error has occurred when running " |
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"the classifier inference tests: " << e.what() << std::endl; |
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} |
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return retVal; |
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}
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