Added more comments into speech sample (#5049)
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Anton Romanov
GitHub
parent
37893caa36
commit
d30740af66
@ -263,11 +263,6 @@ float StdDevError(score_error_t error) {
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- (error.sumError / error.numScores) * (error.sumError / error.numScores)));
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}
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float StdDevRelError(score_error_t error) {
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return (sqrt(error.sumSquaredRelError / error.numScores
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- (error.sumRelError / error.numScores) * (error.sumRelError / error.numScores)));
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}
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#if !defined(__arm__) && !defined(_M_ARM) && !defined(__aarch64__) && !defined(_M_ARM64)
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#ifdef _WIN32
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#include <intrin.h>
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@ -579,23 +574,24 @@ int main(int argc, char *argv[]) {
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// --------------------------- 1. Load inference engine -------------------------------------
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slog::info << "Loading Inference Engine" << slog::endl;
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Core ie;
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CNNNetwork network;
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ExecutableNetwork executableNet;
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/** Printing device version **/
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slog::info << "Device info: " << slog::endl;
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std::cout << ie.GetVersions(deviceStr) << std::endl;
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// -----------------------------------------------------------------------------------------------------
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// 2. Read a model in OpenVINO Intermediate Representation (.xml and .bin files) or ONNX (.onnx file) format
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// --------------------------- 2. Read a model in OpenVINO Intermediate Representation (.xml and .bin files) or ONNX (.onnx file) format
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slog::info << "Loading network files" << slog::endl;
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CNNNetwork network;
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if (!FLAGS_m.empty()) {
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/** Read network model **/
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network = ie.ReadNetwork(FLAGS_m);
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CheckNumberOfInputs(network.getInputsInfo().size(), numInputArkFiles);
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// -------------------------------------------------------------------------------------------------
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// --------------------------- 3. Set batch size ---------------------------------------------------
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// --------------------------- Set batch size ---------------------------------------------------
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/** Set batch size. Unlike in imaging, batching in time (rather than space) is done for speech recognition. **/
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network.setBatchSize(batchSize);
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slog::info << "Batch size is " << std::to_string(network.getBatchSize())
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@ -604,7 +600,7 @@ int main(int argc, char *argv[]) {
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 4. Set parameters and scale factors -------------------------------------
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// --------------------------- Set parameters and scale factors -------------------------------------
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/** Setting parameter for per layer metrics **/
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std::map<std::string, std::string> gnaPluginConfig;
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std::map<std::string, std::string> genericPluginConfig;
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@ -678,7 +674,7 @@ int main(int argc, char *argv[]) {
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gnaPluginConfig[GNA_CONFIG_KEY(PWL_MAX_ERROR_PERCENT)] = std::to_string(FLAGS_pwl_me);
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 5. Write model to file --------------------------------------------------
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// --------------------------- Write model to file --------------------------------------------------
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// Embedded GNA model dumping (for Intel(R) Speech Enabling Developer Kit)
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if (!FLAGS_we.empty()) {
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gnaPluginConfig[GNAConfigParams::KEY_GNA_FIRMWARE_MODEL_IMAGE] = FLAGS_we;
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@ -686,14 +682,13 @@ int main(int argc, char *argv[]) {
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}
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 6. Loading model to the device ------------------------------------------
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// --------------------------- 3. Loading model to the device ------------------------------------------
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if (useGna) {
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genericPluginConfig.insert(std::begin(gnaPluginConfig), std::end(gnaPluginConfig));
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}
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auto t0 = Time::now();
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std::vector<std::string> outputs;
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ExecutableNetwork executableNet;
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if (!FLAGS_oname.empty()) {
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std::vector<std::string> output_names = ParseBlobName(FLAGS_oname);
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@ -726,7 +721,7 @@ int main(int argc, char *argv[]) {
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ms loadTime = std::chrono::duration_cast<ms>(Time::now() - t0);
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slog::info << "Model loading time " << loadTime.count() << " ms" << slog::endl;
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// --------------------------- 7. Exporting gna model using InferenceEngine AOT API---------------------
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// --------------------------- Exporting gna model using InferenceEngine AOT API---------------------
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if (!FLAGS_wg.empty()) {
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slog::info << "Writing GNA Model to file " << FLAGS_wg << slog::endl;
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t0 = Time::now();
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@ -744,13 +739,17 @@ int main(int argc, char *argv[]) {
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return 0;
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}
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// --------------------------- 4. Create infer request --------------------------------------------------
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std::vector<InferRequestStruct> inferRequests((FLAGS_cw_r > 0 || FLAGS_cw_l > 0) ? 1 : FLAGS_nthreads);
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for (auto& inferRequest : inferRequests) {
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inferRequest = {executableNet.CreateInferRequest(), -1, batchSize};
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}
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// -----------------------------------------------------------------------------------------------------
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// ---------------------------------------------------------------------------------------------------------
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// --------------------------- 8. Prepare input blobs --------------------------------------------------
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// --------------------------- 5. Configure input & output --------------------------------------------------
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//--- Prepare input blobs ----------------------------------------------
