ImagePreprocessor.cpp)
TfLiteParserTest(TfLiteVGG16Quantized-Armnn "${TfLiteVGG16Quantized-Armnn_sources}")
+ set(TfLiteMobileNetQuantizedSoftmax-Armnn_sources
+ TfLiteMobileNetQuantizedSoftmax-Armnn/TfLiteMobileNetQuantizedSoftmax-Armnn.cpp
+ ImagePreprocessor.hpp
+ ImagePreprocessor.cpp)
+ TfLiteParserTest(TfLiteMobileNetQuantizedSoftmax-Armnn "${TfLiteMobileNetQuantizedSoftmax-Armnn_sources}")
+
set(TfLiteInceptionV3Quantized-Armnn_sources
TfLiteInceptionV3Quantized-Armnn/TfLiteInceptionV3Quantized-Armnn.cpp
ImagePreprocessor.hpp
TfLiteParserTest(TfLiteResNetV2-Armnn "${TfLiteResNetV2-Armnn_sources}")
set(TfLiteResNetV2-50-Quantized-Armnn_sources
- TfLiteResNetV2-50-Quantized-Armnn/TfLiteResNetV2-50-Quantized-Armnn.cpp
- ImagePreprocessor.hpp
- ImagePreprocessor.cpp)
+ TfLiteResNetV2-50-Quantized-Armnn/TfLiteResNetV2-50-Quantized-Armnn.cpp
+ ImagePreprocessor.hpp
+ ImagePreprocessor.cpp)
TfLiteParserTest(TfLiteResNetV2-50-Quantized-Armnn "${TfLiteResNetV2-50-Quantized-Armnn_sources}")
set(TfLiteMnasNet-Armnn_sources
--- /dev/null
+//
+// 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", 795},
+ {"Cat.jpg", 592},
+ {"shark.jpg", 436},
+ };
+
+ 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 & model) {
+ // we need to get the input quantization parameters from
+ // the parsed model
+ auto inputBinding = model.GetInputBindingInfo();
+ return DatabaseType(
+ dataDir,
+ 128,
+ 128,
+ imageSet,
+ inputBinding.second.GetQuantizationScale(),
+ inputBinding.second.GetQuantizationOffset(),
+ {{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;
+}