From c32e327320078808b04fd38e23b95e8a197cb856 Mon Sep 17 00:00:00 2001 From: Rune Harlyk Date: Mon, 22 Jul 2024 13:50:36 +0200 Subject: [PATCH] =?UTF-8?q?=F0=9F=8D=87=20Adds=20tensorflow=20sin=20exampl?= =?UTF-8?q?e?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- esp32/lib/ESP32-sveltekit/ESP32SvelteKit.cpp | 2 +- esp32/src/NeuralNetwork.cpp | 74 ++++++++++ esp32/src/NeuralNetwork.h | 38 ++++++ esp32/src/main.cpp | 24 +++- esp32/src/model.h | 134 +++++++++++++++++++ 5 files changed, 270 insertions(+), 2 deletions(-) create mode 100644 esp32/src/NeuralNetwork.cpp create mode 100644 esp32/src/NeuralNetwork.h create mode 100644 esp32/src/model.h diff --git a/esp32/lib/ESP32-sveltekit/ESP32SvelteKit.cpp b/esp32/lib/ESP32-sveltekit/ESP32SvelteKit.cpp index bb6bcf0..626a97e 100644 --- a/esp32/lib/ESP32-sveltekit/ESP32SvelteKit.cpp +++ b/esp32/lib/ESP32-sveltekit/ESP32SvelteKit.cpp @@ -206,7 +206,7 @@ void IRAM_ATTR ESP32SvelteKit::loop() { _wifiSettingsService.loop(); _apSettingsService.loop(); #if FT_ENABLED(FT_ANALYTICS) - _analyticsService.loop(); + _analyticsService.loop(); #endif #if FT_ENABLED(FT_BATTERY) _batteryService.loop(); diff --git a/esp32/src/NeuralNetwork.cpp b/esp32/src/NeuralNetwork.cpp new file mode 100644 index 0000000..b4238ca --- /dev/null +++ b/esp32/src/NeuralNetwork.cpp @@ -0,0 +1,74 @@ +#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) { + int8_t x_quantized = value / input->params.scale + input->params.zero_point; + input->data.int8[0] = x_quantized; +} + +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; + } + + int8_t y_quantized = output->data.int8[0]; + + float y = (y_quantized - output->params.zero_point) * output->params.scale; + + return y; +} \ No newline at end of file diff --git a/esp32/src/NeuralNetwork.h b/esp32/src/NeuralNetwork.h new file mode 100644 index 0000000..891e7f8 --- /dev/null +++ b/esp32/src/NeuralNetwork.h @@ -0,0 +1,38 @@ +#ifndef __NeuralNetwork__ +#define __NeuralNetwork__ + +#include "model.h" +#include + +namespace tflite { +template +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 \ No newline at end of file diff --git a/esp32/src/main.cpp b/esp32/src/main.cpp index fb473af..a5aaea4 100644 --- a/esp32/src/main.cpp +++ b/esp32/src/main.cpp @@ -1,8 +1,13 @@ #include #include +#include "NeuralNetwork.h" +#include + #define SERIAL_BAUD_RATE 115200 +NeuralNetwork *nn; + DRAM_ATTR PsychicHttpServer server; DRAM_ATTR ESP32SvelteKit spot(&server, 130); @@ -11,6 +16,23 @@ void setup() { Serial.begin(SERIAL_BAUD_RATE); spot.begin(); + + nn = new NeuralNetwork(); } -void loop() { vTaskDelete(NULL); } +void run_nn() { + float x = random(6); + x = max(min(x, 6.28f), 0.0f); + + float y_pred = nn->predict(x); + float y = sin(x); + + float error = abs(y_pred - y); + + ESP_LOGI("Run nn", "Predicted sin:%.2f,\t actual:%.2f,\t error%.2f", y_pred, y, error); +} + +void loop() { + EXECUTE_EVERY_N_MS(1000, TIME_IT(run_nn())); + spot.loop(); +} diff --git a/esp32/src/model.h b/esp32/src/model.h new file mode 100644 index 0000000..5e2e2ca --- /dev/null +++ b/esp32/src/model.h @@ -0,0 +1,134 @@ +alignas(16) const unsigned char g_model[] = { + 0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x14, 0x00, 0x20, 0x00, 0x1c, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10, + 0x00, 0x0c, 0x00, 0x00, 0x00, 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0x00, 0x09, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09}; + +const int g_model_len = sizeof(g_model); \ No newline at end of file