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support Cortex-M + ESP, support [email protected] on Cortex-M, support [email protected].…
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,5 +1,5 @@ | ||
| name=EloquentTinyML | ||
| version=0.0.10 | ||
| version=2.4.0 | ||
| author=Simone Salerno,[email protected] | ||
| maintainer=Simone Salerno,[email protected] | ||
| sentence=An eloquent interface to Tensorflow Lite for Microcontrollers | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,237 +1,22 @@ | ||
| #pragma once | ||
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| #include <Arduino.h> | ||
| #include <math.h> | ||
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| #ifdef max | ||
| #define REDEFINE_MAX | ||
| #undef max | ||
| #undef min | ||
| #endif | ||
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| #include <math.h> | ||
| #include "tensorflow/lite/version.h" | ||
| #include "tensorflow/lite/micro/kernels/all_ops_resolver.h" | ||
| #include "tensorflow/lite/micro/micro_error_reporter.h" | ||
| #include "tensorflow/lite/micro/micro_interpreter.h" | ||
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| #if defined(ESP32) | ||
| #include "TfLiteESP32.h" | ||
| #else | ||
| #include "TfLiteARM.h" | ||
| #endif | ||
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| #ifdef REDEFINE_MAX | ||
| #define max(a,b) ((a)>(b)?(a):(b)) | ||
| #define min(a,b) ((a)<(b)?(a):(b)) | ||
| #endif | ||
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| namespace Eloquent { | ||
| namespace TinyML { | ||
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| enum TfLiteError { | ||
| OK, | ||
| VERSION_MISMATCH, | ||
| CANNOT_ALLOCATE_TENSORS, | ||
| NOT_INITIALIZED, | ||
| INVOKE_ERROR | ||
| }; | ||
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| /** | ||
| * Eloquent interface to Tensorflow Lite for Microcontrollers | ||
| * | ||
| * @tparam inputSize | ||
| * @tparam outputSize | ||
| * @tparam tensorArenaSize how much memory to allocate to the tensors | ||
| */ | ||
| template<size_t inputSize, size_t outputSize, size_t tensorArenaSize> | ||
| class TfLite { | ||
| public: | ||
| /** | ||
| * Contructor | ||
| * @param modelData a model as exported by tinymlgen | ||
| */ | ||
| TfLite() : | ||
| failed(false) { | ||
| } | ||
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| ~TfLite() { | ||
| delete reporter; | ||
| delete interpreter; | ||
| } | ||
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| /** | ||
| * Inizialize NN | ||
| * | ||
| * @param modelData | ||
| * @return | ||
| */ | ||
| bool begin(const unsigned char *modelData) { | ||
| tflite::ops::micro::AllOpsResolver resolver; | ||
| reporter = new tflite::MicroErrorReporter(); | ||
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| model = tflite::GetModel(modelData); | ||
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| // assert model version and runtime version match | ||
| if (model->version() != TFLITE_SCHEMA_VERSION) { | ||
| failed = true; | ||
| error = VERSION_MISMATCH; | ||
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| reporter->Report( | ||
| "Model provided is schema version %d not equal " | ||
| "to supported version %d.", | ||
| model->version(), TFLITE_SCHEMA_VERSION); | ||
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| return false; | ||
| } | ||
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| interpreter = new tflite::MicroInterpreter(model, resolver, tensorArena, tensorArenaSize, reporter); | ||
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| if (interpreter->AllocateTensors() != kTfLiteOk) { | ||
| failed = true; | ||
| error = CANNOT_ALLOCATE_TENSORS; | ||
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| return false; | ||
| } | ||
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| input = interpreter->input(0); | ||
| output = interpreter->output(0); | ||
| error = OK; | ||
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| return true; | ||
| } | ||
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| /** | ||
| * Test if the initialization completed fine | ||
| */ | ||
| bool initialized() { | ||
| return !failed; | ||
| } | ||
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| /** | ||
| * | ||
| * @param input | ||
| * @param output | ||
| * @return | ||
| */ | ||
| uint8_t predict(uint8_t *input, uint8_t *output = NULL) { | ||
| // abort if initialization failed | ||
| if (!initialized()) | ||
| return sqrt(-1); | ||
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| memcpy(this->input->data.uint8, input, sizeof(uint8_t) * inputSize); | ||
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| if (interpreter->Invoke() != kTfLiteOk) { | ||
| reporter->Report("Inference failed"); | ||
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| return sqrt(-1); | ||
