IVGCVSW-5284 Refactor ExecuteNetwork
* Removed boost program options and replaced it with cxxopts
* Unified adding, parsing and validation of program options
into the struct ProgramOptions
* Program options are now parsed directly into ExecuteNetworkParams
which can be passed directly to MainImpl
* Split NetworkExecutionUtils into header and source
* Removed RunTest
* Removed RunCsvTest
* Removed RunClTuning
* Moved MainImpl back to ExecuteNetwork.cpp
* Added additional util functions
The functionality of ExecuteNetwork remains the same. Only
cl tuning runs need to be started separately
and there is no short option for fp16-turbo-mode because -h is
reserved in cxxopts to print help messages
Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Change-Id: Ib9689375c81e1a184c17bb3ea66c3550430bbe09
diff --git a/tests/NetworkExecutionUtils/NetworkExecutionUtils.cpp b/tests/NetworkExecutionUtils/NetworkExecutionUtils.cpp
new file mode 100644
index 0000000..3e7c87d
--- /dev/null
+++ b/tests/NetworkExecutionUtils/NetworkExecutionUtils.cpp
@@ -0,0 +1,292 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "NetworkExecutionUtils.hpp"
+
+#include <Filesystem.hpp>
+#include <InferenceTest.hpp>
+#include <ResolveType.hpp>
+
+#if defined(ARMNN_SERIALIZER)
+#include "armnnDeserializer/IDeserializer.hpp"
+#endif
+#if defined(ARMNN_CAFFE_PARSER)
+#include "armnnCaffeParser/ICaffeParser.hpp"
+#endif
+#if defined(ARMNN_TF_PARSER)
+#include "armnnTfParser/ITfParser.hpp"
+#endif
+#if defined(ARMNN_TF_LITE_PARSER)
+#include "armnnTfLiteParser/ITfLiteParser.hpp"
+#endif
+#if defined(ARMNN_ONNX_PARSER)
+#include "armnnOnnxParser/IOnnxParser.hpp"
+#endif
+
+
+template<typename T, typename TParseElementFunc>
+std::vector<T> ParseArrayImpl(std::istream& stream, TParseElementFunc parseElementFunc, const char* chars = "\t ,:")
+{
+ std::vector<T> result;
+ // Processes line-by-line.
+ std::string line;
+ while (std::getline(stream, line))
+ {
+ std::vector<std::string> tokens = armnn::stringUtils::StringTokenizer(line, chars);
+ for (const std::string& token : tokens)
+ {
+ if (!token.empty()) // See https://stackoverflow.com/questions/10437406/
+ {
+ try
+ {
+ result.push_back(parseElementFunc(token));
+ }
+ catch (const std::exception&)
+ {
+ ARMNN_LOG(error) << "'" << token << "' is not a valid number. It has been ignored.";
+ }
+ }
+ }
+ }
+
+ return result;
+}
+
+
+template<armnn::DataType NonQuantizedType>
+auto ParseDataArray(std::istream& stream);
+
+template<armnn::DataType QuantizedType>
+auto ParseDataArray(std::istream& stream,
+ const float& quantizationScale,
+ const int32_t& quantizationOffset);
+
+template<>
+auto ParseDataArray<armnn::DataType::Float32>(std::istream& stream)
+{
+ return ParseArrayImpl<float>(stream, [](const std::string& s) { return std::stof(s); });
+}
+
+template<>
+auto ParseDataArray<armnn::DataType::Signed32>(std::istream& stream)
+{
+ return ParseArrayImpl<int>(stream, [](const std::string& s) { return std::stoi(s); });
+}
+
+template<>
+auto ParseDataArray<armnn::DataType::QAsymmU8>(std::istream& stream)
+{
+ return ParseArrayImpl<uint8_t>(stream,
+ [](const std::string& s) { return armnn::numeric_cast<uint8_t>(std::stoi(s)); });
+}
+
+template<>
+auto ParseDataArray<armnn::DataType::QAsymmU8>(std::istream& stream,
+ const float& quantizationScale,
+ const int32_t& quantizationOffset)
+{
+ return ParseArrayImpl<uint8_t>(stream,
+ [&quantizationScale, &quantizationOffset](const std::string& s)
+ {
+ return armnn::numeric_cast<uint8_t>(
+ armnn::Quantize<uint8_t>(std::stof(s),
+ quantizationScale,
+ quantizationOffset));
+ });
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+std::vector<T> GenerateDummyTensorData(unsigned int numElements)
+{
+ return std::vector<T>(numElements, static_cast<T>(0));
+}
+
+
+std::vector<unsigned int> ParseArray(std::istream& stream)
+{
+ return ParseArrayImpl<unsigned int>(
+ stream,
+ [](const std::string& s) { return armnn::numeric_cast<unsigned int>(std::stoi(s)); });
+}
+
+std::vector<std::string> ParseStringList(const std::string& inputString, const char* delimiter)
+{
+ std::stringstream stream(inputString);
+ return ParseArrayImpl<std::string>(stream, [](const std::string& s) {
+ return armnn::stringUtils::StringTrimCopy(s); }, delimiter);
+}
+
+
+TensorPrinter::TensorPrinter(const std::string& binding,
+ const armnn::TensorInfo& info,
+ const std::string& outputTensorFile,
+ bool dequantizeOutput)
+ : m_OutputBinding(binding)
+ , m_Scale(info.GetQuantizationScale())
+ , m_Offset(info.GetQuantizationOffset())
+ , m_OutputTensorFile(outputTensorFile)
+ , m_DequantizeOutput(dequantizeOutput) {}
+
+void TensorPrinter::operator()(const std::vector<float>& values)
+{
+ ForEachValue(values, [](float value)
+ {
+ printf("%f ", value);
+ });
+ WriteToFile(values);
+}
+
+void TensorPrinter::operator()(const std::vector<uint8_t>& values)
+{
+ if(m_DequantizeOutput)
+ {
+ auto& scale = m_Scale;
+ auto& offset = m_Offset;
+ std::vector<float> dequantizedValues;
+ ForEachValue(values, [&scale, &offset, &dequantizedValues](uint8_t value)
+ {
