IVGCVSW-2900 Adding the Accuracy Checker Tool and tests
Change-Id: I4ac325e45f2236b8e0757d21046f117024ce3979
Signed-off-by: Éanna Ó Catháin <eanna.ocathain@arm.com>
diff --git a/CMakeLists.txt b/CMakeLists.txt
index b3056c9..c54c395 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -42,6 +42,8 @@
src/armnnUtils/HeapProfiling.hpp
src/armnnUtils/LeakChecking.cpp
src/armnnUtils/LeakChecking.hpp
+ src/armnnUtils/ModelAccuracyChecker.cpp
+ src/armnnUtils/ModelAccuracyChecker.hpp
src/armnnUtils/CsvReader.cpp
src/armnnUtils/CsvReader.hpp
src/armnnUtils/FloatingPointConverter.cpp
@@ -455,6 +457,7 @@
src/armnn/test/GraphUtils.hpp
src/armnn/test/InstrumentTests.cpp
src/armnn/test/LayerValidateOutputTest.cpp
+ src/armnn/test/ModelAccuracyCheckerTest.cpp
src/armnn/test/NetworkTests.cpp
src/armnn/test/ObservableTest.cpp
src/armnn/test/OptimizerTests.cpp
diff --git a/src/armnn/test/ModelAccuracyCheckerTest.cpp b/src/armnn/test/ModelAccuracyCheckerTest.cpp
new file mode 100644
index 0000000..f3a6c9d
--- /dev/null
+++ b/src/armnn/test/ModelAccuracyCheckerTest.cpp
@@ -0,0 +1,98 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#include "ModelAccuracyChecker.hpp"
+
+#include <boost/algorithm/string.hpp>
+#include <boost/test/unit_test.hpp>
+
+#include <iostream>
+#include <string>
+#include <boost/log/core/core.hpp>
+#include <boost/filesystem.hpp>
+#include <boost/optional.hpp>
+#include <boost/variant.hpp>
+
+using namespace armnnUtils;
+
+struct TestHelper {
+ const std::map<std::string, int> GetValidationLabelSet()
+ {
+ std::map<std::string, int> validationLabelSet;
+ validationLabelSet.insert( std::make_pair("ILSVRC2012_val_00000001", 2));
+ validationLabelSet.insert( std::make_pair("ILSVRC2012_val_00000002", 9));
+ validationLabelSet.insert( std::make_pair("ILSVRC2012_val_00000003", 1));
+ validationLabelSet.insert( std::make_pair("ILSVRC2012_val_00000004", 6));
+ validationLabelSet.insert( std::make_pair("ILSVRC2012_val_00000005", 5));
+ validationLabelSet.insert( std::make_pair("ILSVRC2012_val_00000006", 0));
+ validationLabelSet.insert( std::make_pair("ILSVRC2012_val_00000007", 8));
+ validationLabelSet.insert( std::make_pair("ILSVRC2012_val_00000008", 4));
+ validationLabelSet.insert( std::make_pair("ILSVRC2012_val_00000009", 3));
+ validationLabelSet.insert( std::make_pair("ILSVRC2012_val_00000009", 7));
+
+ return validationLabelSet;
+ }
+};
+
+BOOST_AUTO_TEST_SUITE(ModelAccuracyCheckerTest)
+
+using TContainer = boost::variant<std::vector<float>, std::vector<int>, std::vector<unsigned char>>;
+
+BOOST_FIXTURE_TEST_CASE(TestFloat32OutputTensorAccuracy, TestHelper)
+{
+ ModelAccuracyChecker checker(GetValidationLabelSet());
+
+ // Add image 1 and check accuracy
+ std::vector<float> inferenceOutputVector1 = {0.05f, 0.10f, 0.70f, 0.15f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f};
+ TContainer inference1Container(inferenceOutputVector1);
+ std::vector<TContainer> outputTensor1;
+ outputTensor1.push_back(inference1Container);
+
+ std::string imageName = "ILSVRC2012_val_00000001.JPEG";
+ checker.AddImageResult<TContainer>(imageName, outputTensor1);
+
+ // Top 1 Accuracy
+ float totalAccuracy = checker.GetAccuracy(1);
+ BOOST_CHECK(totalAccuracy == 100.0f);
+
+ // Add image 2 and check accuracy
+ std::vector<float> inferenceOutputVector2 = {0.10f, 0.0f, 0.0f, 0.0f, 0.05f, 0.70f, 0.0f, 0.0f, 0.0f, 0.15f};
+ TContainer inference2Container(inferenceOutputVector2);
+ std::vector<TContainer> outputTensor2;
+ outputTensor2.push_back(inference2Container);
+
+ imageName = "ILSVRC2012_val_00000002.JPEG";
+ checker.AddImageResult<TContainer>(imageName, outputTensor2);
+
