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Run on all devices even if other device types fail (#186) 

* Run on all device types even if the other device types fail

* Renamed models to model

* Fixed test failure so that last failure HRESULT from create session / bind / eval can be returned

* Made changes based on PR comments
This commit is contained in:
Ryan Lai 2019-03-06 11:16:02 -08:00 коммит произвёл GitHub
Родитель c208a75895
Коммит 42ee5b45de
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Идентификатор ключа GPG: 4AEE18F83AFDEB23
1 изменённых файлов: 66 добавлений и 55 удалений
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121
Tools/WinMLRunner/src/Run.cpp
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@ -359,6 +359,64 @@ HRESULT EvaluateModel(const LearningModel& model, const CommandLineArgs& args, O
return S_OK;
}
HRESULT EvaluateModelWithDeviceType(const LearningModel& model, const DeviceType deviceType,
const std::vector<InputBindingType>& inputBindingTypes,
const std::vector<InputDataType>& inputDataTypes,
const std::vector<DeviceCreationLocation> deviceCreationLocations,
const CommandLineArgs& args, const std::wstring& modelPath, OutputHelper& output,
Profiler<WINML_MODEL_TEST_PERF>& profiler,
TensorFeatureDescriptor& tensorDescriptor)
{
for (const auto& inputBindingType : inputBindingTypes)
{
for (const auto& inputDataType : inputDataTypes)
{
for (const auto& deviceCreationLocation : deviceCreationLocations)
{
if (args.IsPerformanceCapture() || args.IsPerIterationCapture())
{
// Resets all values from profiler for bind and evaluate.
profiler.Reset(WINML_MODEL_TEST_PERF::BIND_VALUE, WINML_MODEL_TEST_PERF::COUNT);
}
if (inputDataType != InputDataType::Tensor)
{
// Currently GPU binding only work with 4D tensors and RGBA/BGRA images
if (tensorDescriptor.Shape().Size() != 4 || tensorDescriptor.Shape().GetAt(1) != 3)
{
continue;
}
}
HRESULT evalHResult = EvaluateModel(model, args, output, deviceType, inputBindingType, inputDataType,
deviceCreationLocation, profiler);
if (FAILED(evalHResult))
{
return evalHResult;
}
if (args.IsPerformanceCapture())
{
output.PrintResults(profiler, args.NumIterations(), deviceType, inputBindingType, inputDataType,
deviceCreationLocation, args.IsPerformanceConsoleOutputVerbose());
if (args.IsOutputPerf())
{
std::string deviceTypeStringified = TypeHelper::Stringify(deviceType);
std::string inputDataTypeStringified = TypeHelper::Stringify(inputDataType);
std::string inputBindingTypeStringified = TypeHelper::Stringify(inputBindingType);
std::string deviceCreationLocationStringified = TypeHelper::Stringify(deviceCreationLocation);
output.WritePerformanceDataToCSV(
profiler, args.NumIterations(), modelPath, deviceTypeStringified, inputDataTypeStringified,
inputBindingTypeStringified, deviceCreationLocationStringified);
}
}
}
}
}
return S_OK;
}
HRESULT EvaluateModels(const std::vector<std::wstring>& modelPaths, const std::vector<DeviceType>& deviceTypes,
const std::vector<InputBindingType>& inputBindingTypes,
const std::vector<InputDataType>& inputDataTypes,
@ -366,11 +424,10 @@ HRESULT EvaluateModels(const std::vector<std::wstring>& modelPaths, const std::v
OutputHelper& output, Profiler<WINML_MODEL_TEST_PERF>& profiler)
{
output.PrintHardwareInfo();
HRESULT lastEvaluateModelResult = S_OK;
for (const auto& path : modelPaths)
{
LearningModel model = nullptr;
try
{
model =
@ -381,7 +438,6 @@ HRESULT EvaluateModels(const std::vector<std::wstring>& modelPaths, const std::v
std::cout << hr.message().c_str() << std::endl;
return hr.code();
}
auto firstFeature = model.InputFeatures().First().Current();
auto tensorDescriptor = firstFeature.try_as<TensorFeatureDescriptor>();
@ -392,65 +448,20 @@ HRESULT EvaluateModels(const std::vector<std::wstring>& modelPaths, const std::v
<< std::endl;
continue;
}
for (const auto& deviceType : deviceTypes)
{
for (const auto& inputBindingType : inputBindingTypes)
HRESULT evaluateModelWithDeviceTypeResult =
EvaluateModelWithDeviceType(model, deviceType, inputBindingTypes, inputDataTypes,
deviceCreationLocations, args, path, output, profiler, tensorDescriptor);
if (FAILED(evaluateModelWithDeviceTypeResult))
{
for (const auto& inputDataType : inputDataTypes)
{
for (const auto& deviceCreationLocation : deviceCreationLocations)
{
if (args.IsPerformanceCapture() || args.IsPerIterationCapture())
{
// Resets all values from profiler for bind and evaluate.
profiler.Reset(WINML_MODEL_TEST_PERF::BIND_VALUE, WINML_MODEL_TEST_PERF::COUNT);
}
if (inputDataType != InputDataType::Tensor)
{
// Currently GPU binding only work with 4D tensors and RGBA/BGRA images
if (tensorDescriptor.Shape().Size() != 4 || tensorDescriptor.Shape().GetAt(1) != 3)
{
continue;
}
}
HRESULT evalHResult = EvaluateModel(model, args, output, deviceType, inputBindingType,
inputDataType, deviceCreationLocation, profiler);
if (FAILED(evalHResult))
{
return evalHResult;
}
if (args.IsPerformanceCapture())
{
output.PrintResults(profiler, args.NumIterations(), deviceType, inputBindingType,
inputDataType, deviceCreationLocation,
args.IsPerformanceConsoleOutputVerbose());
if (args.IsOutputPerf())
{
std::string deviceTypeStringified = TypeHelper::Stringify(deviceType);
std::string inputDataTypeStringified = TypeHelper::Stringify(inputDataType);
std::string inputBindingTypeStringified = TypeHelper::Stringify(inputBindingType);
std::string deviceCreationLocationStringified =
TypeHelper::Stringify(deviceCreationLocation);
output.WritePerformanceDataToCSV(profiler, args.NumIterations(), path,
deviceTypeStringified, inputDataTypeStringified,
inputBindingTypeStringified,
deviceCreationLocationStringified);
}
}
}
}
lastEvaluateModelResult = evaluateModelWithDeviceTypeResult;
std::cout << "Run failed for DeviceType: " << TypeHelper::Stringify(deviceType) << std::endl;
}
}
model.Close();
}
return S_OK;
return lastEvaluateModelResult; // Return the last HRESULT that failed. Will return S_OK otherwise.
}
std::vector<InputDataType> FetchInputDataTypes(const CommandLineArgs& args)