+ auto dequantizedValue = armnn::Dequantize(value, scale, offset);
+ printf("%f ", dequantizedValue);
+ dequantizedValues.push_back(dequantizedValue);
+ });
+ WriteToFile(dequantizedValues);
+ }
+ else
+ {
+ const std::vector<int> intValues(values.begin(), values.end());
+ operator()(intValues);
+ }
+}
+
+void TensorPrinter::operator()(const std::vector<int>& values)
+{
+ ForEachValue(values, [](int value)
+ {
+ printf("%d ", value);
+ });
+ WriteToFile(values);
+}
+
+template<typename Container, typename Delegate>
+void TensorPrinter::ForEachValue(const Container& c, Delegate delegate)
+{
+ std::cout << m_OutputBinding << ": ";
+ for (const auto& value : c)
+ {
+ delegate(value);
+ }
+ printf("\n");
+}
+
+template<typename T>
+void TensorPrinter::WriteToFile(const std::vector<T>& values)
+{
+ if (!m_OutputTensorFile.empty())
+ {
+ std::ofstream outputTensorFile;
+ outputTensorFile.open(m_OutputTensorFile, std::ofstream::out | std::ofstream::trunc);
+ if (outputTensorFile.is_open())
+ {
+ outputTensorFile << m_OutputBinding << ": ";
+ std::copy(values.begin(), values.end(), std::ostream_iterator<T>(outputTensorFile, " "));
+ }
+ else
+ {
+ ARMNN_LOG(info) << "Output Tensor File: " << m_OutputTensorFile << " could not be opened!";
+ }
+ outputTensorFile.close();
+ }
+}
+
+using TContainer = mapbox::util::variant<std::vector<float>, std::vector<int>, std::vector<unsigned char>>;
+using QuantizationParams = std::pair<float, int32_t>;
+
+void PopulateTensorWithData(TContainer& tensorData,
+ unsigned int numElements,
+ const std::string& dataTypeStr,
+ const armnn::Optional<QuantizationParams>& qParams,
+ const armnn::Optional<std::string>& dataFile)
+{
+ const bool readFromFile = dataFile.has_value() && !dataFile.value().empty();
+ const bool quantizeData = qParams.has_value();
+
+ std::ifstream inputTensorFile;
+ if (readFromFile)
+ {
+ inputTensorFile = std::ifstream(dataFile.value());
+ }
+
+ if (dataTypeStr.compare("float") == 0)
+ {
+ if (quantizeData)
+ {
+ const float qScale = qParams.value().first;
+ const int qOffset = qParams.value().second;
+
+ tensorData = readFromFile ?
+ ParseDataArray<armnn::DataType::QAsymmU8>(inputTensorFile, qScale, qOffset) :
+ GenerateDummyTensorData<armnn::DataType::QAsymmU8>(numElements);
+ }
+ else
+ {
+ tensorData = readFromFile ?
+ ParseDataArray<armnn::DataType::Float32>(inputTensorFile) :
+ GenerateDummyTensorData<armnn::DataType::Float32>(numElements);
+ }
+ }
+ else if (dataTypeStr.compare("int") == 0)
+ {
+ tensorData = readFromFile ?
+ ParseDataArray<armnn::DataType::Signed32>(inputTensorFile) :
+ GenerateDummyTensorData<armnn::DataType::Signed32>(numElements);
+ }
+ else if (dataTypeStr.compare("qasymm8") == 0)
+ {
+ tensorData = readFromFile ?
+ ParseDataArray<armnn::DataType::QAsymmU8>(inputTensorFile) :
+ GenerateDummyTensorData<armnn::DataType::QAsymmU8>(numElements);
+ }
+ else
+ {
+ std::string errorMessage = "Unsupported tensor data type " + dataTypeStr;
+ ARMNN_LOG(fatal) << errorMessage;
+
+ inputTensorFile.close();
+ throw armnn::Exception(errorMessage);
+ }
+
+ inputTensorFile.close();
+}
+
+bool ValidatePath(const std::string& file, const bool expectFile)
+{
+ if (!fs::exists(file))
+ {
+ std::cerr << "Given file path '" << file << "' does not exist" << std::endl;
+ return false;
+ }
+ if (!fs::is_regular_file(file) && expectFile)
+ {
+ std::cerr << "Given file path '" << file << "' is not a regular file" << std::endl;
+ return false;
+ }
+ return true;
+}
+
+bool ValidatePaths(const std::vector<std::string>& fileVec, const bool expectFile)
+{
+ bool allPathsValid = true;
+ for (auto const& file : fileVec)
+ {
+ if(!ValidatePath(file, expectFile))
+ {
+ allPathsValid = false;
+ }
+ }
+ return allPathsValid;
+}
+
+
+
diff --git a/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp b/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp
index f79d630..d101d4a 100644
--- a/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp
+++ b/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp
@@ -2,290 +2,50 @@
// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
-#include <armnn/ArmNN.hpp>
-#include <armnn/TypesUtils.hpp>
-#include <armnn/utility/NumericCast.hpp>
-#include <armnn/utility/Timer.hpp>
-#if defined(ARMNN_SERIALIZER)
-#include "armnnDeserializer/IDeserializer.hpp"
-#endif
-#if defined(ARMNN_CAFFE_PARSER)
-#include "armnnCaffeParser/ICaffeParser.hpp"
-#endif
-#if defined(ARMNN_TF_PARSER)
-#include "armnnTfParser/ITfParser.hpp"
-#endif
-#if defined(ARMNN_TF_LITE_PARSER)
-#include "armnnTfLiteParser/ITfLiteParser.hpp"
-#endif
-#if defined(ARMNN_ONNX_PARSER)
-#include "armnnOnnxParser/IOnnxParser.hpp"
-#endif
+#pragma once
+
#include "CsvReader.hpp"
-#include "../InferenceTest.hpp"
+#include <armnn/IRuntime.hpp>
+#include <armnn/Types.hpp>
-#include <Profiling.hpp>
-#include <ResolveType.hpp>
-
-#include <boost/program_options.hpp>
#include <mapbox/variant.hpp>
#include <iostream>
-#include <fstream>
-#include <functional>
-#include <future>
-#include <algorithm>
-#include <iterator>
-namespace
-{
-// Configure boost::program_options for command-line parsing and validation.
