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[GNA] Set input scale factors for imported model (#7139)

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Mikhail Ryzhov 2021-08-19 12:27:02 +03:00 committed by GitHub
parent b73788d08c
commit 0f1fc504b4
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8 changed files with 79 additions and 28 deletions
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31
inference-engine/samples/speech_sample/main.cpp
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@ -627,24 +627,21 @@ int main(int argc, char* argv[]) {
if (FLAGS_q.compare("user") == 0) {
if (!FLAGS_rg.empty()) {
slog::warn
<< "Custom scale factor will be ignored - using scale factor from provided imported gna model: "
<< FLAGS_rg << slog::endl;
} else {
auto scaleFactorInput = ParseScaleFactors(FLAGS_sf);
if (numInputFiles != scaleFactorInput.size()) {
std::string errMessage(
"Incorrect command line for multiple inputs: " + std::to_string(scaleFactorInput.size()) +
" scale factors provided for " + std::to_string(numInputFiles) + " input files.");
throw std::logic_error(errMessage);
}
slog::warn << "Custom scale factor will be used for imported gna model: " << FLAGS_rg << slog::endl;
}
for (size_t i = 0; i < scaleFactorInput.size(); ++i) {
slog::info << "For input " << i << " using scale factor of " << scaleFactorInput[i] << slog::endl;
std::string scaleFactorConfigKey =
GNA_CONFIG_KEY(SCALE_FACTOR) + std::string("_") + std::to_string(i);
gnaPluginConfig[scaleFactorConfigKey] = scaleFactorInput[i];
}
auto scaleFactorInput = ParseScaleFactors(FLAGS_sf);
if (numInputFiles != scaleFactorInput.size()) {
std::string errMessage(
"Incorrect command line for multiple inputs: " + std::to_string(scaleFactorInput.size()) +
" scale factors provided for " + std::to_string(numInputFiles) + " input files.");
throw std::logic_error(errMessage);
}
for (size_t i = 0; i < scaleFactorInput.size(); ++i) {
slog::info << "For input " << i << " using scale factor of " << scaleFactorInput[i] << slog::endl;
std::string scaleFactorConfigKey = GNA_CONFIG_KEY(SCALE_FACTOR) + std::string("_") + std::to_string(i);
gnaPluginConfig[scaleFactorConfigKey] = scaleFactorInput[i];
}
} else {
// "static" quantization with calculated scale factor

12
inference-engine/src/gna_plugin/gna_plugin.cpp
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@ -1579,6 +1579,18 @@ InferenceEngine::IExecutableNetworkInternal::Ptr GNAPlugin::ImportNetwork(std::i
transpose_inputs_info,
transpose_outputs_info);
// If scale factors are defined in configuration we still need to use them instead of imported values,
// for example to change the scale factors for the old models.
if (!config.inputScaleFactors.empty()) {
IE_ASSERT(config.inputScaleFactors.size() == inputsDesc->inputScaleFactors.size());
for (size_t i = 0; i < config.inputScaleFactors.size(); ++i) {
if (config.inputScaleFactors[i] != GNAPluginNS::kScaleFactorDefault) {
gnalog() << "[Import Network] Using input scale factor defined in configuration for input " << i << std::endl;
inputsDesc->inputScaleFactors[i] = config.inputScaleFactors[i];
}
}
}
#if GNA_LIB_VER == 2
auto getOrientation = [](Gna2Operation & gnaOperation) {
return gnaOperation.Type == Gna2OperationTypeConvolution ?

2
inference-engine/src/gna_plugin/gna_plugin_config.cpp
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@ -95,7 +95,7 @@ void Config::UpdateFromMap(const std::map<std::string, std::string>& config) {
}
// missing scale factors are set to be 1.0f
if (inputScaleFactors.size() <= input_index) {
inputScaleFactors.resize(input_index + 1, 1.f);
inputScaleFactors.resize(input_index + 1, GNAPluginNS::kScaleFactorDefault);
}
inputScaleFactors[input_index] = InferenceEngine::CNNLayer::ie_parse_float(value);
} else if (key == GNA_CONFIG_KEY(FIRMWARE_MODEL_IMAGE)) {

2
inference-engine/src/gna_plugin/gna_plugin_config.hpp
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@ -18,6 +18,8 @@
namespace GNAPluginNS {
static const float kScaleFactorDefault = 1.f;
struct Config {
Config() {
AdjustKeyMapValues();

