Remove locks
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@@ -5,15 +5,6 @@
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namespace ANSCENTER {
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bool ANSOVSCRFDFD::Initialize(std::string licenseKey, ModelConfig modelConfig, const std::string& modelZipFilePath, const std::string& modelZipPassword, std::string& labelMap) {
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bool result = ANSFDBase::Initialize(licenseKey, modelConfig, modelZipFilePath, modelZipPassword, labelMap);
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// Serialize derived init against concurrent extract re-entries on the
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// same folder. See ModelFolderLockGuard in ANSEngineCommon.h.
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ModelFolderLockGuard _flg(_modelFolder, "ANSOVSCRFDFD::Initialize");
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if (!_flg.acquired()) {
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this->_logger.LogError("ANSOVSCRFDFD::Initialize",
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"Timed out waiting for model-folder lock: " + _modelFolder,
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__FILE__, __LINE__);
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return false;
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}
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labelMap = "Face";
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// We do not need to check for the license
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_licenseValid = true;
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@@ -46,7 +37,6 @@ namespace ANSCENTER {
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<< model->input().get_any_name() << "' will be used." << slog::endl;
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}
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const ov::Layout& inputLayout = layout;
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//ppp.input().tensor().set_element_type(ov::element::u8).set_layout("NHWC").set_color_format(ov::preprocess::ColorFormat::BGR);
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@@ -70,7 +60,6 @@ namespace ANSCENTER {
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int outputSize = model->outputs().size();
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std::cout << "output Size:" << outputSize << std::endl;
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const ov::Layout outputLayout{ "CHW" };
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maxProposalsCount = model->outputs().front().get_shape()[ov::layout::height_idx(outputLayout)];
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for (const auto& output : model->outputs()) {
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@@ -110,15 +99,6 @@ namespace ANSCENTER {
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// We need to get the _modelFolder
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bool result = ANSFDBase::LoadModel(modelZipFilePath, modelZipPassword);
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if (!result) return false;
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// Serialize derived init against concurrent extract re-entries on the
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// same folder. See ModelFolderLockGuard in ANSEngineCommon.h.
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ModelFolderLockGuard _flg(_modelFolder, "ANSOVSCRFDFD::LoadModel");
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if (!_flg.acquired()) {
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this->_logger.LogError("ANSOVSCRFDFD::LoadModel",
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"Timed out waiting for model-folder lock: " + _modelFolder,
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__FILE__, __LINE__);
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return false;
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}
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// We need to get the modelfolder from here
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std::string xmlfile = CreateFilePath(_modelFolder, "scrfdface.xml");
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@@ -257,7 +237,6 @@ namespace ANSCENTER {
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// Perform inference
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inference_request_.infer();
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std::cout << "Model outputs:" << std::endl;
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auto ovOutputs= compiled_model_.outputs();
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for (int i = 0; i < ovOutputs.size();i++) {
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@@ -288,7 +267,6 @@ namespace ANSCENTER {
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std::cout << "Total valid detections: " << valid_detections.size() << std::endl;
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/* float* score = inference_request_.get_output_tensor(2).data<float>();
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//if (score != nullptr) {
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// std::cout << "Score:" << score[0]<<std::endl;
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@@ -296,7 +274,6 @@ namespace ANSCENTER {
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float* bbox = inference_request_.get_output_tensor(5).data<float>();*/
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// Get the output tensor
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//// Step 0: Prepare input
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//this->PreprocessImage(im);
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