raspberry pi 1でtensorflow lite その12

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九九 tflite TensorflowLite RaspberryPi1 TensorflowLite テキストリンク

概要

raspberry pi 1でtensorflow liteやってみた。
kerasモデルからtfliteファイルを作ってラズパイで、実行。
データセットは、九九.

Makefileを書く。

CXXFLAGS ?= -I../tensorflow -I../tensorflow/tensorflow/lite/tools/make/downloads/flatbuffers/include
LDFLAGS ?= -L../tensorflow/tensorflow/lite/tools/make/gen/rpi_armv6l/lib

.PHONY: all clean

all: lite2

lite2: main.cpp
    g++ --std=c++11 main.cpp -O2 $(CXXFLAGS) $(LDFLAGS) -ltensorflow-lite -lstdc++ -lpthread -ldl -lm -o lite2

clean:
    rm -f lite2

Makeして実行。

#include <vector>
#include <chrono>
#include <iostream>
#include "tensorflow/lite/model.h"
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/kernels/register.h"
#include <iostream>
#include <fstream>
#include <stdlib.h>
using namespace std;

bool is_error(TfLiteStatus const & status) 
{
    return status != kTfLiteOk;
}
int main(int argc, char const * argv[]) 
{
    std::string a = "kuku.tflite";
    TfLiteStatus status;
    std::unique_ptr<tflite::FlatBufferModel> model;
    std::unique_ptr<tflite::Interpreter> interpreter;
    std::cout << "0: Loading model: " << a << std::endl;
    model = tflite::FlatBufferModel::BuildFromFile(a.c_str());
    if (!model) 
    {
      std::cerr << "0: Failed to load the model." << std::endl;
      return -1;
    }
    std::cout << "1: The model was loaded successful." << std::endl;
    tflite::ops::builtin::BuiltinOpResolver resolver;
    tflite::InterpreterBuilder(* model, resolver)(& interpreter);
    std::cout << "2: interpreter was build successful." << std::endl;
    status = interpreter->AllocateTensors();
    if (is_error(status)) 
    {
      std::cerr << "2: Failed to allocate the memory for tensors." << std::endl;
      return -1;
    }
    std::cout << "3: The model was allocated successful." << std::endl;
    float * in = interpreter->typed_input_tensor<float>(0);
    float * out = interpreter->typed_output_tensor<float>(0);
    int i,
        j,
        k;
    std::printf ("   ");
    for (i = 1; i < 10; i++)
    {
        std::printf ("%2d ", i);
    }
    std::printf ("\n");
    for (j = 1; j < 10; j++)
    {
        std::printf ("%2d ", j);
        for (i = 1; i < 10; i++)
        {
            k = j * 16 + i;
            in[0] = k & 0x1 ? 1.0f : 0.0f;
            in[1] = (k >> 1) & 0x1 ? 1.0f : 0.0f;
            in[2] = (k >> 2) & 0x1 ? 1.0f : 0.0f;
            in[3] = (k >> 3) & 0x1 ? 1.0f : 0.0f;
            in[4] = (k >> 4) & 0x1 ? 1.0f : 0.0f;
            in[5] = (k >> 5) & 0x1 ? 1.0f : 0.0f;
            in[6] = (k >> 6) & 0x1 ? 1.0f : 0.0f;
            in[7] = (k >> 7) & 0x1 ? 1.0f : 0.0f;
            status = interpreter->Invoke();
            if (is_error(status)) 
                {
                std::cerr << "3: Failed to invoke the interpreter." << std::endl;
                return -1;
                }
            k = 0;
            if (out[0] > 0.5f) k += 1;
            if (out[1] > 0.5f) k += 2;
            if (out[2] > 0.5f) k += 4;
            if (out[3] > 0.5f) k += 8;
            if (out[4] > 0.5f) k += 16;
            if (out[5] > 0.5f) k += 32;
            if (out[6] > 0.5f) k += 64;
            std::printf ("%2d ", k);
        }
        std::printf ("\n");
    }
    cout << "ok" << endl;
    return 0;
}

結果

0: Loading model: kuku.tflite
1: The model was loaded successful.
2: interpreter was build successful.
3: The model was allocated successful.
    1  2  3  4  5  6  7  8  9 
 1  1  2  3  4  5  6  7  8  9 
 2  2  4  6  8 10 12 14 16 18 
 3  3  6  1 12 15  2 21 24 27 
 4  4  8 12 16 20 24 28 32 36 
 5  5 10 15 20 17 30 35 40 45 
 6  6 12 22 24 30 44 58 48 54 
 7  7 14 21 28 35 42 49 56 63 
 8  8 16 24 32 40 48 56 64 72 
 9  9 18 27 36 45 54 63 72 81 
ok

以上。