-namespace po = boost::program_options;
+std::vector<unsigned int> ParseArray(std::istream& stream);
-template<typename T, typename TParseElementFunc>
-std::vector<T> ParseArrayImpl(std::istream& stream, TParseElementFunc parseElementFunc, const char * chars = "\t ,:")
-{
- std::vector<T> result;
- // Processes line-by-line.
- std::string line;
- while (std::getline(stream, line))
- {
- std::vector<std::string> tokens = armnn::stringUtils::StringTokenizer(line, chars);
- for (const std::string& token : tokens)
- {
- if (!token.empty()) // See https://stackoverflow.com/questions/10437406/
- {
- try
- {
- result.push_back(parseElementFunc(token));
- }
- catch (const std::exception&)
- {
- ARMNN_LOG(error) << "'" << token << "' is not a valid number. It has been ignored.";
- }
- }
- }
- }
-
- return result;
-}
-
-bool CheckOption(const po::variables_map& vm,
- const char* option)
-{
- // Check that the given option is valid.
- if (option == nullptr)
- {
- return false;
- }
-
- // Check whether 'option' is provided.
- return vm.find(option) != vm.end();
-}
-
-void CheckOptionDependency(const po::variables_map& vm,
- const char* option,
- const char* required)
-{
- // Check that the given options are valid.
- if (option == nullptr || required == nullptr)
- {
- throw po::error("Invalid option to check dependency for");
- }
-
- // Check that if 'option' is provided, 'required' is also provided.
- if (CheckOption(vm, option) && !vm[option].defaulted())
- {
- if (CheckOption(vm, required) == 0 || vm[required].defaulted())
- {
- throw po::error(std::string("Option '") + option + "' requires option '" + required + "'.");
- }
- }
-}
-
-void CheckOptionDependencies(const po::variables_map& vm)
-{
- CheckOptionDependency(vm, "model-path", "model-format");
- CheckOptionDependency(vm, "model-path", "input-name");
- CheckOptionDependency(vm, "model-path", "output-name");
- CheckOptionDependency(vm, "input-tensor-shape", "model-path");
-}
-
-template<armnn::DataType NonQuantizedType>
-auto ParseDataArray(std::istream & stream);
-
-template<armnn::DataType QuantizedType>
-auto ParseDataArray(std::istream& stream,
- const float& quantizationScale,
- const int32_t& quantizationOffset);
-
-template<>
-auto ParseDataArray<armnn::DataType::Float32>(std::istream & stream)
-{
- return ParseArrayImpl<float>(stream, [](const std::string& s) { return std::stof(s); });
-}
-
-template<>
-auto ParseDataArray<armnn::DataType::Signed32>(std::istream & stream)
-{
- return ParseArrayImpl<int>(stream, [](const std::string & s) { return std::stoi(s); });
-}
-
-template<>
-auto ParseDataArray<armnn::DataType::QAsymmU8>(std::istream& stream)
-{
- return ParseArrayImpl<uint8_t>(stream,
- [](const std::string& s) { return armnn::numeric_cast<uint8_t>(std::stoi(s)); });
-}
-
-template<>
-auto ParseDataArray<armnn::DataType::QAsymmU8>(std::istream& stream,
- const float& quantizationScale,
- const int32_t& quantizationOffset)
-{
- return ParseArrayImpl<uint8_t>(stream,
- [&quantizationScale, &quantizationOffset](const std::string & s)
- {
- return armnn::numeric_cast<uint8_t>(
- armnn::Quantize<uint8_t>(std::stof(s),
- quantizationScale,
- quantizationOffset));
- });
-}
-std::vector<unsigned int> ParseArray(std::istream& stream)
-{
- return ParseArrayImpl<unsigned int>(stream,
- [](const std::string& s) { return armnn::numeric_cast<unsigned int>(std::stoi(s)); });
-}
-
-std::vector<std::string> ParseStringList(const std::string & inputString, const char * delimiter)
-{
- std::stringstream stream(inputString);
- return ParseArrayImpl<std::string>(stream, [](const std::string& s) {
- return armnn::stringUtils::StringTrimCopy(s); }, delimiter);
-}
-
-void RemoveDuplicateDevices(std::vector<armnn::BackendId>& computeDevices)
-{
- // Mark the duplicate devices as 'Undefined'.
- for (auto i = computeDevices.begin(); i != computeDevices.end(); ++i)
- {
- for (auto j = std::next(i); j != computeDevices.end(); ++j)
- {
- if (*j == *i)
- {
- *j = armnn::Compute::Undefined;
- }
- }
- }
-
- // Remove 'Undefined' devices.
