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-rw-r--r--tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp154
1 files changed, 107 insertions, 47 deletions
diff --git a/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp b/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp
index a922228689..ec0eaf90f8 100644
--- a/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp
+++ b/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp
@@ -4,6 +4,7 @@
//
#include <armnn/ArmNN.hpp>
#include <armnn/TypesUtils.hpp>
+#include <armnn/utility/Timer.hpp>
#if defined(ARMNN_SERIALIZER)
#include "armnnDeserializer/IDeserializer.hpp"
@@ -378,7 +379,8 @@ struct ExecuteNetworkParams
template<typename TParser, typename TDataType>
int MainImpl(const ExecuteNetworkParams& params,
- const std::shared_ptr<armnn::IRuntime>& runtime = nullptr)
+ const std::shared_ptr<armnn::IRuntime>& runtime = nullptr,
+ size_t iterations = 1)
{
using TContainer = boost::variant<std::vector<float>, std::vector<int>, std::vector<unsigned char>>;
@@ -473,44 +475,47 @@ int MainImpl(const ExecuteNetworkParams& params,
}
}
- // model.Run returns the inference time elapsed in EnqueueWorkload (in milliseconds)
- auto inference_duration = model.Run(inputDataContainers, outputDataContainers);
-
- if (params.m_GenerateTensorData)
+ for (size_t x = 0; x < iterations; x++)
{
- ARMNN_LOG(warning) << "The input data was generated, note that the output will not be useful";
- }
+ // model.Run returns the inference time elapsed in EnqueueWorkload (in milliseconds)
+ auto inference_duration = model.Run(inputDataContainers, outputDataContainers);
- // 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);
- boost::apply_visitor(printer, outputDataContainers[i]);
- }
+ if (params.m_GenerateTensorData)
+ {
+ ARMNN_LOG(warning) << "The input data was generated, note that the output will not be useful";
+ }
- ARMNN_LOG(info) << "\nInference time: " << std::setprecision(2)
- << std::fixed << inference_duration.count() << " ms";
+ // 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);
+ boost::apply_visitor(printer, outputDataContainers[i]);
+ }
- // 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";
+ ARMNN_LOG(info) << "\nInference time: " << std::setprecision(2)
+ << std::fixed << inference_duration.count() << " ms\n";
- if (thresholdMinusInference < 0)
+ // If thresholdTime == 0.0 (default), then it hasn't been supplied at command line
+ if (params.m_ThresholdTime != 0.0)
{
- std::string errorMessage = "Elapsed inference time is greater than provided threshold time.";
- ARMNN_LOG(fatal) << errorMessage;
+ 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;
+ }
}
}
}
@@ -545,6 +550,7 @@ int RunTest(const std::string& format,
const size_t subgraphId,
bool enableLayerDetails = false,
bool parseUnsupported = false,
+ const size_t iterations = 1,
const std::shared_ptr<armnn::IRuntime>& runtime = nullptr)
{
std::string modelFormat = armnn::stringUtils::StringTrimCopy(format);
@@ -682,34 +688,34 @@ int RunTest(const std::string& format,
if (modelFormat.find("armnn") != std::string::npos)
{
#if defined(ARMNN_SERIALIZER)
- return MainImpl<armnnDeserializer::IDeserializer, float>(params, runtime);
+ return MainImpl<armnnDeserializer::IDeserializer, float>(params, runtime, iterations);
#else
ARMNN_LOG(fatal) << "Not built with serialization support.";
- return EXIT_FAILURE;
+ return EXIT_FAILURE;
#endif
}
else if (modelFormat.find("caffe") != std::string::npos)
{
#if defined(ARMNN_CAFFE_PARSER)
- return MainImpl<armnnCaffeParser::ICaffeParser, float>(params, runtime);
+ 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);
+ return MainImpl<armnnOnnxParser::IOnnxParser, float>(params, runtime, iterations);
#else
ARMNN_LOG(fatal) << "Not built with Onnx parser support.";
- return EXIT_FAILURE;
+ return EXIT_FAILURE;
#endif
}
else if (modelFormat.find("tensorflow") != std::string::npos)
{
#if defined(ARMNN_TF_PARSER)
- return MainImpl<armnnTfParser::ITfParser, float>(params, runtime);
+ return MainImpl<armnnTfParser::ITfParser, float>(params, runtime, iterations);
#else
ARMNN_LOG(fatal) << "Not built with Tensorflow parser support.";
return EXIT_FAILURE;
@@ -720,21 +726,21 @@ int RunTest(const std::string& format,
#if defined(ARMNN_TF_LITE_PARSER)
if (! isModelBinary)
{
- ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat << "'. Only 'binary' format supported \
- for tflite files";
+ ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat
+ << "'. Only 'binary' format supported for tflite files";
return EXIT_FAILURE;
}
- return MainImpl<armnnTfLiteParser::ITfLiteParser, float>(params, runtime);
+ return MainImpl<armnnTfLiteParser::ITfLiteParser, float>(params, runtime, iterations);
#else
- ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat <<
- "'. Please include 'caffe', 'tensorflow', 'tflite' or 'onnx'";
+ 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'";
+ ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat
+ << "'. Please include 'caffe', 'tensorflow', 'tflite' or 'onnx'";
return EXIT_FAILURE;
}
}
@@ -864,3 +870,57 @@ int RunCsvTest(const armnnUtils::CsvRow &csvRow, const std::shared_ptr<armnn::IR
dequantizeOutput, enableProfiling, enableFp16TurboMode, enableBf16TurboMode,
thresholdTime, printIntermediate, subgraphId, enableLayerDetails, parseUnuspported);
}
+
+#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)
+{
+ 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, 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
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-19 07:03:58 +0000