+ // Top 1 Accuracy
+ totalAccuracy = checker.GetAccuracy(1);
+ BOOST_CHECK(totalAccuracy == 50.0f);
+
+ // Top 2 Accuracy
+ totalAccuracy = checker.GetAccuracy(2);
+ BOOST_CHECK(totalAccuracy == 100.0f);
+
+ // Add image 3 and check accuracy
+ std::vector<float> inferenceOutputVector3 = {0.0f, 0.10f, 0.0f, 0.0f, 0.05f, 0.70f, 0.0f, 0.0f, 0.0f, 0.15f};
+ TContainer inference3Container(inferenceOutputVector3);
+ std::vector<TContainer> outputTensor3;
+ outputTensor3.push_back(inference3Container);
+
+ imageName = "ILSVRC2012_val_00000003.JPEG";
+ checker.AddImageResult<TContainer>(imageName, outputTensor3);
+
+ // Top 1 Accuracy
+ totalAccuracy = checker.GetAccuracy(1);
+ BOOST_CHECK(totalAccuracy == 33.3333321f);
+
+ // Top 2 Accuracy
+ totalAccuracy = checker.GetAccuracy(2);
+ BOOST_CHECK(totalAccuracy == 66.6666641f);
+
+ // Top 3 Accuracy
+ totalAccuracy = checker.GetAccuracy(3);
+ BOOST_CHECK(totalAccuracy == 100.0f);
+}
+
+BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/armnnUtils/ModelAccuracyChecker.cpp b/src/armnnUtils/ModelAccuracyChecker.cpp
new file mode 100644
index 0000000..bee5ca2
--- /dev/null
+++ b/src/armnnUtils/ModelAccuracyChecker.cpp
@@ -0,0 +1,31 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <vector>
+#include <map>
+#include <boost/log/trivial.hpp>
+#include "ModelAccuracyChecker.hpp"
+
+namespace armnnUtils
+{
+
+armnnUtils::ModelAccuracyChecker::ModelAccuracyChecker(const std::map<std::string, int>& validationLabels)
+ : m_GroundTruthLabelSet(validationLabels){}
+
+float ModelAccuracyChecker::GetAccuracy(unsigned int k)
+{
+ if(k > 10) {
+ BOOST_LOG_TRIVIAL(info) << "Accuracy Tool only supports a maximum of Top 10 Accuracy. "
+ "Printing Top 10 Accuracy result!";
+ k = 10;
+ }
+ unsigned int total = 0;
+ for (unsigned int i = k; i > 0; --i)
+ {
+ total += m_TopK[i];
+ }
+ return static_cast<float>(total * 100) / static_cast<float>(m_ImagesProcessed);
+}
+}
\ No newline at end of file
diff --git a/src/armnnUtils/ModelAccuracyChecker.hpp b/src/armnnUtils/ModelAccuracyChecker.hpp
new file mode 100644
index 0000000..abf994b
--- /dev/null
+++ b/src/armnnUtils/ModelAccuracyChecker.hpp
@@ -0,0 +1,103 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <cstddef>
+#include <string>
+#include <map>
+#include <vector>
+#include <boost/variant/apply_visitor.hpp>
+#include <iostream>
+#include <armnn/Types.hpp>
+#include <functional>
+#include <algorithm>
+
+namespace armnnUtils
+{
+
+using namespace armnn;
+
+class ModelAccuracyChecker
+{
+public:
+ ModelAccuracyChecker(const std::map<std::string, int>& validationLabelSet);
+
+ float GetAccuracy(unsigned int k);
+
+ template<typename TContainer>
+ void AddImageResult(const std::string& imageName, std::vector<TContainer> outputTensor)
+ {
+ // Increment the total number of images processed
+ ++m_ImagesProcessed;
+
+ std::map<int, float> confidenceMap;
+ auto & output = outputTensor[0];
+
+ // Create a map of all predictions
+ boost::apply_visitor([&](auto && value)
+ {
+ int index = 0;
+ for (const auto & o : value)
+ {
+ if (o > 0)
+ {
+ confidenceMap.insert(std::pair<int, float>(index, static_cast<float>(o)));
+ }
+ ++index;
+ }
+ },
+ output);
+
+ // Create a comparator for sorting the map in order of highest probability
+ typedef std::function<bool(std::pair<int, float>, std::pair<int, float>)> Comparator;
+
+ Comparator compFunctor =
+ [](std::pair<int, float> element1, std::pair<int, float> element2)
+ {
+ return element1.second > element2.second;
+ };
+
+ // Do the sorting and store in an ordered set
+ std::set<std::pair<int, float>, Comparator> setOfPredictions(
+ confidenceMap.begin(), confidenceMap.end(), compFunctor);