- computeDevices.erase(std::remove(computeDevices.begin(), computeDevices.end(), armnn::Compute::Undefined),
- computeDevices.end());
-}
+/// Splits a given string at every accurance of delimiter into a vector of string
+std::vector<std::string> ParseStringList(const std::string& inputString, const char* delimiter);
struct TensorPrinter
{
TensorPrinter(const std::string& binding,
const armnn::TensorInfo& info,
const std::string& outputTensorFile,
- bool dequantizeOutput)
- : m_OutputBinding(binding)
- , m_Scale(info.GetQuantizationScale())
- , m_Offset(info.GetQuantizationOffset())
- , m_OutputTensorFile(outputTensorFile)
- , m_DequantizeOutput(dequantizeOutput)
- {}
+ bool dequantizeOutput);
- void operator()(const std::vector<float>& values)
- {
- ForEachValue(values, [](float value)
- {
- printf("%f ", value);
- });
- WriteToFile(values);
- }
+ void operator()(const std::vector<float>& values);
- void operator()(const std::vector<uint8_t>& values)
- {
- if(m_DequantizeOutput)
- {
- auto& scale = m_Scale;
- auto& offset = m_Offset;
- std::vector<float> dequantizedValues;
- ForEachValue(values, [&scale, &offset, &dequantizedValues](uint8_t value)
- {
- auto dequantizedValue = armnn::Dequantize(value, scale, offset);
- printf("%f ", dequantizedValue);
- dequantizedValues.push_back(dequantizedValue);
- });
- WriteToFile(dequantizedValues);
- }
- else
- {
- const std::vector<int> intValues(values.begin(), values.end());
- operator()(intValues);
- }
- }
+ void operator()(const std::vector<uint8_t>& values);
- void operator()(const std::vector<int>& values)
- {
- ForEachValue(values, [](int value)
- {
- printf("%d ", value);
- });
- WriteToFile(values);
- }
+ void operator()(const std::vector<int>& values);
private:
template<typename Container, typename Delegate>
- void ForEachValue(const Container& c, Delegate delegate)
- {
- std::cout << m_OutputBinding << ": ";
- for (const auto& value : c)
- {
- delegate(value);
- }
- printf("\n");
- }
+ void ForEachValue(const Container& c, Delegate delegate);
template<typename T>
- void WriteToFile(const std::vector<T>& values)
- {
- if (!m_OutputTensorFile.empty())
- {
- std::ofstream outputTensorFile;
- outputTensorFile.open(m_OutputTensorFile, std::ofstream::out | std::ofstream::trunc);
- if (outputTensorFile.is_open())
- {
- outputTensorFile << m_OutputBinding << ": ";
- std::copy(values.begin(), values.end(), std::ostream_iterator<T>(outputTensorFile, " "));
- }
- else
- {
- ARMNN_LOG(info) << "Output Tensor File: " << m_OutputTensorFile << " could not be opened!";
- }
- outputTensorFile.close();
- }
- }
+ void WriteToFile(const std::vector<T>& values);
std::string m_OutputBinding;
- float m_Scale=0.0f;
- int m_Offset=0;
+ float m_Scale;
+ int m_Offset;
std::string m_OutputTensorFile;
- bool m_DequantizeOutput = false;
+ bool m_DequantizeOutput;
};
-
-
-template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
-std::vector<T> GenerateDummyTensorData(unsigned int numElements)
-{
- return std::vector<T>(numElements, static_cast<T>(0));
-}
-
using TContainer = mapbox::util::variant<std::vector<float>, std::vector<int>, std::vector<unsigned char>>;
using QuantizationParams = std::pair<float, int32_t>;
@@ -293,648 +53,20 @@
unsigned int numElements,
const std::string& dataTypeStr,
const armnn::Optional<QuantizationParams>& qParams,
- const armnn::Optional<std::string>& dataFile)
-{
- const bool readFromFile = dataFile.has_value() && !dataFile.value().empty();
- const bool quantizeData = qParams.has_value();
+ const armnn::Optional<std::string>& dataFile);
- std::ifstream inputTensorFile;
- if (readFromFile)
- {
- inputTensorFile = std::ifstream(dataFile.value());
- }
+/**
+ * Verifies if the given string is a valid path. Reports invalid paths to std::err.
+ * @param file string - A string containing the path to check
+ * @param expectFile bool - If true, checks for a regular file.
+ * @return bool - True if given string is a valid path., false otherwise.
+ * */
+bool ValidatePath(const std::string& file, const bool expectFile);
- if (dataTypeStr.compare("float") == 0)
- {
- if (quantizeData)
- {
- const float qScale = qParams.value().first;
- const int qOffset = qParams.value().second;
-
- tensorData = readFromFile ?
- ParseDataArray<armnn::DataType::QAsymmU8>(inputTensorFile, qScale, qOffset) :
- GenerateDummyTensorData<armnn::DataType::QAsymmU8>(numElements);
- }
- else
- {
- tensorData = readFromFile ?
- ParseDataArray<armnn::DataType::Float32>(inputTensorFile) :
- GenerateDummyTensorData<armnn::DataType::Float32>(numElements);
- }
- }
- else if (dataTypeStr.compare("int") == 0)
- {
- tensorData = readFromFile ?
- ParseDataArray<armnn::DataType::Signed32>(inputTensorFile) :
- GenerateDummyTensorData<armnn::DataType::Signed32>(numElements);
- }
- else if (dataTypeStr.compare("qasymm8") == 0)
- {
- tensorData = readFromFile ?
- ParseDataArray<armnn::DataType::QAsymmU8>(inputTensorFile) :
- GenerateDummyTensorData<armnn::DataType::QAsymmU8>(numElements);
- }
- else
- {
- std::string errorMessage = "Unsupported tensor data type " + dataTypeStr;
- ARMNN_LOG(fatal) << errorMessage;
-
- inputTensorFile.close();
- throw armnn::Exception(errorMessage);
- }
-
- inputTensorFile.close();
-}
-
-} // anonymous namespace
-
-bool generateTensorData = true;
-
-struct ExecuteNetworkParams
-{
- using TensorShapePtr = std::unique_ptr<armnn::TensorShape>;
-
- const char* m_ModelPath;
- bool m_IsModelBinary;
- std::vector<armnn::BackendId> m_ComputeDevices;
- std::string m_DynamicBackendsPath;
- std::vector<string> m_InputNames;
- std::vector<TensorShapePtr> m_InputTensorShapes;
- std::vector<string> m_InputTensorDataFilePaths;
- std::vector<string> m_InputTypes;
- bool m_QuantizeInput;
- std::vector<string> m_OutputTypes;
- std::vector<string> m_OutputNames;
- std::vector<string> m_OutputTensorFiles;
- bool m_DequantizeOutput;
- bool m_EnableProfiling;
- bool m_EnableFp16TurboMode;
- bool m_EnableBf16TurboMode;
- double m_ThresholdTime;
- bool m_PrintIntermediate;
- size_t m_SubgraphId;
- bool m_EnableLayerDetails = false;
- bool m_GenerateTensorData;
- bool m_ParseUnsupported = false;
- bool m_InferOutputShape = false;
- bool m_EnableFastMath = false;
-};
-
-template<typename TParser, typename TDataType>
-int MainImpl(const ExecuteNetworkParams& params,
- const std::shared_ptr<armnn::IRuntime>& runtime = nullptr,
- size_t iterations = 1)
-{
- using TContainer = mapbox::util::variant<std::vector<float>, std::vector<int>, std::vector<unsigned char>>;
-
- std::vector<TContainer> inputDataContainers;
-
- try
- {
- // Creates an InferenceModel, which will parse the model and load it into an IRuntime.
