📦 Moves NN

esp32/src/NeuralNetwork.cpp

@@ -1,67 +0,0 @@
-#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];
-}

esp32/src/NeuralNetwork.h

@@ -1,38 +0,0 @@
-#ifndef __NeuralNetwork__
-#define __NeuralNetwork__
-
-#include "model.h"
-#include <stdint.h>
-
-namespace tflite {
-template <unsigned int tOpCount>
-class MicroMutableOpResolver;
-class ErrorReporter;
-class Model;
-class MicroInterpreter;
-} // namespace tflite
-
-struct TfLiteTensor;
-
-class NeuralNetwork {
-  private:
-    tflite::MicroMutableOpResolver<10> *resolver;
-    tflite::ErrorReporter *error_reporter;
-    tflite::MicroInterpreter *interpreter;
-    const tflite::Model *model;
-    uint8_t *tensor_arena;
-    TfLiteTensor *input;
-    TfLiteTensor *output;
-    const int _kArenaSize = 20000;
-
-    void setInput(float value);
-
-    float predict();
-
-  public:
-    NeuralNetwork(const void *model_data = g_model, const int kArenaSize = 20000);
-
-    float predict(float value);
-};
-
-#endif