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e79636bfbbcde2a8806b1f064216d617b2c04930
openvino/samples/cpp/speech_sample/main.cpp
Szymon Irzabek e79636bfbb [GNA] Add 3.6 and 4.0 targets (#15735)
2023-03-07 17:14:59 +01:00

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// Copyright (C) 2018-2023 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <time.h>
#include <chrono>
#include <fstream>
#include <functional>
#include <iomanip>
#include <iostream>
#include <limits>
#include <map>
#include <memory>
#include <random>
#include <string>
#include <thread>
#include <utility>
#include <vector>
// clang-format off
#include <openvino/openvino.hpp>
#include <openvino/runtime/intel_gna/properties.hpp>
#include <samples/args_helper.hpp>
#include <samples/slog.hpp>
#include "fileutils.hpp"
#include "speech_sample.hpp"
#include "utils.hpp"
// clang-format on
using namespace ov::preprocess;
/**
* @brief The entry point for OpenVINO Runtime automatic speech recognition sample
* @file speech_sample/main.cpp
* @example speech_sample/main.cpp
*/
int main(int argc, char* argv[]) {
try {
// ------------------------------ Get OpenVINO Runtime version ----------------------------------------------
slog::info << "OpenVINO runtime: " << ov::get_openvino_version() << slog::endl;
// ------------------------------ Parsing and validation of input arguments ---------------------------------
if (!parse_and_check_command_line(argc, argv)) {
return 0;
}
BaseFile* file;
BaseFile* fileOutput;
ArkFile arkFile;
NumpyFile numpyFile;
std::pair<std::string, std::vector<std::string>> input_data;
if (!FLAGS_i.empty())
input_data = parse_parameters(FLAGS_i);
auto extInputFile = fileExt(input_data.first);
if (extInputFile == "ark") {
file = &arkFile;
} else if (extInputFile == "npz") {
file = &numpyFile;
} else {
throw std::logic_error("Invalid input file");
}
std::vector<std::string> inputFiles;
std::vector<uint32_t> numBytesThisUtterance;
uint32_t numUtterances(0);
if (!input_data.first.empty()) {
std::string outStr;
std::istringstream stream(input_data.first);
uint32_t currentNumUtterances(0), currentNumBytesThisUtterance(0);
while (getline(stream, outStr, ',')) {
std::string filename(fileNameNoExt(outStr) + "." + extInputFile);
inputFiles.push_back(filename);
file->get_file_info(filename.c_str(), 0, &currentNumUtterances, &currentNumBytesThisUtterance);
if (numUtterances == 0) {
numUtterances = currentNumUtterances;
} else if (currentNumUtterances != numUtterances) {
throw std::logic_error(
"Incorrect input files. Number of utterance must be the same for all input files");
}
numBytesThisUtterance.push_back(currentNumBytesThisUtterance);
}
}
size_t numInputFiles(inputFiles.size());
// --------------------------- Step 1. Initialize OpenVINO Runtime core and read model
// -------------------------------------
ov::Core core;
try {
const auto& gnaLibraryVersion = core.get_property("GNA", ov::intel_gna::library_full_version);
slog::info << "Detected GNA Library: " << gnaLibraryVersion << slog::endl;
} catch (std::exception& e) {
slog::info << "Cannot detect GNA Library version, exception: " << e.what() << slog::endl;
}
slog::info << "Loading model files:" << slog::endl << FLAGS_m << slog::endl;
uint32_t batchSize = (FLAGS_cw_r > 0 || FLAGS_cw_l > 0 || !FLAGS_bs) ? 1 : (uint32_t)FLAGS_bs;
std::shared_ptr<ov::Model> model;
// --------------------------- Processing custom outputs ---------------------------------------------
const auto output_data = parse_parameters(FLAGS_o);
const auto reference_data = parse_parameters(FLAGS_r);
const auto outputs = get_first_non_empty(output_data.second, reference_data.second);
// ------------------------------ Preprocessing ------------------------------------------------------
// the preprocessing steps can be done only for loaded network and are not applicable for the imported network
// (already compiled)
if (!FLAGS_m.empty()) {
const auto outputs_with_ports = parse_to_extract_port(outputs);
model = core.read_model(FLAGS_m);
for (const auto& output_with_port : outputs_with_ports) {
auto output = model->add_output(output_with_port.first, output_with_port.second);
output.set_names({output_with_port.first + ":" + std::to_string(output_with_port.second)});