- typename InferenceModel<TParser, TDataType>::Params inferenceModelParams;
- inferenceModelParams.m_ModelPath = params.m_ModelPath;
- inferenceModelParams.m_IsModelBinary = params.m_IsModelBinary;
- inferenceModelParams.m_ComputeDevices = params.m_ComputeDevices;
- inferenceModelParams.m_DynamicBackendsPath = params.m_DynamicBackendsPath;
- inferenceModelParams.m_PrintIntermediateLayers = params.m_PrintIntermediate;
- inferenceModelParams.m_VisualizePostOptimizationModel = params.m_EnableLayerDetails;
- inferenceModelParams.m_ParseUnsupported = params.m_ParseUnsupported;
- inferenceModelParams.m_InferOutputShape = params.m_InferOutputShape;
- inferenceModelParams.m_EnableFastMath = params.m_EnableFastMath;
-
- for(const std::string& inputName: params.m_InputNames)
- {
- inferenceModelParams.m_InputBindings.push_back(inputName);
- }
-
- for(unsigned int i = 0; i < params.m_InputTensorShapes.size(); ++i)
- {
- inferenceModelParams.m_InputShapes.push_back(*params.m_InputTensorShapes[i]);
- }
-
- for(const std::string& outputName: params.m_OutputNames)
- {
- inferenceModelParams.m_OutputBindings.push_back(outputName);
- }
-
- inferenceModelParams.m_SubgraphId = params.m_SubgraphId;
- inferenceModelParams.m_EnableFp16TurboMode = params.m_EnableFp16TurboMode;
- inferenceModelParams.m_EnableBf16TurboMode = params.m_EnableBf16TurboMode;
-
- InferenceModel<TParser, TDataType> model(inferenceModelParams,
- params.m_EnableProfiling,
- params.m_DynamicBackendsPath,
- runtime);
-
- const size_t numInputs = inferenceModelParams.m_InputBindings.size();
- for(unsigned int i = 0; i < numInputs; ++i)
- {
- armnn::Optional<QuantizationParams> qParams = params.m_QuantizeInput ?
- armnn::MakeOptional<QuantizationParams>(model.GetInputQuantizationParams()) :
- armnn::EmptyOptional();
-
- armnn::Optional<std::string> dataFile = params.m_GenerateTensorData ?
- armnn::EmptyOptional() :
- armnn::MakeOptional<std::string>(params.m_InputTensorDataFilePaths[i]);
-
- unsigned int numElements = model.GetInputSize(i);
- if (params.m_InputTensorShapes.size() > i && params.m_InputTensorShapes[i])
- {
- // If the user has provided a tensor shape for the current input,
- // override numElements
- numElements = params.m_InputTensorShapes[i]->GetNumElements();
- }
-
- TContainer tensorData;
- PopulateTensorWithData(tensorData,
- numElements,
- params.m_InputTypes[i],
- qParams,
- dataFile);
-
- inputDataContainers.push_back(tensorData);
- }
-
- const size_t numOutputs = inferenceModelParams.m_OutputBindings.size();
- std::vector<TContainer> outputDataContainers;
-
- for (unsigned int i = 0; i < numOutputs; ++i)
- {
- if (params.m_OutputTypes[i].compare("float") == 0)
- {
- outputDataContainers.push_back(std::vector<float>(model.GetOutputSize(i)));
- }
- else if (params.m_OutputTypes[i].compare("int") == 0)
- {
- outputDataContainers.push_back(std::vector<int>(model.GetOutputSize(i)));
- }
- else if (params.m_OutputTypes[i].compare("qasymm8") == 0)
- {
- outputDataContainers.push_back(std::vector<uint8_t>(model.GetOutputSize(i)));
- }
- else
- {
- ARMNN_LOG(fatal) << "Unsupported tensor data type \"" << params.m_OutputTypes[i] << "\". ";
- return EXIT_FAILURE;
- }
- }
-
- for (size_t x = 0; x < iterations; x++)
- {
- // model.Run returns the inference time elapsed in EnqueueWorkload (in milliseconds)
- auto inference_duration = model.Run(inputDataContainers, outputDataContainers);
-
- if (params.m_GenerateTensorData)
- {
- ARMNN_LOG(warning) << "The input data was generated, note that the output will not be useful";
- }
-
- // Print output tensors
- const auto& infosOut = model.GetOutputBindingInfos();
- for (size_t i = 0; i < numOutputs; i++)
- {
- const armnn::TensorInfo& infoOut = infosOut[i].second;
- auto outputTensorFile = params.m_OutputTensorFiles.empty() ? "" : params.m_OutputTensorFiles[i];
-
- TensorPrinter printer(inferenceModelParams.m_OutputBindings[i],
- infoOut,
- outputTensorFile,
- params.m_DequantizeOutput);
- mapbox::util::apply_visitor(printer, outputDataContainers[i]);
- }
-
- ARMNN_LOG(info) << "\nInference time: " << std::setprecision(2)
- << std::fixed << inference_duration.count() << " ms\n";
-
- // If thresholdTime == 0.0 (default), then it hasn't been supplied at command line
- if (params.m_ThresholdTime != 0.0)
- {
- ARMNN_LOG(info) << "Threshold time: " << std::setprecision(2)
- << std::fixed << params.m_ThresholdTime << " ms";
- auto thresholdMinusInference = params.m_ThresholdTime - inference_duration.count();
- ARMNN_LOG(info) << "Threshold time - Inference time: " << std::setprecision(2)
