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raspberrypi / pico-tflmicro

Pico TensorFlow Lite Port
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Simple model does not work #1

Closed mattn closed 3 years ago

mattn commented 3 years ago

Hi, thanks your great work.

I'm trying to write code to make inference with simple xor-gate model.

import numpy as np

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import RMSprop
from tensorflow.lite.python import lite

X_train = np.array([[0, 0],
                    [255, 0],
                    [0, 255],
                    [255, 255]], dtype = 'int8')
Y_train = np.array([0,
                    255,
                    255,
                    0], dtype = 'int8')
model = Sequential()
output_count_layer0 = 2
model.add(
    Dense(
      output_count_layer0,
      input_shape=(2, ),
      activation='sigmoid'))  # Need to specify input shape for input layer
output_count_layer1 = 1
model.add(Dense(output_count_layer1, activation='linear'))
model.compile(
    loss='mean_squared_error', optimizer=RMSprop(), metrics=['accuracy'])
BATCH_SIZE = 4
history = model.fit(
    X_train, Y_train, batch_size=BATCH_SIZE, epochs=3600, verbose=1)
X_test = X_train
Y_test = Y_train
score = model.evaluate(X_test, Y_test, verbose=0)
model.save('xor_model.h5')

converter = lite.TFLiteConverter.from_keras_model_file('xor_model.h5')
#converter.optimizations = [lite.Optimize.DEFAULT]
#converter.target_spec.supported_types = [tf.float32]
tflite_model = converter.convert()
open('xor_model.tflite', 'wb').write(tflite_model)
#include <stdio.h>
#include "pico/stdlib.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"

alignas(8) const unsigned char xor_model_tflite[] = {
  0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x12, 0x00,
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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();

  // 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);
  int8_t x_quantized = 1 / input->params.scale + input->params.zero_point;

  while (true) {
    printf("%d, %d, %d\n", input->type, input->dims[0].data[0], input->dims[0].size);
    printf("%d, %d, %d\n", output->type, output->dims[0].data[0], output->dims[0].size);
    int c1 = getchar();
    printf("%c\n", c1);
    int c2 = getchar();
    printf("%c\n", c2);
    //input->data.f16[0].data = c1 == '1' ? 1.0f : 0.0f;
    //input->data.f16[1].data = c2 == '1' ? 1.0f : 0.0f;
    int i;
    for (i = 1; i < 235; i++)
      input->data.int8[i] = 0;
    input->data.int8[0] = c1 == '1' ? 255 : 0;
    input->data.int8[1] = c2 == '1' ? 255 : 0;
    status = interpreter->Invoke();
    if (status != kTfLiteOk) {
      TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
      continue;
    }
    printf("%d,%d\n", output->data.int8[0], output->data.int8[1]);
    sleep_ms(1000);
  }
  return 0;
}

But output always be zero. Is this a bug of pico-tfmicro?

lurch commented 3 years ago

https://github.com/raspberrypi/pico-tflmicro#contributing says "This repository (https://github.com/raspberrypi/pico-tflmicro) is read-only, because it has been automatically generated from the master TensorFlow repository at https://github.com/tensorflow/tensorflow. This means that all issues and pull requests need to be filed there." ?

petewarden commented 3 years ago

Sorry you're hitting problems @mattn! I don't see anything obviously wrong from inspection, but I would suggest the next step as running the same code on a different Linux platform (without the pico/stdlib.h include) to see if we can reproduce it there.

If you can file this bug against https://github.com/tensorflow/tensorflow and cc me, that would be the best way to continue.

mattn commented 3 years ago

I figure out what was wrong in few hours ago. TF_LITE_STATIC_MEMORY is required for raspberrypipico.

#define TF_LITE_STATIC_MEMORY

image

https://gist.github.com/mattn/cb26cf4cc2641ecb4ed07f299cd298ca