[SAMPLES] [GNA] Remove port assertion in speech_sample when importing (#13687)
* [SAMPLES] [GNA] Remove port assertion in speech_sample when importing the GNA fromat model * Fix code style
This commit is contained in:
Krzysztof Bruniecki
GitHub
parent
764525ba6f
commit
f8b12b4f1a
@ -94,42 +94,21 @@ int main(int argc, char* argv[]) {
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slog::info << "Loading model files:" << slog::endl << FLAGS_m << slog::endl;
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uint32_t batchSize = (FLAGS_cw_r > 0 || FLAGS_cw_l > 0 || !FLAGS_bs) ? 1 : (uint32_t)FLAGS_bs;
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std::shared_ptr<ov::Model> model;
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std::vector<std::string> outputs;
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std::vector<std::string> output_names;
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std::vector<size_t> ports;
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// --------------------------- Processing custom outputs ---------------------------------------------
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std::pair<std::string, std::vector<std::string>> output_data;
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std::pair<std::string, std::vector<std::string>> reference_data;
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if (!FLAGS_o.empty())
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output_data = parse_parameters(FLAGS_o);
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if (!FLAGS_r.empty())
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reference_data = parse_parameters(FLAGS_r);
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if (!output_data.second.empty())
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output_names = output_data.second;
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else if (!reference_data.second.empty())
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output_names = reference_data.second;
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for (const auto& output_name : output_names) {
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auto pos_layer = output_name.rfind(":");
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if (pos_layer == std::string::npos) {
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throw std::logic_error("Output " + output_name + " doesn't have a port");
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}
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outputs.push_back(output_name.substr(0, pos_layer));
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try {
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ports.push_back(std::stoi(output_name.substr(pos_layer + 1)));
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} catch (const std::exception&) {
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throw std::logic_error("Ports should have integer type");
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}
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}
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const auto output_data = parse_parameters(FLAGS_o);
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const auto reference_data = parse_parameters(FLAGS_r);
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const auto outputs = get_first_non_empty(output_data.second, reference_data.second);
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// ------------------------------ Preprocessing ------------------------------------------------------
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// the preprocessing steps can be done only for loaded network and are not applicable for the imported network
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// (already compiled)
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if (!FLAGS_m.empty()) {
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const auto outputs_with_ports = parse_to_extract_port(outputs);
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model = core.read_model(FLAGS_m);
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if (!outputs.empty()) {
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for (size_t i = 0; i < outputs.size(); i++) {
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auto output = model->add_output(outputs[i], ports[i]);
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output.set_names({outputs[i] + ":" + std::to_string(ports[i])});
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}
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for (const auto& output_with_port : outputs_with_ports) {
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auto output = model->add_output(output_with_port.first, output_with_port.second);
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output.set_names({output_with_port.first + ":" + std::to_string(output_with_port.second)});
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}
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check_number_of_inputs(model->inputs().size(), numInputFiles);
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ov::preprocess::PrePostProcessor proc(model);
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@ -350,14 +329,14 @@ int main(int argc, char* argv[]) {
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size_t count_file = 1;
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if (!output_data.first.empty()) {
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output_name_files = convert_str_to_vector(output_data.first);
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if (output_name_files.size() != outputs.size() && !outputs.empty()) {
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if (output_name_files.size() != outputs.size() && outputs.size()) {
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throw std::logic_error("The number of output files is not equal to the number of network outputs.");
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}
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count_file = output_name_files.empty() ? 1 : output_name_files.size();
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}
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if (!reference_data.first.empty()) {
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reference_name_files = convert_str_to_vector(reference_data.first);
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if (reference_name_files.size() != outputs.size() && !outputs.empty()) {
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if (reference_name_files.size() != outputs.size() && outputs.size()) {
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throw std::logic_error("The number of reference files is not equal to the number of network outputs.");
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}
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count_file = reference_name_files.empty() ? 1 : reference_name_files.size();
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@ -369,10 +348,10 @@ int main(int argc, char* argv[]) {
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// -----------------------------------------------------------------------------------------------------
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// --------------------------- Step 5. Do inference --------------------------------------------------------
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std::vector<std::vector<uint8_t>> ptrUtterances;
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std::vector<std::vector<uint8_t>> vectorPtrScores((outputs.size() == 0) ? executableNet.outputs().size()
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: outputs.size());
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std::vector<uint16_t> numScoresPerOutput((outputs.size() == 0) ? executableNet.outputs().size()
