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[GNA] clear input scale factor from configuration for imported model (#2172)

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* [GNA] clear input scale factor from configuration for imported model

* add test

* [GNA] add warning for using imported scale factors
This commit is contained in:
Anna Alberska 2020-09-29 17:32:09 +02:00 committed by GitHub
parent 2be6032c8d
commit 1cfa47cde8
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 77 additions and 38 deletions
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69
inference-engine/samples/speech_sample/main.cpp
View File

@ -602,41 +602,50 @@ int main(int argc, char *argv[]) {
}
if (FLAGS_q.compare("user") == 0) {
auto scaleFactorInput = ParseScaleFactors(FLAGS_sf);
if (numInputArkFiles != scaleFactorInput.size()) {
std::string errMessage("Incorrect command line for multiple inputs: "
+ std::to_string(scaleFactorInput.size()) + " scale factors provided for "
+ std::to_string(numInputArkFiles) + " input files.");
throw std::logic_error(errMessage);
}
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 (numInputArkFiles != scaleFactorInput.size()) {
std::string errMessage("Incorrect command line for multiple inputs: "
+ std::to_string(scaleFactorInput.size()) + " scale factors provided for "
+ std::to_string(numInputArkFiles) + " 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];
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
for (size_t i = 0; i < numInputArkFiles; i++) {
auto inputArkName = inputArkFiles[i].c_str();
std::string name;
std::vector<uint8_t> ptrFeatures;
uint32_t numArrays(0), numBytes(0), numFrames(0), numFrameElements(0), numBytesPerElement(0);
GetKaldiArkInfo(inputArkName, 0, &numArrays, &numBytes);
ptrFeatures.resize(numBytes);
LoadKaldiArkArray(inputArkName,
0,
name,
ptrFeatures,
&numFrames,
&numFrameElements,
&numBytesPerElement);
auto floatScaleFactor =
if (!FLAGS_rg.empty()) {
slog::info << "Using scale factor from provided imported gna model: " << FLAGS_rg << slog::endl;
} else {
for (size_t i = 0; i < numInputArkFiles; i++) {
auto inputArkName = inputArkFiles[i].c_str();
std::string name;
std::vector<uint8_t> ptrFeatures;
uint32_t numArrays(0), numBytes(0), numFrames(0), numFrameElements(0), numBytesPerElement(0);
GetKaldiArkInfo(inputArkName, 0, &numArrays, &numBytes);
ptrFeatures.resize(numBytes);
LoadKaldiArkArray(inputArkName,
0,
name,
ptrFeatures,
&numFrames,
&numFrameElements,
&numBytesPerElement);
auto floatScaleFactor =
ScaleFactorForQuantization(ptrFeatures.data(), MAX_VAL_2B_FEAT, numFrames * numFrameElements);
slog::info << "Using scale factor of " << floatScaleFactor << " calculated from first utterance."
<< slog::endl;
std::string scaleFactorConfigKey = GNA_CONFIG_KEY(SCALE_FACTOR) + std::string("_") + std::to_string(i);
gnaPluginConfig[scaleFactorConfigKey] = std::to_string(floatScaleFactor);
slog::info << "Using scale factor of " << floatScaleFactor << " calculated from first utterance."
<< slog::endl;
std::string scaleFactorConfigKey = GNA_CONFIG_KEY(SCALE_FACTOR) + std::string("_") + std::to_string(i);
gnaPluginConfig[scaleFactorConfigKey] = std::to_string(floatScaleFactor);
}
}
}

6
inference-engine/src/gna_plugin/gna_plugin.cpp
View File

@ -1180,6 +1180,12 @@ InferenceEngine::IExecutableNetwork::Ptr GNAPlugin::ImportNetwork(std::istream&
auto serial = GNAModelSerial(&std::get<0>(nnets.back())->obj, mt);
#endif
if (!inputsDesc->inputScaleFactors.empty()) {
gnalog() << "[Import Network] Clearing input scale factors defined in configuration. "
<< "Scale factors provided in imported model will be used\n";
inputsDesc->inputScaleFactors.clear();
}
serial.setHeader(header);
serial.Import(basePtr,
header.gnaMemSize,

40
inference-engine/tests/functional/plugin/gna/import_export_network.cpp
View File