41
inference-engine/tests/functional/plugin/gna/shared_tests_instances/import_export_tests/import_reshape_permute_conv.cpp
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@ -11,7 +11,7 @@ using namespace LayerTestsDefinitions;
namespace {
class ImportReshapePermuteConvGNA : public ImportReshapePermuteConv {
class ImportExportGNAModelUnchanged : public ImportReshapePermuteConv {
private:
void exportImportNetwork() override {
{
@ -42,8 +42,14 @@ private:
std::string fileName = "exported_model.blob";
};
TEST_P(ImportReshapePermuteConvGNA, CompareWithRefImpl) {
Run();
class ImportExportGNAModelChanged : public ImportExportGNAModelUnchanged {};
TEST_P(ImportExportGNAModelUnchanged, ReshapePermuteConv) {
TestRun(false);
};
TEST_P(ImportExportGNAModelChanged, ReshapePermuteConv) {
TestRun(true);
};
const std::vector<InferenceEngine::Precision> netPrecisions = {
@ -58,15 +64,25 @@ const std::vector<std::map<std::string, std::string>> exportConfigs = {
}
};
const std::vector<std::map<std::string, std::string>> importConfigs = {
const std::vector<std::map<std::string, std::string>> importConfigsChanged = {
{
{"GNA_DEVICE_MODE", "GNA_SW_EXACT"},
{"GNA_SCALE_FACTOR_0", "32767"}
},
}
};
const std::vector<std::map<std::string, std::string>> importConfigsUnchanged = {
{
{"GNA_DEVICE_MODE", "GNA_SW_EXACT"},
{"GNA_SCALE_FACTOR_0", "327.67"}
},
{
{"GNA_DEVICE_MODE", "GNA_SW_EXACT"},
{"GNA_SCALE_FACTOR_0", "1"}
},
{
{"GNA_DEVICE_MODE", "GNA_SW_EXACT"}
}
};
const std::vector<std::string> appHeaders = {
@ -74,13 +90,22 @@ const std::vector<std::string> appHeaders = {
"APPLICATION_HEADER"
};
INSTANTIATE_TEST_SUITE_P(smoke_ImportNetworkCase, ImportReshapePermuteConvGNA,
INSTANTIATE_TEST_CASE_P(smoke_ImportNetworkGNA, ImportExportGNAModelUnchanged,
::testing::Combine(
::testing::ValuesIn(netPrecisions),
::testing::Values(CommonTestUtils::DEVICE_GNA),
::testing::ValuesIn(exportConfigs),
::testing::ValuesIn(importConfigs),
::testing::ValuesIn(importConfigsUnchanged),
::testing::ValuesIn(appHeaders)),
ImportReshapePermuteConvGNA::getTestCaseName);
ImportExportGNAModelUnchanged::getTestCaseName);
INSTANTIATE_TEST_CASE_P(smoke_ImportNetworkGNA, ImportExportGNAModelChanged,
::testing::Combine(
::testing::ValuesIn(netPrecisions),
::testing::Values(CommonTestUtils::DEVICE_GNA),
::testing::ValuesIn(exportConfigs),
::testing::ValuesIn(importConfigsChanged),
::testing::ValuesIn(appHeaders)),
ImportExportGNAModelChanged::getTestCaseName);
} // namespace

1
inference-engine/tests/functional/plugin/shared/include/base/import_export_base/import_export_base.hpp
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@ -23,6 +23,7 @@ class ImportNetworkTestBase : public testing::WithParamInterface<exportImportNet
public:
static std::string getTestCaseName(testing::TestParamInfo<exportImportNetworkParams> obj);
void Run() override;
void TestRun(bool isModelChanged);
protected:
std::map<std::string, std::string> exportConfiguration;

17
inference-engine/tests/functional/plugin/shared/src/base/import_export_base/import_export_base.cpp
View File

@ -42,14 +42,18 @@ void ImportNetworkTestBase::exportImportNetwork() {
}
void ImportNetworkTestBase::Run() {
SKIP_IF_CURRENT_TEST_IS_DISABLED()
TestRun(false);
}
void ImportNetworkTestBase::TestRun(bool isModelChanged) {
SKIP_IF_CURRENT_TEST_IS_DISABLED()
// load export configuration and save outputs
configuration.insert(exportConfiguration.begin(), exportConfiguration.end());
LoadNetwork();
GenerateInputs();
Infer();
auto actualOutputs = GetOutputs();
const auto& actualOutputs = GetOutputs();
auto referenceOutputs = CalculateRefs();
Compare(referenceOutputs, actualOutputs);
@ -57,6 +61,14 @@ void ImportNetworkTestBase::Run() {
configuration[configItem.first] = configItem.second;
}
// for import with different scale factor need to use import configuration to get refference outputs.
if (isModelChanged) {
LoadNetwork();
GenerateInputs();
Infer();
actualOutputs = GetOutputs();
}
const auto compiledExecNetwork = executableNetwork;
exportImportNetwork();
const auto importedExecNetwork = executableNetwork;
@ -75,6 +87,7 @@ void ImportNetworkTestBase::Run() {
ASSERT_NO_THROW(compiledExecNetwork.GetOutputsInfo()[next_output.first]);
}
auto importedOutputs = GetOutputs();
ASSERT_EQ(actualOutputs.size(), importedOutputs.size());
for (size_t i = 0; i < actualOutputs.size(); i++) {

1
inference-engine/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
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@ -313,6 +313,7 @@ void LayerTestsCommon::LoadNetwork() {
}
void LayerTestsCommon::GenerateInputs() {
inputs.clear();
const auto& inputsInfo = executableNetwork.GetInputsInfo();
const auto& functionParams = function->get_parameters();
for (int i = 0; i < functionParams.size(); ++i) {