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Using Custom TensorFlow Lite Models on Raspberry Pi Pico

に公開
Python
機械学習
C言語
TensorFlowLite
Raspberry Pi Pico
tech

Introduction

Raspberry Pi Pico is a lot of fun, isn't it? The other day, I made a button that uses the Raspberry Pi Pico to automatically input if err != nil {...} in Go.

https://twitter.com/mattn_jp/status/1358067345088892928

Recently, I've been trying to run TensorFlow Lite on the Raspberry Pi Pico, and yesterday I finally solved a problem where my custom model just wouldn't work no matter what I did.

What I Built

I embedded a model that performs inference for an XOR gate, which turns an LED on or off based on input from the serial port.

https://twitter.com/mattn_jp/status/1360041410930429953

First, Create the Model

There's nothing particularly difficult here; it's just a standard training process.

import tensorflow as tf
from tensorflow import keras
import numpy as np

x_train = np.array([[0, 0],
                    [1, 0],
                    [0, 1],
                    [1, 1]]).astype(np.float32)

y_train = np.array([0,
                    1,
                    1,
                    0]).astype(np.float32)


model = tf.keras.Sequential()
model.add(keras.layers.Dense(16, activation='relu', input_shape=(2,)))
model.add(keras.layers.Dense(16, activation='relu'))
model.add(keras.layers.Dense(1))
model.compile(optimizer='adam', loss="mse", metrics=["mae"])
history = model.fit(x_train, y_train, epochs=500, batch_size=4)
model.save('saved_model')


def representative_dataset():
    for i in range(4):
        yield([x_train[i]])


converter = tf.lite.TFLiteConverter.from_saved_model("saved_model")
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.float32
converter.inference_output_type = tf.float32
converter.representative_dataset = representative_dataset
model_tflite = converter.convert()

with open('xor_model.tflite', 'wb') as f:
    f.write(model_tflite)

The only thing to watch out for is creating it without quantization.

Writing the Code

Convert the resulting tflite model file into a C language byte array using a tool called xxd.

$ xxd -i xor_model.tflite > model.c

#include <stdio.h>
#include "pico/stdlib.h"
#include "hardware/gpio.h"

#define TF_LITE_STATIC_MEMORY
#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"

alignas(8) const unsigned char xor_model_tflite[] = {
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};

namespace {
  tflite::ErrorReporter* error_reporter = nullptr;
  const tflite::Model* model = nullptr;
  tflite::MicroInterpreter* interpreter = nullptr;
  TfLiteTensor* input = nullptr;
  TfLiteTensor* output = nullptr;
  constexpr int kTensorArenaSize = 2000;
  uint8_t tensor_arena[kTensorArenaSize];
}

int main() {
  stdio_init_all();
  gpio_init(25);
  gpio_set_dir(25, GPIO_OUT);

  // NOLINTNEXTLINE(runtime-global-variables)
  static tflite::MicroErrorReporter micro_error_reporter;
  error_reporter = &micro_error_reporter;

  model = tflite::GetModel(xor_model_tflite);

  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 1;
  }

  // This pulls in all the operation implementations we need.
  // NOLINTNEXTLINE(runtime-global-variables)
  static tflite::AllOpsResolver resolver;

  // Build an interpreter to run the model with.
  static tflite::MicroInterpreter static_interpreter(
      model, resolver, tensor_arena, kTensorArenaSize, error_reporter);
  interpreter = &static_interpreter;

  // Allocate memory from the tensor_arena for the model's tensors.
  TfLiteStatus status = interpreter->AllocateTensors();
  if (status != kTfLiteOk) {
    TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
    return 1;
  }

  input = interpreter->input_tensor(0);
  output = interpreter->output_tensor(0);

  while (true) {
    int c1 = getchar();
    printf("%c\n", c1);
    int c2 = getchar();
    printf("%c\n", c2);
    input->data.f[0] = c1 == '1' ? 1.0f : 0.0f;
    input->data.f[1] = c2 == '1' ? 1.0f : 0.0f;
    status = interpreter->Invoke();
    if (status != kTfLiteOk) {
      TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
      continue;
    }
    int on = output->data.f[0] > 0.5 ? 1 : 0;
    printf("XOR(%d, %d) = %d\n",
      c1 == '1' ? 1 : 0,
      c2 == '1' ? 1 : 0,
      on);
    gpio_put(25, on);
  }
  return 0;
}

One thing to note here: when creating a model embedded in the source, make sure to define TF_LITE_STATIC_MEMORY. It seems that without this, an unintended delete operator gets embedded, causing the application to fail to run correctly.

// C++ will automatically create class-specific delete operators for virtual
// objects, which by default call the global delete function. For embedded
// applications we want to avoid this, and won't be calling new/delete on these
// objects, so we need to override the default implementation with one that does
// nothing to avoid linking in ::delete().
// This macro needs to be included in all subclasses of a virtual base class in
// the private section.
#ifdef TF_LITE_STATIC_MEMORY
#define TF_LITE_REMOVE_VIRTUAL_DELETE \
  void operator delete(void* p) {}
#else
#define TF_LITE_REMOVE_VIRTUAL_DELETE
#endif

Build and Transfer

To build it, create a directory called tflite/tflite_xor directly under pico-examples, and create one CMakeLists.txt directly under tflite.

if (NOT PICO_NO_HARDWARE)
    add_subdirectory(tflite_xor)
endif ()

Then, create another one under tflite_xor.

link_directories(${CMAKE_SOURCE_DIR}/lib)

add_executable(tflite_xor
main.cxx
)

set_target_properties(
  tflite_xor
  PROPERTIES
  COMPILE_FLAGS -fno-rtti
  COMPILE_FLAGS -fno-exceptions
  COMPILE_FLAGS -fno-threadsafe-statics
  COMPILE_FLAGS -nostdlib
)


# Pull in our pico_stdlib which aggregates commonly used features
target_link_libraries(tflite_xor pico_stdlib pico-tflmicro)

pico_enable_stdio_usb(tflite_xor 1)
pico_enable_stdio_uart(tflite_xor 0)

pico_add_extra_outputs(tflite_xor)

# add url via pico_set_program_url
example_auto_set_url(tflite_xor)

target_include_directories(tflite_xor
  PRIVATE
  "c:/dev/pico-tflmicro/src"
  "c:/dev/pico-tflmicro/src/third_party/flatbuffers/include"
)

Since I'm developing on Windows, full paths are included; please correct them according to your own environment. If you're using the serial port, don't forget to set pico_enable_stdio_usb to 1.

Transfer the generated pico-examples/build/tflite/tflite_xor/tflite_xor.uf2 to your Raspberry Pi Pico, and you're done!

Conclusion

I wasted about half a day because I didn't notice TF_LITE_STATIC_MEMORY, but I finally got it working. If we can connect a camera, display, or Wi-Fi, we might even be able to perform image inference via HTTP.

Well, since it's also possible to achieve something like that by pouring a mime/multipart body into the serial port and having it respond with JSON, I'll try that if I have time.

Discussion