}
check_number_of_inputs(model->inputs().size(), numInputFiles);
ov::preprocess::PrePostProcessor proc(model);
const auto& inputs = model->inputs();
std::map<std::string, std::string> custom_layouts;
if (!FLAGS_layout.empty()) {
custom_layouts = parse_input_layouts(FLAGS_layout, inputs);
}
for (const auto& input : inputs) {
const auto& item_name = input.get_any_name();
auto& in = proc.input(item_name);
in.tensor().set_element_type(ov::element::f32);
// Explicitly set inputs layout
if (custom_layouts.count(item_name) > 0) {
in.model().set_layout(ov::Layout(custom_layouts.at(item_name)));
}
}
for (size_t i = 0; i < model->outputs().size(); i++) {
proc.output(i).tensor().set_element_type(ov::element::f32);
}
model = proc.build();
if (FLAGS_bs) {
if (FLAGS_layout.empty() &&
std::any_of(inputs.begin(), inputs.end(), [](const ov::Output<ov::Node>& i) {
return ov::layout::get_layout(i).empty();
})) {
throw std::logic_error(
"-bs option is set to " + std::to_string(FLAGS_bs) +
" but model does not contain layout information for any input. Please "
"specify it explicitly using -layout option. For example, input1[NCHW], input2[NC] or [NC]");
} else {
ov::set_batch(model, batchSize);
}
}
}
// ------------------------------ Get Available Devices ------------------------------------------------------
auto isFeature = [&](const std::string xFeature) {
return FLAGS_d.find(xFeature) != std::string::npos;
};
bool useGna = isFeature("GNA");
bool useHetero = isFeature("HETERO");
std::string deviceStr = useHetero && useGna ? "HETERO:GNA,CPU" : FLAGS_d.substr(0, (FLAGS_d.find("_")));
// -----------------------------------------------------------------------------------------------------
// --------------------------- Set parameters and scale factors -------------------------------------
/** Setting parameter for per layer metrics **/
ov::AnyMap gnaPluginConfig;
ov::AnyMap genericPluginConfig;
if (useGna) {
std::string gnaDevice =
useHetero ? FLAGS_d.substr(FLAGS_d.find("GNA"), FLAGS_d.find(",") - FLAGS_d.find("GNA")) : FLAGS_d;
auto parse_gna_device = [&](const std::string& device) -> ov::intel_gna::ExecutionMode {
ov::intel_gna::ExecutionMode mode;
std::stringstream ss(device);
ss >> mode;
return mode;
};
gnaPluginConfig[ov::intel_gna::execution_mode.name()] = gnaDevice.find("_") == std::string::npos
? ov::intel_gna::ExecutionMode::AUTO
: parse_gna_device(gnaDevice);
}
if (FLAGS_pc) {
genericPluginConfig.emplace(ov::enable_profiling(true));
}
if (FLAGS_q.compare("user") == 0) {
if (!FLAGS_rg.empty()) {
std::string errMessage("Custom scale factor can not be set for imported gna model: " + FLAGS_rg);
throw std::logic_error(errMessage);
} else {
auto scale_factors_per_input = parse_scale_factors(model->inputs(), FLAGS_sf);
if (numInputFiles != scale_factors_per_input.size()) {
std::string errMessage("Incorrect command line for multiple inputs: " +
std::to_string(scale_factors_per_input.size()) +
" scale factors provided for " + std::to_string(numInputFiles) +
" input files.");
throw std::logic_error(errMessage);
}
for (auto&& sf : scale_factors_per_input) {
slog::info << "For input " << sf.first << " using scale factor of " << sf.second << slog::endl;
}
gnaPluginConfig[ov::intel_gna::scale_factors_per_input.name()] = scale_factors_per_input;
}
} else {
// "static" quantization with calculated scale factor
if (!FLAGS_rg.empty()) {
slog::info << "Using scale factor from provided imported gna model: " << FLAGS_rg << slog::endl;
} else {
std::map<std::string, float> scale_factors_per_input;
for (size_t i = 0; i < numInputFiles; i++) {
auto inputFileName = inputFiles[i].c_str();
std::string name;
std::vector<uint8_t> ptrFeatures;
uint32_t numArrays(0), numBytes(0), numFrames(0), numFrameElements(0), numBytesPerElement(0);
file->get_file_info(inputFileName, 0, &numArrays, &numBytes);
ptrFeatures.resize(numBytes);
file->load_file(inputFileName,
0,
name,
ptrFeatures,
&numFrames,
&numFrameElements,
&numBytesPerElement);
auto floatScaleFactor = scale_factor_for_quantization(ptrFeatures.data(),
MAX_VAL_2B_FEAT,
numFrames * numFrameElements);
slog::info << "Using scale factor of " << floatScaleFactor << " calculated from first utterance."