- << std::fixed << thresholdMinusInference << " ms" << "\n";
-
- if (thresholdMinusInference < 0)
- {
- std::string errorMessage = "Elapsed inference time is greater than provided threshold time.";
- ARMNN_LOG(fatal) << errorMessage;
- }
- }
- }
- }
- catch (const armnn::Exception& e)
- {
- ARMNN_LOG(fatal) << "Armnn Error: " << e.what();
- return EXIT_FAILURE;
- }
-
- return EXIT_SUCCESS;
-}
-
-// This will run a test
-int RunTest(const std::string& format,
- const std::string& inputTensorShapesStr,
- const vector<armnn::BackendId>& computeDevices,
- const std::string& dynamicBackendsPath,
- const std::string& path,
- const std::string& inputNames,
- const std::string& inputTensorDataFilePaths,
- const std::string& inputTypes,
- bool quantizeInput,
- const std::string& outputTypes,
- const std::string& outputNames,
- const std::string& outputTensorFiles,
- bool dequantizeOuput,
- bool enableProfiling,
- bool enableFp16TurboMode,
- bool enableBf16TurboMode,
- const double& thresholdTime,
- bool printIntermediate,
- const size_t subgraphId,
- bool enableLayerDetails = false,
- bool parseUnsupported = false,
- bool inferOutputShape = false,
- bool enableFastMath = false,
- const size_t iterations = 1,
- const std::shared_ptr<armnn::IRuntime>& runtime = nullptr)
-{
- std::string modelFormat = armnn::stringUtils::StringTrimCopy(format);
- std::string modelPath = armnn::stringUtils::StringTrimCopy(path);
- std::vector<std::string> inputNamesVector = ParseStringList(inputNames, ",");
- std::vector<std::string> inputTensorShapesVector = ParseStringList(inputTensorShapesStr, ":");
- std::vector<std::string> inputTensorDataFilePathsVector = ParseStringList(
- inputTensorDataFilePaths, ",");
- std::vector<std::string> outputNamesVector = ParseStringList(outputNames, ",");
- std::vector<std::string> inputTypesVector = ParseStringList(inputTypes, ",");
- std::vector<std::string> outputTypesVector = ParseStringList(outputTypes, ",");
- std::vector<std::string> outputTensorFilesVector = ParseStringList(outputTensorFiles, ",");
-
- // Parse model binary flag from the model-format string we got from the command-line
- bool isModelBinary;
- if (modelFormat.find("bin") != std::string::npos)
- {
- isModelBinary = true;
- }
- else if (modelFormat.find("txt") != std::string::npos || modelFormat.find("text") != std::string::npos)
- {
- isModelBinary = false;
- }
- else
- {
- ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat << "'. Please include 'binary' or 'text'";
- return EXIT_FAILURE;
- }
-
- if ((inputTensorShapesVector.size() != 0) && (inputTensorShapesVector.size() != inputNamesVector.size()))
- {
- ARMNN_LOG(fatal) << "input-name and input-tensor-shape must have the same amount of elements.";
- return EXIT_FAILURE;
- }
-
- if ((inputTensorDataFilePathsVector.size() != 0) &&
- (inputTensorDataFilePathsVector.size() != inputNamesVector.size()))
- {
- ARMNN_LOG(fatal) << "input-name and input-tensor-data must have the same amount of elements.";
- return EXIT_FAILURE;
- }
-
- if ((outputTensorFilesVector.size() != 0) &&
- (outputTensorFilesVector.size() != outputNamesVector.size()))
- {
- ARMNN_LOG(fatal) << "output-name and write-outputs-to-file must have the same amount of elements.";
- return EXIT_FAILURE;
- }
-
- if (inputTypesVector.size() == 0)
- {
- //Defaults the value of all inputs to "float"
- inputTypesVector.assign(inputNamesVector.size(), "float");
- }
- else if ((inputTypesVector.size() != 0) && (inputTypesVector.size() != inputNamesVector.size()))
- {
- ARMNN_LOG(fatal) << "input-name and input-type must have the same amount of elements.";
- return EXIT_FAILURE;
- }
-
- if (outputTypesVector.size() == 0)
- {
- //Defaults the value of all outputs to "float"
- outputTypesVector.assign(outputNamesVector.size(), "float");
- }
- else if ((outputTypesVector.size() != 0) && (outputTypesVector.size() != outputNamesVector.size()))
- {
- ARMNN_LOG(fatal) << "output-name and output-type must have the same amount of elements.";
- return EXIT_FAILURE;
- }
-
- // Parse input tensor shape from the string we got from the command-line.
- std::vector<std::unique_ptr<armnn::TensorShape>> inputTensorShapes;
-
- if (!inputTensorShapesVector.empty())
- {
- inputTensorShapes.reserve(inputTensorShapesVector.size());
-
- for(const std::string& shape : inputTensorShapesVector)
- {
- std::stringstream ss(shape);
- std::vector<unsigned int> dims = ParseArray(ss);
-
- try
- {
- // Coverity fix: An exception of type armnn::InvalidArgumentException is thrown and never caught.