+
+ std::string trimmedName = GetTrimmedImageName(imageName);
+ int value = m_GroundTruthLabelSet.find(trimmedName)->second;
+
+ unsigned int index = 1;
+ for (std::pair<int, float> element : setOfPredictions)
+ {
+ if(element.first == value)
+ {
+ ++m_TopK[index];
+ } else
+ {
+ ++index;
+ }
+ }
+ }
+
+ std::string GetTrimmedImageName(const std::string& imageName) const
+ {
+ std::string trimmedName;
+ size_t lastindex = imageName.find_last_of(".");
+ if(lastindex != std::string::npos)
+ {
+ trimmedName = imageName.substr(0, lastindex);
+ } else
+ {
+ trimmedName = imageName;
+ }
+ return trimmedName;
+ }
+
+private:
+ const std::map<std::string, int> m_GroundTruthLabelSet;
+ std::vector<unsigned int> m_TopK = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+ unsigned int m_ImagesProcessed = 0;
+};
+} //namespace armnnUtils
+
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 028fc82..dfcf4b4 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -291,6 +291,32 @@
addDllCopyCommands(ExecuteNetwork)
endif()
+if(BUILD_ACCURACY_TOOL)
+ macro(AccuracyTool executorName)
+ target_link_libraries(${executorName} ${CMAKE_THREAD_LIBS_INIT})
+ if(OPENCL_LIBRARIES)
+ target_link_libraries(${executorName} ${OPENCL_LIBRARIES})
+ endif()
+ target_link_libraries(${executorName}
+ ${Boost_SYSTEM_LIBRARY}
+ ${Boost_FILESYSTEM_LIBRARY}
+ ${Boost_PROGRAM_OPTIONS_LIBRARY})
+ addDllCopyCommands(${executorName})
+ endmacro()
+
+ set(ModelAccuracyTool-Armnn_sources
+ ModelAccuracyTool-Armnn/ModelAccuracyTool-Armnn.cpp)
+
+ add_executable_ex(ModelAccuracyTool ${ModelAccuracyTool-Armnn_sources})
+ target_include_directories(ModelAccuracyTool PRIVATE ../src/armnn)
+ target_include_directories(ModelAccuracyTool PRIVATE ../src/armnnUtils)
+ target_include_directories(ModelAccuracyTool PRIVATE ../src/backends)
+ target_link_libraries(ModelAccuracyTool inferenceTest)
+ target_link_libraries(ModelAccuracyTool armnn)
+ target_link_libraries(ModelAccuracyTool armnnSerializer)
+ AccuracyTool(ModelAccuracyTool)
+endif()
+
if(BUILD_ARMNN_QUANTIZER)
macro(ImageTensorExecutor executorName)
target_link_libraries(${executorName} ${CMAKE_THREAD_LIBS_INIT})
diff --git a/tests/InferenceTest.cpp b/tests/InferenceTest.cpp
index 89e78de..cf97459 100644
--- a/tests/InferenceTest.cpp
+++ b/tests/InferenceTest.cpp
@@ -92,6 +92,12 @@
bool ValidateDirectory(std::string& dir)
{
+ if (dir.empty())
+ {
+ std::cerr << "No directory specified" << std::endl;
+ return false;
+ }
+
if (dir[dir.length() - 1] != '/')
{
dir += "/";
@@ -103,6 +109,12 @@
return false;
}
+ if (!boost::filesystem::is_directory(dir))
+ {
+ std::cerr << "Given directory [" << dir << "] is not a directory" << std::endl;
+ return false;
+ }
+
return true;
}
diff --git a/tests/ModelAccuracyTool-Armnn/ModelAccuracyTool-Armnn.cpp b/tests/ModelAccuracyTool-Armnn/ModelAccuracyTool-Armnn.cpp
new file mode 100644
index 0000000..7b96830
--- /dev/null
+++ b/tests/ModelAccuracyTool-Armnn/ModelAccuracyTool-Armnn.cpp
@@ -0,0 +1,289 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ModelAccuracyChecker.hpp"
+#include "../InferenceTest.hpp"
+#include "../ImagePreprocessor.hpp"
+#include "armnnDeserializer/IDeserializer.hpp"
+
+#include <boost/filesystem.hpp>
+#include <boost/range/iterator_range.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+using namespace armnn::test;
+
+namespace po = boost::program_options;
+
+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();
+}
+
+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;
+ try
+ {
+ // Coverity fix: boost::split() may throw an exception of type boost::bad_function_call.