<< slog::endl;
scale_factors_per_input[model->input(i).get_any_name()] = floatScaleFactor;
}
gnaPluginConfig[ov::intel_gna::scale_factors_per_input.name()] = scale_factors_per_input;
}
}
gnaPluginConfig[ov::inference_precision.name()] = (FLAGS_qb == 8) ? ov::element::i8 : ov::element::i16;
const std::unordered_map<std::string, ov::intel_gna::HWGeneration> StringHWGenerationMap{
{"GNA_TARGET_1_0", ov::intel_gna::HWGeneration::GNA_1_0},
{"GNA_TARGET_2_0", ov::intel_gna::HWGeneration::GNA_2_0},
{"GNA_TARGET_3_0", ov::intel_gna::HWGeneration::GNA_3_0},
{"GNA_TARGET_3_1", ov::intel_gna::HWGeneration::GNA_3_1},
{"GNA_TARGET_3_5", ov::intel_gna::HWGeneration::GNA_3_5},
{"GNA_TARGET_3_5_E", ov::intel_gna::HWGeneration::GNA_3_5_E},
{"GNA_TARGET_3_6", ov::intel_gna::HWGeneration::GNA_3_6},
{"GNA_TARGET_4_0", ov::intel_gna::HWGeneration::GNA_4_0}};
auto parse_target = [&](const std::string& target) -> ov::intel_gna::HWGeneration {
auto hw_target = ov::intel_gna::HWGeneration::UNDEFINED;
const auto key_iter = StringHWGenerationMap.find(target);
if (key_iter != StringHWGenerationMap.end()) {
hw_target = key_iter->second;
} else if (!target.empty()) {
slog::warn << "Unsupported target: " << target << slog::endl;
}
return hw_target;
};
gnaPluginConfig[ov::intel_gna::execution_target.name()] = parse_target(FLAGS_exec_target);
gnaPluginConfig[ov::intel_gna::compile_target.name()] = parse_target(FLAGS_compile_target);
gnaPluginConfig[ov::intel_gna::memory_reuse.name()] = !FLAGS_memory_reuse_off;
gnaPluginConfig[ov::intel_gna::pwl_max_error_percent.name()] = FLAGS_pwl_me;
gnaPluginConfig[ov::log::level.name()] = FLAGS_log;
// -----------------------------------------------------------------------------------------------------
// --------------------------- Write model to file --------------------------------------------------
// Embedded GNA model dumping (for Intel(R) Speech Enabling Developer Kit)
if (!FLAGS_we.empty()) {
gnaPluginConfig[ov::intel_gna::firmware_model_image_path.name()] = FLAGS_we;
}
// -----------------------------------------------------------------------------------------------------
// --------------------------- Step 2. Loading model to the device ------------------------------------------
if (useGna) {
genericPluginConfig.insert(std::begin(gnaPluginConfig), std::end(gnaPluginConfig));
}
auto t0 = Time::now();
ms loadTime = std::chrono::duration_cast<ms>(Time::now() - t0);
slog::info << "Model loading time " << loadTime.count() << " ms" << slog::endl;
ov::CompiledModel executableNet;
if (!FLAGS_m.empty()) {
slog::info << "Loading model to the device " << FLAGS_d << slog::endl;
executableNet = core.compile_model(model, deviceStr, genericPluginConfig);
} else {
slog::info << "Importing model to the device" << slog::endl;
std::ifstream streamrq(FLAGS_rg, std::ios_base::binary | std::ios_base::in);
if (!streamrq.is_open()) {
throw std::runtime_error("Cannot open model file " + FLAGS_rg);
}
executableNet = core.import_model(streamrq, deviceStr, genericPluginConfig);
// loading batch from exported model
const auto& imported_inputs = executableNet.inputs();
if (std::any_of(imported_inputs.begin(), imported_inputs.end(), [](const ov::Output<const ov::Node>& i) {
return ov::layout::get_layout(i).empty();
})) {
slog::warn << "No batch dimension was found at any input, assuming batch to be 1." << slog::endl;
batchSize = 1;
} else {
for (auto& info : imported_inputs) {
auto imported_layout = ov::layout::get_layout(info);
if (ov::layout::has_batch(imported_layout)) {
batchSize = (uint32_t)info.get_shape()[ov::layout::batch_idx(imported_layout)];
break;
}
}
}
}
// --------------------------- Exporting gna model using InferenceEngine AOT API---------------------
if (!FLAGS_wg.empty()) {
slog::info << "Writing GNA Model to file " << FLAGS_wg << slog::endl;
t0 = Time::now();