- inputTensorShapes.push_back(
- std::make_unique<armnn::TensorShape>(static_cast<unsigned int>(dims.size()), dims.data()));
- }
- catch (const armnn::InvalidArgumentException& e)
- {
- ARMNN_LOG(fatal) << "Cannot create tensor shape: " << e.what();
- return EXIT_FAILURE;
- }
- }
- }
-
- // Check that threshold time is not less than zero
- if (thresholdTime < 0)
- {
- ARMNN_LOG(fatal) << "Threshold time supplied as a command line argument is less than zero.";
- return EXIT_FAILURE;
- }
-
- ExecuteNetworkParams params;
- params.m_ModelPath = modelPath.c_str();
- params.m_IsModelBinary = isModelBinary;
- params.m_ComputeDevices = computeDevices;
- params.m_DynamicBackendsPath = dynamicBackendsPath;
- params.m_InputNames = inputNamesVector;
- params.m_InputTensorShapes = std::move(inputTensorShapes);
- params.m_InputTensorDataFilePaths = inputTensorDataFilePathsVector;
- params.m_InputTypes = inputTypesVector;
- params.m_QuantizeInput = quantizeInput;
- params.m_OutputTypes = outputTypesVector;
- params.m_OutputNames = outputNamesVector;
- params.m_OutputTensorFiles = outputTensorFilesVector;
- params.m_DequantizeOutput = dequantizeOuput;
- params.m_EnableProfiling = enableProfiling;
- params.m_EnableFp16TurboMode = enableFp16TurboMode;
- params.m_EnableBf16TurboMode = enableBf16TurboMode;
- params.m_ThresholdTime = thresholdTime;
- params.m_PrintIntermediate = printIntermediate;
- params.m_SubgraphId = subgraphId;
- params.m_EnableLayerDetails = enableLayerDetails;
- params.m_GenerateTensorData = inputTensorDataFilePathsVector.empty();
- params.m_ParseUnsupported = parseUnsupported;
- params.m_InferOutputShape = inferOutputShape;
- params.m_EnableFastMath = enableFastMath;
-
- // Warn if ExecuteNetwork will generate dummy input data
- if (params.m_GenerateTensorData)
- {
- ARMNN_LOG(warning) << "No input files provided, input tensors will be filled with 0s.";
- }
-
- // Forward to implementation based on the parser type
- if (modelFormat.find("armnn") != std::string::npos)
- {
-#if defined(ARMNN_SERIALIZER)
- return MainImpl<armnnDeserializer::IDeserializer, float>(params, runtime, iterations);
-#else
- ARMNN_LOG(fatal) << "Not built with serialization support.";
- return EXIT_FAILURE;
-#endif
- }
- else if (modelFormat.find("caffe") != std::string::npos)
- {
-#if defined(ARMNN_CAFFE_PARSER)
- return MainImpl<armnnCaffeParser::ICaffeParser, float>(params, runtime, iterations);
-#else
- ARMNN_LOG(fatal) << "Not built with Caffe parser support.";
- return EXIT_FAILURE;
-#endif
- }
- else if (modelFormat.find("onnx") != std::string::npos)
- {
-#if defined(ARMNN_ONNX_PARSER)
- return MainImpl<armnnOnnxParser::IOnnxParser, float>(params, runtime, iterations);
-#else
- ARMNN_LOG(fatal) << "Not built with Onnx parser support.";
- return EXIT_FAILURE;
-#endif
- }
- else if (modelFormat.find("tensorflow") != std::string::npos)
- {
-#if defined(ARMNN_TF_PARSER)
- return MainImpl<armnnTfParser::ITfParser, float>(params, runtime, iterations);
-#else
- ARMNN_LOG(fatal) << "Not built with Tensorflow parser support.";
- return EXIT_FAILURE;
-#endif
- }
- else if(modelFormat.find("tflite") != std::string::npos)
- {
-#if defined(ARMNN_TF_LITE_PARSER)
- if (! isModelBinary)
- {
- ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat
- << "'. Only 'binary' format supported for tflite files";
- return EXIT_FAILURE;
- }
- return MainImpl<armnnTfLiteParser::ITfLiteParser, float>(params, runtime, iterations);
-#else
- ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat
- << "'. Please include 'caffe', 'tensorflow', 'tflite' or 'onnx'";
- return EXIT_FAILURE;
-#endif
- }
- else
- {
- ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat
- << "'. Please include 'caffe', 'tensorflow', 'tflite' or 'onnx'";
- return EXIT_FAILURE;
- }
-}
-
-int RunCsvTest(const armnnUtils::CsvRow &csvRow, const std::shared_ptr<armnn::IRuntime>& runtime,
- const bool enableProfiling, const bool enableFp16TurboMode, const bool enableBf16TurboMode,
- const double& thresholdTime, const bool printIntermediate, bool enableLayerDetails = false,
- bool parseUnuspported = false, bool inferOutputShape = false, bool enableFastMath = false)
-{
- IgnoreUnused(runtime);
- std::string modelFormat;
- std::string modelPath;
- std::string inputNames;
- std::string inputTensorShapes;
- std::string inputTensorDataFilePaths;
- std::string outputNames;
- std::string inputTypes;
- std::string outputTypes;
- std::string dynamicBackendsPath;
- std::string outputTensorFiles;
-
- size_t subgraphId = 0;
-
- const std::string backendsMessage = std::string("The preferred order of devices to run layers on by default. ")
- + std::string("Possible choices: ")
- + armnn::BackendRegistryInstance().GetBackendIdsAsString();
-
- po::options_description desc("Options");
- try
- {
- desc.add_options()
- ("model-format,f", po::value(&modelFormat),
- "armnn-binary, caffe-binary, caffe-text, tflite-binary, onnx-binary, onnx-text, tensorflow-binary or "
- "tensorflow-text.")
- ("model-path,m", po::value(&modelPath), "Path to model file, e.g. .armnn, .caffemodel, .prototxt, "
- ".tflite, .onnx")
- ("compute,c", po::value<std::vector<armnn::BackendId>>()->multitoken(),
- backendsMessage.c_str())
- ("dynamic-backends-path,b", po::value(&dynamicBackendsPath),
- "Path where to load any available dynamic backend from. "
- "If left empty (the default), dynamic backends will not be used.")
- ("input-name,i", po::value(&inputNames), "Identifier of the input tensors in the network separated by comma.")
- ("subgraph-number,n", po::value<size_t>(&subgraphId)->default_value(0), "Id of the subgraph to be "
- "executed. Defaults to 0.")
- ("input-tensor-shape,s", po::value(&inputTensorShapes),
- "The shape of the input tensors in the network as a flat array of integers separated by comma. "
- "Several shapes can be passed separating them by semicolon. "
- "This parameter is optional, depending on the network.")
- ("input-tensor-data,d", po::value(&inputTensorDataFilePaths)->default_value(""),
- "Path to files containing the input data as a flat array separated by whitespace. "
- "Several paths can be passed separating them by comma. If not specified, the network will be run with dummy "
- "data (useful for profiling).")
- ("input-type,y",po::value(&inputTypes), "The type of the input tensors in the network separated by comma. "
- "If unset, defaults to \"float\" for all defined inputs. "
- "Accepted values (float, int or qasymm8).")
