【丁寧解説】tensorflow liteを使って自作モデルを変換しラズパイで推論

pip3 install --extra-index-url https://google-coral.github.io/py-repo/ tflite_runtime

sudo apt install libavutil56 libcairo-gobject2 libgtk-3-0 libqtgui4 libpango-1.0-0 libqtcore4 libavcodec58 libcairo2 libswscale5 libtiff5 libqt4-test libatk1.0-0 libavformat58 libgdk-pixbuf2.0-0 libilmbase23 libjasper1 libopenexr23 libpangocairo-1.0-0 libwebp6

sudo apt-get install libatlas-base-dev
sudo apt-get install libjasper-dev

sudo pip3 install opencv-python==4.1.0.25

import tensorflow as tf

#モデルの読み込み
model = tf.keras.applications.EfficientNetB3(
    include_top=True, weights='imagenet'
)

#TFliteモデルへの変換
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open("EfB3_imagenet.tflite", "wb").write(tflite_model)

#TF liteのライブラリインポート
import tflite_runtime.interpreter as tflite
#画像読み込みと推論のためのライブラリインポート
import numpy as np
import cv2

#モデルの読み込み
interpreter = tflite.Interpreter(model_path="EfB3_arcface.tflite")
#モデルのテンソルへの割り当て
interpreter.allocate_tensors()

#入力の詳細の取得
input_details = interpreter.get_input_details()
#出力の詳細の取得
output_details = interpreter.get_output_details()

[{'name': 'input_1',
  'index': 0,
  'shape': array([  1, 300, 300,   3]),
  'shape_signature': array([ -1, 300, 300,   3]),
  'dtype': numpy.float32,
  'quantization': (0.0, 0),
  'quantization_parameters': {'scales': array([], dtype=float32),
   'zero_points': array([], dtype=int32),
   'quantized_dimension': 0},
  'sparsity_parameters': {}}]

[{'name': 'Identity',
  'index': 780,
  'shape': array([   1, 1000]),
  'shape_signature': array([  -1, 1000]),
  'dtype': numpy.float32,
  'quantization': (0.0, 0),
  'quantization_parameters': {'scales': array([], dtype=float32),
   'zero_points': array([], dtype=int32),
   'quantized_dimension': 0},
  'sparsity_parameters': {}}]

#画像のパスの定義
img_path = "画像のパス"
#画像の読み込み
temp = cv2.imread(img_path)
#BGRの並びをRGBに変換
temp = cv2.cvtColor(temp, cv2.COLOR_BGR2RGB)
#画像のリサイズ
temp = cv2.resize(temp, (300, 300))
#バッチ次元の追加と型変換
input_data = np.expand_dims(temp, axis=0).astype("float32")

#バッチ次元の追加と型変換
input_data = np.expand_dims(temp, axis=0).astype("float32")

#入力画像のセット
interpreter.set_tensor(input_details[0]['index'], input_data)

[{'name': 'input_1',
  'index': 0,
  'shape': array([  1, 300, 300,   3]),
  'shape_signature': array([ -1, 300, 300,   3]),
  'dtype': numpy.float32,
  'quantization': (0.0, 0),
  'quantization_parameters': {'scales': array([], dtype=float32),
   'zero_points': array([], dtype=int32),
   'quantized_dimension': 0},
  'sparsity_parameters': {}}]

interpreter.invoke()

output_data = interpreter.get_tensor(output_details[0]['index'])

import tflite_runtime.interpreter as tflite
import numpy as np
import cv2
import time

interpreter = tflite.Interpreter("EfB3_imagenet.tflite")

interpreter.allocate_tensors()

input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

img_path = "画像パス"
temp = cv2.imread(img_path)
temp = cv2.cvtColor(temp, cv2.COLOR_BGR2RGB)
temp = cv2.resize(temp, (300, 300))
input_data = np.expand_dims(temp, axis=0).astype("float32")

interpreter.set_tensor(input_details[0]['index'], input_data)

interpreter.invoke()

output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data.argmax())