std::ofstream streamwq(FLAGS_wg, std::ios_base::binary | std::ios::out);
executableNet.export_model(streamwq);
ms exportTime = std::chrono::duration_cast<ms>(Time::now() - t0);
slog::info << "Exporting time " << exportTime.count() << " ms" << slog::endl;
return 0;
}
if (!FLAGS_we.empty()) {
slog::info << "Exported GNA embedded model to file " << FLAGS_we << slog::endl;
if (!FLAGS_compile_target.empty()) {
slog::info << "GNA embedded model target: " << FLAGS_compile_target << slog::endl;
}
return 0;
}
// ---------------------------------------------------------------------------------------------------------
// --------------------------- Step 3. Create infer request
// --------------------------------------------------
std::vector<InferRequestStruct> inferRequests(1);
for (auto& inferRequest : inferRequests) {
inferRequest = {executableNet.create_infer_request(), -1, batchSize};
}
// --------------------------- Step 4. Configure input & output
// --------------------------------------------------
std::vector<ov::Tensor> ptrInputBlobs;
auto cInputInfo = executableNet.inputs();
check_number_of_inputs(cInputInfo.size(), numInputFiles);
if (!input_data.second.empty()) {
std::vector<std::string> inputNameBlobs = input_data.second;
if (inputNameBlobs.size() != cInputInfo.size()) {
std::string errMessage(std::string("Number of network inputs ( ") + std::to_string(cInputInfo.size()) +
" ) is not equal to the number of inputs entered in the -i argument ( " +
std::to_string(inputNameBlobs.size()) + " ).");
throw std::logic_error(errMessage);
}
for (const auto& input : inputNameBlobs) {
ov::Tensor blob = inferRequests.begin()->inferRequest.get_tensor(input);
if (!blob) {
std::string errMessage("No blob with name : " + input);
throw std::logic_error(errMessage);
}
ptrInputBlobs.push_back(blob);
}
} else {
for (const auto& input : cInputInfo) {
ptrInputBlobs.push_back(inferRequests.begin()->inferRequest.get_tensor(input));
}
}
std::vector<std::string> output_name_files;
std::vector<std::string> reference_name_files;
size_t count_file = 1;
if (!output_data.first.empty()) {
output_name_files = convert_str_to_vector(output_data.first);
if (output_name_files.size() != outputs.size() && outputs.size()) {
throw std::logic_error("The number of output files is not equal to the number of network outputs.");
}
count_file = output_name_files.size();
if (executableNet.outputs().size() > 1 && output_data.second.empty() && count_file == 1) {
throw std::logic_error("-o is ambiguous: the model has multiple outputs but only one file provided "
"without output name specification");
}
}
if (!reference_data.first.empty()) {
reference_name_files = convert_str_to_vector(reference_data.first);
if (reference_name_files.size() != outputs.size() && outputs.size()) {
throw std::logic_error("The number of reference files is not equal to the number of network outputs.");
}
count_file = reference_name_files.size();
if (executableNet.outputs().size() > 1 && reference_data.second.empty() && count_file == 1) {
throw std::logic_error("-r is ambiguous: the model has multiple outputs but only one file provided "
"without output name specification");
}
}
if (count_file > executableNet.outputs().size()) {
throw std::logic_error(
"The number of output/reference files is not equal to the number of network outputs.");
}
// -----------------------------------------------------------------------------------------------------
// --------------------------- Step 5. Do inference --------------------------------------------------------
std::vector<std::vector<uint8_t>> ptrUtterances;
const auto effective_outputs_size = outputs.size() ? outputs.size() : executableNet.outputs().size();
std::vector<std::vector<uint8_t>> vectorPtrScores(effective_outputs_size);
std::vector<uint16_t> numScoresPerOutput(effective_outputs_size);
