📦 Moves NN

esp32/lib/ESP32-sveltekit/NeuralNetwork.cpp

@@ -0,0 +1,67 @@
+#include "NeuralNetwork.h"
+
+#include "tensorflow/lite/micro/all_ops_resolver.h"
+#include "tensorflow/lite/micro/micro_error_reporter.h"
+#include "tensorflow/lite/micro/micro_interpreter.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+#include "tensorflow/lite/version.h"
+
+NeuralNetwork::NeuralNetwork(const void *model_data, const int kArenaSize) : _kArenaSize(kArenaSize) {
+    error_reporter = new tflite::MicroErrorReporter();
+
+    model = tflite::GetModel(model_data);
+    if (model->version() != TFLITE_SCHEMA_VERSION) {
+        TF_LITE_REPORT_ERROR(error_reporter, "Model provided is schema version %d not equal to supported version %d.",
+                             model->version(), TFLITE_SCHEMA_VERSION);
+        return;
+    }
+    // This pulls in the operators implementations we need
+    resolver = new tflite::MicroMutableOpResolver<10>();
+    resolver->AddFullyConnected();
+    resolver->AddMul();
+    resolver->AddAdd();
+    resolver->AddLogistic();
+    resolver->AddReshape();
+    resolver->AddQuantize();
+    resolver->AddDequantize();
+
+    tensor_arena = (uint8_t *)malloc(_kArenaSize);
+    if (!tensor_arena) {
+        TF_LITE_REPORT_ERROR(error_reporter, "Could not allocate arena");
+        return;
+    }
+
+    // Build an interpreter to run the model with.
+    interpreter = new tflite::MicroInterpreter(model, *resolver, tensor_arena, _kArenaSize, error_reporter);
+
+    // Allocate memory from the tensor_arena for the model's tensors.
+    TfLiteStatus allocate_status = interpreter->AllocateTensors();
+    if (allocate_status != kTfLiteOk) {
+        TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
+        return;
+    }
+
+    size_t used_bytes = interpreter->arena_used_bytes();
+    TF_LITE_REPORT_ERROR(error_reporter, "Used bytes %d\n", used_bytes);
+
+    // Obtain pointers to the model's input and output tensors.
+    input = interpreter->input(0);
+    output = interpreter->output(0);
+}
+
+void NeuralNetwork::setInput(float value) { input->data.f[0] = value; }
+
+float NeuralNetwork::predict(float value) {
+    setInput(value);
+    return predict();
+}
+
+float NeuralNetwork::predict() {
+    TfLiteStatus invoke_status = interpreter->Invoke();
+    if (invoke_status != kTfLiteOk) {
+        TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed!");
+        return -1;
+    }
+
+    return output->data.f[0];
+}