| } | ||
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| // copy output | ||
| if (output != NULL) { | ||
| for (uint16_t i = 0; i < outputSize; i++) | ||
| output[i] = this->output->data.uint8[i]; | ||
| } | ||
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| return this->output->data.uint8[0]; | ||
| } | ||
|
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| /** | ||
| * Run inference | ||
| * @return output[0], so you can use it directly if it's the only output | ||
| */ | ||
| float predict(float *input, float *output = NULL) { | ||
| // abort if initialization failed | ||
| if (!initialized()) { | ||
| error = NOT_INITIALIZED; | ||
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| return sqrt(-1); | ||
| } | ||
|
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| // copy input | ||
| for (size_t i = 0; i < inputSize; i++) | ||
| this->input->data.f[i] = input[i]; | ||
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| if (interpreter->Invoke() != kTfLiteOk) { | ||
| error = INVOKE_ERROR; | ||
| reporter->Report("Inference failed"); | ||
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| return sqrt(-1); | ||
| } | ||
|
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| // copy output | ||
| if (output != NULL) { | ||
| for (uint16_t i = 0; i < outputSize; i++) | ||
| output[i] = this->output->data.f[i]; | ||
| } | ||
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| return this->output->data.f[0]; | ||
| } | ||
|
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| /** | ||
| * Predict class | ||
| * @param input | ||
| * @return | ||
| */ | ||
| uint8_t predictClass(float *input) { | ||
| float output[outputSize]; | ||
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| predict(input, output); | ||
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| return probaToClass(output); | ||
| } | ||
|
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| /** | ||
| * Get class with highest probability | ||
| * @param output | ||
| * @return | ||
| */ | ||
| uint8_t probaToClass(float *output) { | ||
| uint8_t classIdx = 0; | ||
| float maxProba = output[0]; | ||
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| for (uint8_t i = 1; i < outputSize; i++) { | ||
| if (output[i] > maxProba) { | ||
| classIdx = i; | ||
| maxProba = output[i]; | ||
| } | ||
| } | ||
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| return classIdx; | ||
| } | ||
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| /** | ||
| * Get error | ||
| * @return | ||
| */ | ||
| TfLiteError getError() { | ||
| return error; | ||
| } | ||
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| /** | ||
| * Get error message | ||
| * @return | ||
| */ | ||
| const char* errorMessage() { | ||
| switch (error) { | ||
| case OK: | ||
| return "No error"; | ||
| case VERSION_MISMATCH: | ||
| return "Version mismatch"; | ||
| case CANNOT_ALLOCATE_TENSORS: | ||
| return "Cannot allocate tensors"; | ||
| case NOT_INITIALIZED: | ||
| return "Interpreter has not been initialized"; | ||
| case INVOKE_ERROR: | ||
| return "Interpreter invoke() returned an error"; | ||
| default: | ||
| return "Unknown error"; | ||
| } | ||
| } | ||
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| protected: | ||
| bool failed; | ||
| TfLiteError error; | ||
| uint8_t tensorArena[tensorArenaSize]; | ||
| tflite::ErrorReporter *reporter; | ||
| tflite::MicroInterpreter *interpreter; | ||
| TfLiteTensor *input; | ||
| TfLiteTensor *output; | ||
| const tflite::Model *model; | ||
| }; | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,28 @@ | ||
| // | ||
| // Created by Simone on 28/10/2021. | ||
| // | ||
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| #ifndef ELOQUENTTINYML_TFLITEARM_H | ||
| #define ELOQUENTTINYML_TFLITEARM_H | ||
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| #include "tensorflow_arm/tensorflow/lite/version.h" | ||
| #include "tensorflow_arm/tensorflow/lite/schema/schema_generated.h" | ||
| #include "tensorflow_arm/tensorflow/lite/micro/all_ops_resolver.h" | ||
| #include "tensorflow_arm/tensorflow/lite/micro/micro_error_reporter.h" | ||
| #include "tensorflow_arm/tensorflow/lite/micro/micro_interpreter.h" | ||
| #include "TfLiteAbstract.h" | ||
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| namespace Eloquent { | ||
| namespace TinyML { | ||
|
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| /** | ||
| * Run TensorFlow Lite models on ARM | ||
| */ | ||
| template<size_t inputSize, size_t outputSize, size_t tensorArenaSize> | ||
| class TfLite : public TfLiteAbstract<tflite::AllOpsResolver, inputSize, outputSize, tensorArenaSize> { | ||
| }; | ||
| } | ||
| } | ||
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| #endif //ELOQUENTTINYML_TFLITEESP32_H |
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