std::vector<std::vector<uint8_t>> vectorPtrReferenceScores(reference_name_files.size());
std::vector<ScoreErrorT> vectorFrameError(reference_name_files.size()),
vectorTotalError(reference_name_files.size());
ptrUtterances.resize(inputFiles.size());
// initialize memory state before starting
for (auto&& state : inferRequests.begin()->inferRequest.query_state()) {
state.reset();
}
/** Work with each utterance **/
for (uint32_t utteranceIndex = 0; utteranceIndex < numUtterances; ++utteranceIndex) {
std::map<std::string, ov::ProfilingInfo> utterancePerfMap;
uint64_t totalNumberOfRunsOnHw = 0;
std::string uttName;
uint32_t numFrames(0), n(0);
std::vector<uint32_t> numFrameElementsInput;
std::vector<uint32_t> numFramesReference(reference_name_files.size()),
numFrameElementsReference(reference_name_files.size()),
numBytesPerElementReference(reference_name_files.size()),
numBytesReferenceScoreThisUtterance(reference_name_files.size());
/** Get information from input file for current utterance **/
numFrameElementsInput.resize(numInputFiles);
for (size_t i = 0; i < inputFiles.size(); i++) {
std::vector<uint8_t> ptrUtterance;
auto inputFilename = inputFiles[i].c_str();
uint32_t currentNumFrames(0), currentNumFrameElementsInput(0), currentNumBytesPerElementInput(0);
file->get_file_info(inputFilename, utteranceIndex, &n, &numBytesThisUtterance[i]);
ptrUtterance.resize(numBytesThisUtterance[i]);
file->load_file(inputFilename,
utteranceIndex,
uttName,
ptrUtterance,
&currentNumFrames,
&currentNumFrameElementsInput,
&currentNumBytesPerElementInput);
if (numFrames == 0) {
numFrames = currentNumFrames;
} else if (numFrames != currentNumFrames) {
std::string errMessage("Number of frames in input files is different: " +
std::to_string(numFrames) + " and " + std::to_string(currentNumFrames));
throw std::logic_error(errMessage);
}
ptrUtterances[i] = ptrUtterance;
numFrameElementsInput[i] = currentNumFrameElementsInput;
}
int i = 0;
for (auto& ptrInputBlob : ptrInputBlobs) {
if (ptrInputBlob.get_size() != numFrameElementsInput[i++] * batchSize) {
throw std::logic_error("network input size(" + std::to_string(ptrInputBlob.get_size()) +
") mismatch to input file size (" +
std::to_string(numFrameElementsInput[i - 1] * batchSize) + ")");
}
}
double totalTime = 0.0;
for (size_t errorIndex = 0; errorIndex < vectorFrameError.size(); errorIndex++) {
clear_score_error(&vectorTotalError[errorIndex]);
vectorTotalError[errorIndex].threshold = vectorFrameError[errorIndex].threshold = MAX_SCORE_DIFFERENCE;
}
std::vector<uint8_t*> inputFrame;
for (auto& ut : ptrUtterances) {
inputFrame.push_back(&ut.front());
}
std::map<std::string, ov::ProfilingInfo> callPerfMap;
size_t frameIndex = 0;
uint32_t numFramesFile = numFrames;
numFrames += FLAGS_cw_l + FLAGS_cw_r;
uint32_t numFramesThisBatch{batchSize};
auto t0 = Time::now();
auto t1 = t0;
BaseFile* fileReferenceScores;
std::string refUtteranceName;
if (!reference_data.first.empty()) {
/** Read file with reference scores **/
auto exReferenceScoresFile = fileExt(reference_data.first);
if (exReferenceScoresFile == "ark") {
fileReferenceScores = &arkFile;
} else if (exReferenceScoresFile == "npz") {
fileReferenceScores = &numpyFile;
} else {
throw std::logic_error("Invalid Reference Scores file");
}
for (size_t next_output = 0; next_output < count_file; next_output++) {
if (fileReferenceScores != nullptr) {
fileReferenceScores->get_file_info(reference_name_files[next_output].c_str(),
utteranceIndex,
&n,
&numBytesReferenceScoreThisUtterance[next_output]);
vectorPtrReferenceScores[next_output].resize(numBytesReferenceScoreThisUtterance[next_output]);
fileReferenceScores->load_file(reference_name_files[next_output].c_str(),
utteranceIndex,
refUtteranceName,
vectorPtrReferenceScores[next_output],
&numFramesReference[next_output],