- ("quantize-input,q",po::bool_switch()->default_value(false),
- "If this option is enabled, all float inputs will be quantized to qasymm8. "
- "If unset, default to not quantized. "
- "Accepted values (true or false)")
- ("output-type,z",po::value(&outputTypes), "The type of the output tensors in the network separated by comma. "
- "If unset, defaults to \"float\" for all defined outputs. "
- "Accepted values (float, int or qasymm8).")
- ("output-name,o", po::value(&outputNames),
- "Identifier of the output tensors in the network separated by comma.")
- ("dequantize-output,l",po::bool_switch()->default_value(false),
- "If this option is enabled, all quantized outputs will be dequantized to float. "
- "If unset, default to not get dequantized. "
- "Accepted values (true or false)")
- ("write-outputs-to-file,w", po::value(&outputTensorFiles),
- "Comma-separated list of output file paths keyed with the binding-id of the output slot. "
- "If left empty (the default), the output tensors will not be written to a file.");
- }
- catch (const std::exception& e)
- {
- // Coverity points out that default_value(...) can throw a bad_lexical_cast,
- // and that desc.add_options() can throw boost::io::too_few_args.
- // They really won't in any of these cases.
- ARMNN_ASSERT_MSG(false, "Caught unexpected exception");
- ARMNN_LOG(fatal) << "Fatal internal error: " << e.what();
- return EXIT_FAILURE;
- }
-
- std::vector<const char*> clOptions;
- clOptions.reserve(csvRow.values.size());
- for (const std::string& value : csvRow.values)
- {
- clOptions.push_back(value.c_str());
- }
-
- po::variables_map vm;
- try
- {
- po::store(po::parse_command_line(static_cast<int>(clOptions.size()), clOptions.data(), desc), vm);
-
- po::notify(vm);
-
- CheckOptionDependencies(vm);
- }
- catch (const po::error& e)
- {
- std::cerr << e.what() << std::endl << std::endl;
- std::cerr << desc << std::endl;
- return EXIT_FAILURE;
- }
-
- // Get the value of the switch arguments.
- bool quantizeInput = vm["quantize-input"].as<bool>();
- bool dequantizeOutput = vm["dequantize-output"].as<bool>();
-
- // Get the preferred order of compute devices.
- std::vector<armnn::BackendId> computeDevices = vm["compute"].as<std::vector<armnn::BackendId>>();
-
- // Remove duplicates from the list of compute devices.
- RemoveDuplicateDevices(computeDevices);
-
- // Check that the specified compute devices are valid.
- std::string invalidBackends;
- if (!CheckRequestedBackendsAreValid(computeDevices, armnn::Optional<std::string&>(invalidBackends)))
- {
- ARMNN_LOG(fatal) << "The list of preferred devices contains invalid backend IDs: "
- << invalidBackends;
- return EXIT_FAILURE;
- }
-
- return RunTest(modelFormat, inputTensorShapes, computeDevices, dynamicBackendsPath, modelPath, inputNames,
- inputTensorDataFilePaths, inputTypes, quantizeInput, outputTypes, outputNames, outputTensorFiles,
- dequantizeOutput, enableProfiling, enableFp16TurboMode, enableBf16TurboMode,
- thresholdTime, printIntermediate, subgraphId, enableLayerDetails, parseUnuspported,
- inferOutputShape, enableFastMath);
-}
-
-#if defined(ARMCOMPUTECL_ENABLED)
-int RunCLTuning(const std::string& tuningPath,
- const int tuningLevel,
- const std::string& modelFormat,
- const std::string& inputTensorShapes,
- const vector<armnn::BackendId>& computeDevices,
- const std::string& dynamicBackendsPath,
- const std::string& modelPath,
- const std::string& inputNames,
- const std::string& inputTensorDataFilePaths,
- const std::string& inputTypes,
- bool quantizeInput,
- const std::string& outputTypes,
- const std::string& outputNames,
- const std::string& outputTensorFiles,
- bool dequantizeOutput,
- bool enableProfiling,
- bool enableFp16TurboMode,
- bool enableBf16TurboMode,
- const double& thresholdTime,
- bool printIntermediate,
- const size_t subgraphId,
- bool enableLayerDetails = false,
- bool parseUnsupported = false,
- bool inferOutputShape = false,
- bool enableFastMath = false)
-{
- armnn::IRuntime::CreationOptions options;
- options.m_BackendOptions.emplace_back(
- armnn::BackendOptions
- {
- "GpuAcc",
- {
- {"TuningLevel", tuningLevel},
- {"TuningFile", tuningPath.c_str()},
- {"KernelProfilingEnabled", enableProfiling}
- }
- }
- );
-
- std::shared_ptr<armnn::IRuntime> runtime(armnn::IRuntime::Create(options));
- const auto start_time = armnn::GetTimeNow();
-
- ARMNN_LOG(info) << "Tuning run...\n";
- int state = RunTest(modelFormat, inputTensorShapes, computeDevices, dynamicBackendsPath, modelPath, inputNames,
- inputTensorDataFilePaths, inputTypes, quantizeInput, outputTypes, outputNames,
- outputTensorFiles, dequantizeOutput, enableProfiling, enableFp16TurboMode, enableBf16TurboMode,
- thresholdTime, printIntermediate, subgraphId, enableLayerDetails, parseUnsupported,
- inferOutputShape, enableFastMath, 1, runtime);
-
- ARMNN_LOG(info) << "Tuning time: " << std::setprecision(2)
- << std::fixed << armnn::GetTimeDuration(start_time).count() << " ms\n";
-
- return state;
-}
-#endif
\ No newline at end of file
+/**
+ * Verifies if a given vector of strings are valid paths. Reports invalid paths to std::err.
+ * @param fileVec vector of string - A vector of string containing the paths to check
+ * @param expectFile bool - If true, checks for a regular file.
+ * @return bool - True if all given strings are valid paths., false otherwise.
+ * */
+bool ValidatePaths(const std::vector<std::string>& fileVec, const bool expectFile);
\ No newline at end of file