@ -23,7 +23,8 @@
typedef std::tuple<
InferenceEngine::Precision, // Network Precision
std::string, // Target Device
std::map<std::string, std::string> //Configuration
std::map<std::string, std::string>, // Export Configuration
std::map<std::string, std::string> // Import Configuration
> exportImportNetworkParams;
namespace LayerTestsDefinitions {
@ -34,14 +35,18 @@ class ImportNetworkTest : public testing::WithParamInterface<exportImportNetwork
static std::string getTestCaseName(testing::TestParamInfo<exportImportNetworkParams> obj) {
InferenceEngine::Precision netPrecision;
std::string targetDevice;
std::map<std::string, std::string> configuration;
std::tie(netPrecision, targetDevice, configuration) = obj.param;
std::map<std::string, std::string> exportConfiguration;
std::map<std::string, std::string> importConfiguration;
std::tie(netPrecision, targetDevice, exportConfiguration, importConfiguration) = obj.param;
std::ostringstream result;
result << "netPRC=" << netPrecision.name() << "_";
result << "targetDevice=" << targetDevice << "_";
for (auto const& configItem : configuration) {
result << "_configItem=" << configItem.first << "_" << configItem.second;
for (auto const& configItem : exportConfiguration) {
result << "_exportConfigItem=" << configItem.first << "_" << configItem.second;
}
for (auto const& configItem : importConfiguration) {
result << "_importConfigItem=" << configItem.first << "_" << configItem.second;
}
return result.str();
}
@ -49,6 +54,7 @@ class ImportNetworkTest : public testing::WithParamInterface<exportImportNetwork
void Run() override {
SKIP_IF_CURRENT_TEST_IS_DISABLED()
configuration.insert(exportConfiguration.begin(), exportConfiguration.end());
LoadNetwork();
Infer();
executableNetwork.Export("exported_model.blob");
@ -57,6 +63,9 @@ class ImportNetworkTest : public testing::WithParamInterface<exportImportNetwork
auto referenceOutputs = CalculateRefs();
Compare(referenceOutputs, actualOutputs);
for (auto const& configItem : importConfiguration) {
configuration[configItem.first] = configItem.second;
}
std::fstream inputStream("exported_model.blob", std::ios_base::in | std::ios_base::binary);
if (inputStream.fail()) {
FAIL() << "Cannot open file to import model: exported_model.blob";
@ -68,7 +77,7 @@ class ImportNetworkTest : public testing::WithParamInterface<exportImportNetwork
protected:
void SetUp() override {
InferenceEngine::Precision netPrecision;
std::tie(netPrecision, targetDevice, configuration) = this->GetParam();
std::tie(netPrecision, targetDevice, exportConfiguration, importConfiguration) = this->GetParam();
auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
auto params = ngraph::builder::makeParams(ngPrc, { {1, 336} });
@ -95,6 +104,9 @@ class ImportNetworkTest : public testing::WithParamInterface<exportImportNetwork
}
private:
std::map<std::string, std::string> exportConfiguration;
std::map<std::string, std::string> importConfiguration;
std::vector<std::vector<std::uint8_t>> CalculateImportedNetwork(std::istream& networkModel) {
auto importedNetwork = core->ImportNetwork(networkModel, targetDevice, configuration);
@ -149,18 +161,30 @@ class ImportNetworkTest : public testing::WithParamInterface<exportImportNetwork
InferenceEngine::Precision::FP16
};
const std::vector<std::map<std::string, std::string>> configs = {
const std::vector<std::map<std::string, std::string>> exportConfigs = {
{
{"GNA_DEVICE_MODE", "GNA_SW_EXACT"},
{"GNA_SCALE_FACTOR_0", "327.67"}
}
};
const std::vector<std::map<std::string, std::string>> importConfigs = {
{
{"GNA_DEVICE_MODE", "GNA_SW_EXACT"},
{"GNA_SCALE_FACTOR_0", "32767"}
},
{
{"GNA_DEVICE_MODE", "GNA_SW_EXACT"},
{"GNA_SCALE_FACTOR_0", "327.67"}
},
};
INSTANTIATE_TEST_CASE_P(ImportNetworkCase, ImportNetworkTest,
::testing::Combine(
::testing::ValuesIn(netPrecisions),
::testing::Values(CommonTestUtils::DEVICE_GNA),
::testing::ValuesIn(configs)),
::testing::ValuesIn(exportConfigs),
::testing::ValuesIn(importConfigs)),
ImportNetworkTest::getTestCaseName);
} // namespace LayerTestsDefinitions