&numFrameElementsReference[next_output],
&numBytesPerElementReference[next_output]);
}
}
}
while (frameIndex <= numFrames) {
if (frameIndex == numFrames) {
if (std::find_if(inferRequests.begin(), inferRequests.end(), [&](InferRequestStruct x) {
return (x.frameIndex != -1);
}) == inferRequests.end()) {
break;
}
}
bool inferRequestFetched = false;
/** Start inference loop **/
for (auto& inferRequest : inferRequests) {
if (frameIndex == numFrames) {
numFramesThisBatch = 1;
} else {
numFramesThisBatch =
(numFrames - frameIndex < batchSize) ? (numFrames - frameIndex) : batchSize;
}
/* waits until inference result becomes available */
if (inferRequest.frameIndex != -1) {
inferRequest.inferRequest.wait();
if (inferRequest.frameIndex >= 0)
for (size_t next_output = 0; next_output < count_file; next_output++) {
const auto output_name = outputs.size() > next_output
? outputs[next_output]
: executableNet.output(next_output).get_any_name();
auto dims = executableNet.output(output_name).get_shape();
numScoresPerOutput[next_output] = std::accumulate(std::begin(dims),
std::end(dims),
size_t{1},
std::multiplies<size_t>());
vectorPtrScores[next_output].resize(numFramesFile * numScoresPerOutput[next_output] *
sizeof(float));
if (!FLAGS_o.empty()) {
/* Prepare output data for save to file in future */
auto outputFrame = &vectorPtrScores[next_output].front() +
numScoresPerOutput[next_output] * sizeof(float) *
(inferRequest.frameIndex) / batchSize;
ov::Tensor outputBlob =
inferRequest.inferRequest.get_tensor(executableNet.output(output_name));
// locked memory holder should be alive all time while access to its buffer happens
auto byteSize = numScoresPerOutput[next_output] * sizeof(float);
std::memcpy(outputFrame, outputBlob.data<float>(), byteSize);
}
if (!FLAGS_r.empty()) {
/** Compare output data with reference scores **/
ov::Tensor outputBlob =
inferRequest.inferRequest.get_tensor(executableNet.output(output_name));
if (numScoresPerOutput[next_output] / numFrameElementsReference[next_output] ==
batchSize) {
compare_scores(
outputBlob.data<float>(),
&vectorPtrReferenceScores[next_output]
[inferRequest.frameIndex *
numFrameElementsReference[next_output] *
numBytesPerElementReference[next_output]],
&vectorFrameError[next_output],
inferRequest.numFramesThisBatch,
numFrameElementsReference[next_output]);
update_score_error(&vectorFrameError[next_output],
&vectorTotalError[next_output]);
} else {
throw std::logic_error("Number of output and reference frames does not match.");
}
}
if (FLAGS_pc) {
// retrieve new counters
get_performance_counters(inferRequest.inferRequest, callPerfMap);
// summarize retrieved counters with all previous
sum_performance_counters(callPerfMap, utterancePerfMap, totalNumberOfRunsOnHw);
}
}
// -----------------------------------------------------------------------------------------------------
}
if (frameIndex == numFrames) {
inferRequest.frameIndex = -1;
continue;
}
ptrInputBlobs.clear();
if (input_data.second.empty()) {
for (auto& input : cInputInfo) {
ptrInputBlobs.push_back(inferRequest.inferRequest.get_tensor(input));
}
} else {
std::vector<std::string> inputNameBlobs = input_data.second;
for (const auto& input : inputNameBlobs) {
ov::Tensor blob = inferRequests.begin()->inferRequest.get_tensor(input);
if (!blob) {
std::string errMessage("No blob with name : " + input);
throw std::logic_error(errMessage);
}
ptrInputBlobs.push_back(blob);
}
}
/** Iterate over all the input blobs **/
for (size_t i = 0; i < numInputFiles; ++i) {
ov::Tensor minput = ptrInputBlobs[i];
if (!minput) {
std::string errMessage("We expect ptrInputBlobs[" + std::to_string(i) +
"] to be inherited from Tensor, " +
"but in fact we were not able to cast input to Tensor");
throw std::logic_error(errMessage);
}
memcpy(minput.data(),
inputFrame[i],
numFramesThisBatch * numFrameElementsInput[i] * sizeof(float));