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: outputs.size());
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const auto effective_outputs_size = outputs.size() ? outputs.size() : executableNet.outputs().size();
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std::vector<std::vector<uint8_t>> vectorPtrScores(effective_outputs_size);
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std::vector<uint16_t> numScoresPerOutput(effective_outputs_size);
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std::vector<std::vector<uint8_t>> vectorPtrReferenceScores(reference_name_files.size());
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std::vector<ScoreErrorT> vectorFrameError(reference_name_files.size()),
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vectorTotalError(reference_name_files.size());
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@ -500,10 +479,10 @@ int main(int argc, char* argv[]) {
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inferRequest.inferRequest.wait();
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if (inferRequest.frameIndex >= 0)
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for (size_t next_output = 0; next_output < count_file; next_output++) {
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std::string outputName = (outputs.size() == 0)
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? executableNet.output(next_output).get_any_name()
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: output_names[next_output];
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auto dims = executableNet.output(outputName).get_shape();
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const auto output_name = outputs.size() > next_output
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? outputs[next_output]
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: executableNet.output(next_output).get_any_name();
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auto dims = executableNet.output(output_name).get_shape();
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numScoresPerOutput[next_output] = std::accumulate(std::begin(dims),
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std::end(dims),
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size_t{1},
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@ -519,7 +498,7 @@ int main(int argc, char* argv[]) {
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(inferRequest.frameIndex) / batchSize;
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ov::Tensor outputBlob =
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inferRequest.inferRequest.get_tensor(executableNet.output(outputName));
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inferRequest.inferRequest.get_tensor(executableNet.output(output_name));
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// locked memory holder should be alive all time while access to its buffer happens
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auto byteSize = numScoresPerOutput[next_output] * sizeof(float);
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std::memcpy(outputFrame, outputBlob.data<float>(), byteSize);
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@ -527,7 +506,7 @@ int main(int argc, char* argv[]) {
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if (!FLAGS_r.empty()) {
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/** Compare output data with reference scores **/
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ov::Tensor outputBlob =
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inferRequest.inferRequest.get_tensor(executableNet.output(outputName));
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inferRequest.inferRequest.get_tensor(executableNet.output(output_name));
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if (numScoresPerOutput[next_output] / numFrameElementsReference[next_output] ==
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batchSize) {
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@ -677,9 +656,10 @@ int main(int argc, char* argv[]) {
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}
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if (!FLAGS_r.empty()) {
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// print statistical score error
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std::string outputName = (outputs.size() == 0) ? executableNet.output(next_output).get_any_name()
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: output_names[next_output];
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std::cout << "Output name: " << outputName << std::endl;
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const auto output_name = outputs.size() > next_output
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? outputs[next_output]
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: executableNet.output(next_output).get_any_name();
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std::cout << "Output name: " << output_name << std::endl;
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std::cout << "Number scores per frame: " << numScoresPerOutput[next_output] / batchSize << std::endl
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<< std::endl;
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print_reference_compare_results(vectorTotalError[next_output], numFrames, std::cout);
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@ -505,4 +505,29 @@ std::pair<std::string, std::vector<std::string>> parse_parameters(const std::str
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filename += comma_delim;
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}
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return {filename, layers_names};
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}
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}
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std::vector<std::pair<std::string, size_t>> parse_to_extract_port(const std::vector<std::string>& full_names) {
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std::vector<std::pair<std::string, size_t>> result;
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for (const auto& full_name : full_names) {
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auto pos_layer = full_name.rfind(":");
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if (pos_layer == std::string::npos) {
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throw std::logic_error("Output " + full_name + " doesn't have a port");
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}
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const auto name = full_name.substr(0, pos_layer);
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try {
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const size_t port = std::stoul(full_name.substr(pos_layer + 1));
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result.push_back({name, port});
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} catch (const std::exception&) {
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throw std::logic_error("Ports should have integer type");
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}
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}
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return result;
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}
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const std::vector<std::string>& get_first_non_empty(const std::vector<std::string>& first,
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const std::vector<std::string>& second) {
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if (!first.empty())
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return first;
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return second;
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}
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