+ boost::split(tokens, line, boost::algorithm::is_any_of(chars), boost::token_compress_on);
+ }
+ catch (const std::exception& e)
+ {
+ BOOST_LOG_TRIVIAL(error) << "An error occurred when splitting tokens: " << e.what();
+ continue;
+ }
+ 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&)
+ {
+ BOOST_LOG_TRIVIAL(error) << "'" << token << "' is not a valid number. It has been ignored.";
+ }
+ }
+ }
+ }
+
+ return result;
+}
+
+map<std::string, int> LoadValidationLabels(const string & validationLabelPath);
+
+template<armnn::DataType NonQuantizedType>
+auto ParseDataArray(std::istream & stream);
+
+template<>
+auto ParseDataArray<armnn::DataType::Float32>(std::istream & stream)
+{
+ return ParseArrayImpl<float>(stream, [](const std::string& s) { return std::stof(s); });
+}
+
+int main(int argc, char* argv[])
+{
+ try
+ {
+ using namespace boost::filesystem;
+ armnn::LogSeverity level = armnn::LogSeverity::Debug;
+ armnn::ConfigureLogging(true, true, level);
+ armnnUtils::ConfigureLogging(boost::log::core::get().get(), true, true, level);
+
+ // Set-up program Options
+ namespace po = boost::program_options;
+
+ std::vector<armnn::BackendId> computeDevice;
+ std::vector<armnn::BackendId> defaultBackends = {armnn::Compute::CpuAcc, armnn::Compute::CpuRef};
+ std::string modelPath;
+ std::string dataDir;
+ std::string inputName;
+ std::string outputName;
+ std::string validationLabelPath;
+
+ const std::string backendsMessage = "Which device to run layers on by default. Possible choices: "
+ + armnn::BackendRegistryInstance().GetBackendIdsAsString();
+
+ po::options_description desc("Options");
+ try
+ {
+ // Adds generic options needed to run Accuracy Tool.
+ desc.add_options()
+ ("help", "Display help messages")
+ ("model-path,m", po::value<std::string>(&modelPath)->required(), "Path to armnn format model file")
+ ("compute,c", po::value<std::vector<armnn::BackendId>>(&computeDevice)->default_value(defaultBackends),
+ backendsMessage.c_str())
+ ("data-dir,d", po::value<std::string>(&dataDir)->required(),
+ "Path to directory containing the ImageNet test data")
+ ("input-name,i", po::value<std::string>(&inputName)->required(),
+ "Identifier of the input tensors in the network separated by comma.")
+ ("output-name,o", po::value<std::string>(&outputName)->required(),
+ "Identifier of the output tensors in the network separated by comma.")
+ ("validation-labels-path,v", po::value<std::string>(&validationLabelPath)->required(),
+ "Path to ImageNet Validation Label 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.
+ BOOST_ASSERT_MSG(false, "Caught unexpected exception");
+ std::cerr << "Fatal internal error: " << e.what() << std::endl;
+ return 1;
+ }
+
+ po::variables_map vm;
+ try
+ {
+ po::store(po::parse_command_line(argc, argv, desc), vm);
+
+ if (vm.count("help"))
+ {
+ std::cout << desc << std::endl;
+ return 1;
+ }
+ po::notify(vm);
+ }
+ catch (po::error& e)
+ {
+ std::cerr << e.what() << std::endl << std::endl;
+ std::cerr << desc << std::endl;
+ return 1;
+ }
+
+ // Check if the requested backend are all valid
+ std::string invalidBackends;
+ if (!CheckRequestedBackendsAreValid(computeDevice, armnn::Optional<std::string&>(invalidBackends)))
+ {
+ BOOST_LOG_TRIVIAL(fatal) << "The list of preferred devices contains invalid backend IDs: "
+ << invalidBackends;
+ return EXIT_FAILURE;
+ }
+ armnn::Status status;
+
+ // Create runtime
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+ std::ifstream file(modelPath);
+
+ // Create Parser
+ using IParser = armnnDeserializer::IDeserializer;
+ auto armnnparser(IParser::Create());
+
+ // Create a network
+ armnn::INetworkPtr network = armnnparser->CreateNetworkFromBinary(file);
+
+ // Optimizes the network.