// Used to infer fewer frames than the batch size
if (batchSize != numFramesThisBatch) {
memset(minput.data<float>() + numFramesThisBatch * numFrameElementsInput[i],
0,
(batchSize - numFramesThisBatch) * numFrameElementsInput[i]);
}
}
// -----------------------------------------------------------------------------------------------------
int index = static_cast<int>(frameIndex) - (FLAGS_cw_l + FLAGS_cw_r);
/* Starting inference in asynchronous mode*/
inferRequest.inferRequest.start_async();
inferRequest.frameIndex = index < 0 ? -2 : index;
inferRequest.numFramesThisBatch = numFramesThisBatch;
frameIndex += numFramesThisBatch;
for (size_t j = 0; j < inputFiles.size(); j++) {
if (FLAGS_cw_l > 0 || FLAGS_cw_r > 0) {
int idx = frameIndex - FLAGS_cw_l;
if (idx > 0 && idx < static_cast<int>(numFramesFile)) {
inputFrame[j] += sizeof(float) * numFrameElementsInput[j] * numFramesThisBatch;
} else if (idx >= static_cast<int>(numFramesFile)) {
inputFrame[j] = &ptrUtterances[j].front() + (numFramesFile - 1) * sizeof(float) *
numFrameElementsInput[j] *
numFramesThisBatch;
} else if (idx <= 0) {
inputFrame[j] = &ptrUtterances[j].front();
}
} else {
inputFrame[j] += sizeof(float) * numFrameElementsInput[j] * numFramesThisBatch;
}
}
inferRequestFetched |= true;
}
/** Inference was finished for current frame **/
if (!inferRequestFetched) {
std::this_thread::sleep_for(std::chrono::milliseconds(1));
continue;
}
}
t1 = Time::now();
fsec fs = t1 - t0;
ms d = std::chrono::duration_cast<ms>(fs);
totalTime += d.count();
// resetting state between utterances
for (auto&& state : inferRequests.begin()->inferRequest.query_state()) {
state.reset();
}
// -----------------------------------------------------------------------------------------------------
// --------------------------- Step 6. Process output
// -------------------------------------------------------
/** Show performance results **/
std::cout << "Utterance " << utteranceIndex << ": " << std::endl;
std::cout << "Total time in Infer (HW and SW):\t" << totalTime << " ms" << std::endl;
std::cout << "Frames in utterance:\t\t\t" << numFrames << " frames" << std::endl;
std::cout << "Average Infer time per frame:\t\t" << totalTime / static_cast<double>(numFrames) << " ms\n"
<< std::endl;
if (FLAGS_pc) {
// print performance results
print_performance_counters(utterancePerfMap,
frameIndex,
std::cout,
getFullDeviceName(core, FLAGS_d),
totalNumberOfRunsOnHw,
FLAGS_d);
}
for (size_t next_output = 0; next_output < count_file; next_output++) {
if (!FLAGS_o.empty()) {
auto exOutputScoresFile = fileExt(output_data.first);
if (exOutputScoresFile == "ark") {
fileOutput = &arkFile;
} else if (exOutputScoresFile == "npz") {
fileOutput = &numpyFile;
} else {
throw std::logic_error("Invalid Reference Scores file");
}
/* Save output data to file */
bool shouldAppend = (utteranceIndex == 0) ? false : true;
fileOutput->save_file(output_name_files[next_output].c_str(),
shouldAppend,
uttName,
&vectorPtrScores[next_output].front(),
numFramesFile,
numScoresPerOutput[next_output] / batchSize);
}
if (!FLAGS_r.empty()) {
// print statistical score error
const auto output_name = outputs.size() > next_output
? outputs[next_output]
: executableNet.output(next_output).get_any_name();
std::cout << "Output name: " << output_name << std::endl;
std::cout << "Number scores per frame: " << numScoresPerOutput[next_output] / batchSize << std::endl
<< std::endl;
print_reference_compare_results(vectorTotalError[next_output], numFrames, std::cout);
}
}
}
} catch (const std::exception& error) {
slog::err << error.what() << slog::endl;
return 1;
} catch (...) {
slog::err << "Unknown/internal exception happened" << slog::endl;
return 1;
}
slog::info << "Execution successful" << slog::endl;
return 0;
}
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