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/** Taking information about all topology inputs **/
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ConstInputsDataMap cInputInfo = executableNet.GetInputsInfo();
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CheckNumberOfInputs(cInputInfo.size(), numInputArkFiles);
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@ -788,9 +787,9 @@ int main(int argc, char *argv[]) {
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item.second->setPrecision(inputPrecision);
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}
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// -----------------------------------------------------------------------------------------------------
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// ---------------------------------------------------------------------
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// --------------------------- 9. Prepare output blobs -------------------------------------------------
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//--- Prepare output blobs ---------------------------------------------
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ConstOutputsDataMap cOutputInfo(executableNet.GetOutputsInfo());
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OutputsDataMap outputInfo;
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if (!FLAGS_m.empty()) {
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@ -821,9 +820,10 @@ int main(int argc, char *argv[]) {
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Precision outputPrecision = Precision::FP32; // specify Precision::I32 to retrieve quantized outputs
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outData->setPrecision(outputPrecision);
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}
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// ---------------------------------------------------------------------
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- 10. Do inference --------------------------------------------------------
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// --------------------------- 6. Do inference --------------------------------------------------------
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std::vector<std::string> output_name_files;
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std::vector<std::string> reference_name_files;
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size_t count_file = 1;
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@ -854,6 +854,7 @@ int main(int argc, char *argv[]) {
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state.Reset();
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}
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/** Work with each utterance **/
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for (uint32_t utteranceIndex = 0; utteranceIndex < numUtterances; ++utteranceIndex) {
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std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> utterancePerfMap;
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std::string uttName;
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@ -867,6 +868,7 @@ int main(int argc, char *argv[]) {
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slog::info << "Number scores per frame : " << numScoresPerFrame << slog::endl;
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/** Get information from ark file for current utterance **/
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numFrameElementsInput.resize(numInputArkFiles);
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for (size_t i = 0; i < inputArkFiles.size(); i++) {
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std::vector<uint8_t> ptrUtterance;
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@ -905,6 +907,7 @@ int main(int argc, char *argv[]) {
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ptrScores.resize(numFrames * numScoresPerFrame * sizeof(float));
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if (!FLAGS_r.empty()) {
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/** Read ark file with reference scores **/
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std::string refUtteranceName;
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GetKaldiArkInfo(reference_name_files[next_output].c_str(), utteranceIndex, &n, &numBytesReferenceScoreThisUtterance);
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ptrReferenceScores.resize(numBytesReferenceScoreThisUtterance);
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@ -950,6 +953,7 @@ int main(int argc, char *argv[]) {
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}
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bool inferRequestFetched = false;
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/** Start inference loop **/
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for (auto &inferRequest : inferRequests) {
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if (frameIndex == numFrames) {
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numFramesThisBatch = 1;
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@ -969,6 +973,7 @@ int main(int argc, char *argv[]) {
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ConstOutputsDataMap newOutputInfo;
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if (inferRequest.frameIndex >= 0) {
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if (!FLAGS_o.empty()) {
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/* Prepare output data for save to file in future */
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outputFrame =
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&ptrScores.front() +
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numScoresPerFrame * sizeof(float) * (inferRequest.frameIndex);
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@ -993,6 +998,7 @@ int main(int argc, char *argv[]) {
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byteSize);
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}
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if (!FLAGS_r.empty()) {
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/** Compare output data with reference scores **/
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if (!outputs.empty()) {
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newOutputInfo[outputs[next_output]] = cOutputInfo[outputs[next_output]];
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} else {
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@ -1029,6 +1035,7 @@ int main(int argc, char *argv[]) {
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continue;
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}
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/** Prepare input blobs**/
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ptrInputBlobs.clear();
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if (FLAGS_iname.empty()) {
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for (auto &input : cInputInfo) {
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@ -1063,6 +1070,7 @@ int main(int argc, char *argv[]) {
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}
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int index = static_cast<int>(frameIndex) - (FLAGS_cw_l + FLAGS_cw_r);
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/** Start inference **/
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inferRequest.inferRequest.StartAsync();
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inferRequest.frameIndex = index < 0 ? -2 : index;
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inferRequest.numFramesThisBatch = numFramesThisBatch;
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@ -1086,6 +1094,7 @@ int main(int argc, char *argv[]) {
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}
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inferRequestFetched |= true;
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}
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/** Inference was finished for current frame **/
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if (!inferRequestFetched) {
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std::this_thread::sleep_for(std::chrono::milliseconds(1));
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continue;
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@ -1103,6 +1112,7 @@ int main(int argc, char *argv[]) {
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}
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if (!FLAGS_o.empty()) {
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/* Save output data to file */
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bool shouldAppend = (utteranceIndex == 0) ? false : true;
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SaveKaldiArkArray(output_name_files[next_output].c_str(), shouldAppend, uttName, &ptrScores.front(),
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numFramesArkFile, numScoresPerFrame);
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