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https://github.com/jomjol/AI-on-the-edge-device.git
synced 2025-12-11 14:07:00 +03:00
rolling 2021-03-06
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@@ -361,14 +361,14 @@ bool ClassFlowPostProcessing::doFlow(string zwtime)
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if ((!AllowNegativeRates) && (Value < PreValue))
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{
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ErrorMessageText = ErrorMessageText + "Negative Rate - Returned old value - read value: " + zwvalue + " - raw value: " + ReturnRawValue;
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ErrorMessageText = ErrorMessageText + "Negative Rate - Returned old value - read value: " + zwvalue + " - raw value: " + ReturnRawValue + " - checked value: " + std::to_string(Value) + " ";
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Value = PreValue;
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zwvalue = RundeOutput(Value, AnzahlAnalog - DecimalShift);
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}
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if (useMaxRateValue && (abs(Value - PreValue) > MaxRateValue))
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{
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ErrorMessageText = ErrorMessageText + "Rate too high - Returned old value - read value: " + zwvalue + " ";
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ErrorMessageText = ErrorMessageText + "Rate too high - Returned old value - read value: " + zwvalue + " - checked value: " + std::to_string(Value) + " ";
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Value = PreValue;
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zwvalue = RundeOutput(Value, AnzahlAnalog - DecimalShift);
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}
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@@ -140,7 +140,10 @@ bool CTfLiteClass::LoadInputImageBasis(CImageBasis *rs)
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void CTfLiteClass::MakeAllocate()
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{
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static tflite::AllOpsResolver resolver;
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// printf(LogFile.getESPHeapInfo().c_str()); printf("\n");
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this->interpreter = new tflite::MicroInterpreter(this->model, resolver, this->tensor_arena, this->kTensorArenaSize, this->error_reporter);
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// printf(LogFile.getESPHeapInfo().c_str()); printf("\n");
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TfLiteStatus allocate_status = this->interpreter->AllocateTensors();
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if (allocate_status != kTfLiteOk) {
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@@ -229,7 +232,7 @@ CTfLiteClass::CTfLiteClass()
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this->interpreter = nullptr;
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this->input = nullptr;
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this->output = nullptr;
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this->kTensorArenaSize = 150 * 1024; /// laut testfile: 108000 - bisher 600
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this->kTensorArenaSize = 200 * 1024; /// laut testfile: 108000 - bisher 600
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this->tensor_arena = new uint8_t[kTensorArenaSize];
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}
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