+ armnn::IOptimizedNetworkPtr optimizedNet(nullptr, nullptr);
+ try
+ {
+ optimizedNet = armnn::Optimize(*network, computeDevice, runtime->GetDeviceSpec());
+ }
+ catch (armnn::Exception& e)
+ {
+ std::stringstream message;
+ message << "armnn::Exception (" << e.what() << ") caught from optimize.";
+ BOOST_LOG_TRIVIAL(fatal) << message.str();
+ return 1;
+ }
+
+ // Loads the network into the runtime.
+ armnn::NetworkId networkId;
+ status = runtime->LoadNetwork(networkId, std::move(optimizedNet));
+ if (status == armnn::Status::Failure)
+ {
+ BOOST_LOG_TRIVIAL(fatal) << "armnn::IRuntime: Failed to load network";
+ return 1;
+ }
+
+ // Set up Network
+ using BindingPointInfo = InferenceModelInternal::BindingPointInfo;
+
+ const armnnDeserializer::BindingPointInfo&
+ inputBindingInfo = armnnparser->GetNetworkInputBindingInfo(0, inputName);
+
+ std::pair<armnn::LayerBindingId, armnn::TensorInfo>
+ m_InputBindingInfo(inputBindingInfo.m_BindingId, inputBindingInfo.m_TensorInfo);
+ std::vector<BindingPointInfo> inputBindings = { m_InputBindingInfo };
+
+ const armnnDeserializer::BindingPointInfo&
+ outputBindingInfo = armnnparser->GetNetworkOutputBindingInfo(0, outputName);
+
+ std::pair<armnn::LayerBindingId, armnn::TensorInfo>
+ m_OutputBindingInfo(outputBindingInfo.m_BindingId, outputBindingInfo.m_TensorInfo);
+ std::vector<BindingPointInfo> outputBindings = { m_OutputBindingInfo };
+
+ path pathToDataDir(dataDir);
+ map<string, int> validationLabels = LoadValidationLabels(validationLabelPath);
+ armnnUtils::ModelAccuracyChecker checker(validationLabels);
+ using TContainer = boost::variant<std::vector<float>, std::vector<int>, std::vector<uint8_t>>;
+
+ if(ValidateDirectory(dataDir))
+ {
+ for (auto & imageEntry : boost::make_iterator_range(directory_iterator(pathToDataDir), {}))
+ {
+ cout << "Processing image: " << imageEntry << "\n";
+
+ std::ifstream inputTensorFile(imageEntry.path().string());
+ vector<TContainer> inputDataContainers;
+ inputDataContainers.push_back(ParseDataArray<armnn::DataType::Float32>(inputTensorFile));
+ vector<TContainer> outputDataContainers = {vector<float>(1001)};
+
+ status = runtime->EnqueueWorkload(networkId,
+ armnnUtils::MakeInputTensors(inputBindings, inputDataContainers),
+ armnnUtils::MakeOutputTensors(outputBindings, outputDataContainers));
+
+ if (status == armnn::Status::Failure)
+ {
+ BOOST_LOG_TRIVIAL(fatal) << "armnn::IRuntime: Failed to enqueue workload for image: " << imageEntry;
+ }
+
+ const std::string imageName = imageEntry.path().filename().string();
+ checker.AddImageResult<TContainer>(imageName, outputDataContainers);
+ }
+ }
+ else
+ {
+ return 1;
+ }
+
+ for(unsigned int i = 1; i <= 5; ++i)
+ {
+ std::cout << "Top " << i << " Accuracy: " << checker.GetAccuracy(i) << "%" << "\n";
+ }
+
+ BOOST_LOG_TRIVIAL(info) << "Accuracy Tool ran successfully!";
+ return 0;
+ }
+ catch (armnn::Exception const & 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 << "Armnn Error: " << e.what() << std::endl;
+ return 1;
+ }
+ catch (const std::exception & e)
+ {
+ // Coverity fix: various boost exceptions can be thrown by methods called by this test.
+ std::cerr << "WARNING: ModelAccuracyTool-Armnn: An error has occurred when running the "
+ "Accuracy Tool: " << e.what() << std::endl;
+ return 1;
+ }
+}
+
+map<std::string, int> LoadValidationLabels(const string & validationLabelPath)
+{
+ std::string imageName;
+ int classification;
+ map<std::string, int> validationLabel;
+ ifstream infile(validationLabelPath);
+ while (infile >> imageName >> classification)
+ {
+ std::string trimmedName;
+ size_t lastindex = imageName.find_last_of(".");
+ if(lastindex != std::string::npos)
+ {
+ trimmedName = imageName.substr(0, lastindex);
+ }
+ validationLabel.insert(pair<string, int>(trimmedName, classification));
+ }
+ return validationLabel;
+}