The behavior is unconfirmed because I do not have C language implementation skills. Official tutorial on Tensorflow C binding generation

Appx1. C-API build procedure Native build procedure of Tensorflow v2.0.0 C API for RaspberryPi / arm64 devices (armhf / aarch64)

Appx2. C-API Usage

$ wget https://raw.githubusercontent.com/PINTO0309/Tensorflow-bin/main/C-library/2.2.0-armhf/install-buster.sh
$ ./install-buster.sh

$ sudo apt-get install -y \
    libhdf5-dev libc-ares-dev libeigen3-dev gcc gfortran \
    libgfortran5 libatlas3-base libatlas-base-dev \
    libopenblas-dev libopenblas-base libblas-dev \
    liblapack-dev cython3 openmpi-bin libopenmpi-dev \
    libatlas-base-dev python3-dev
$ sudo pip3 install pip --upgrade
$ sudo pip3 install keras_applications==1.0.8 --no-deps
$ sudo pip3 install keras_preprocessing==1.1.0 --no-deps
$ sudo pip3 install h5py==2.9.0
$ sudo pip3 install pybind11
$ pip3 install -U --user six wheel mock
$ sudo pip3 uninstall tensorflow
$ wget "https://raw.githubusercontent.com/PINTO0309/Tensorflow-bin/master/previous_versions/download_tensorflow-1.15.0-cp37-cp37m-linux_armv7l.sh"
$ ./download_tensorflow-1.15.0-cp37-cp37m-linux_armv7l.sh
$ sudo pip3 install tensorflow-1.15.0-cp37-cp37m-linux_armv7l.whl

import argparse
import numpy as np
import time

from PIL import Image

# Tensorflow -v1.12.0
#from tensorflow.contrib.lite.python import interpreter as interpreter_wrapper

# Tensorflow v1.13.0+, v2.x.x
from tensorflow.lite.python import interpreter as interpreter_wrapper

def load_labels(filename):
  my_labels = []
  input_file = open(filename, 'r')
  for l in input_file:
    my_labels.append(l.strip())
  return my_labels
if __name__ == "__main__":
  floating_model = False
  parser = argparse.ArgumentParser()
  parser.add_argument("-i", "--image", default="/tmp/grace_hopper.bmp", \
    help="image to be classified")
  parser.add_argument("-m", "--model_file", \
    default="/tmp/mobilenet_v1_1.0_224_quant.tflite", \
    help=".tflite model to be executed")
  parser.add_argument("-l", "--label_file", default="/tmp/labels.txt", \
    help="name of file containing labels")
  parser.add_argument("--input_mean", default=127.5, help="input_mean")
  parser.add_argument("--input_std", default=127.5, \
    help="input standard deviation")
  parser.add_argument("--num_threads", default=1, help="number of threads")
  args = parser.parse_args()

  ### Tensorflow -v2.2.0
  #interpreter = interpreter_wrapper.Interpreter(model_path=args.model_file)
  ### Tensorflow v2.3.0+
  interpreter = interpreter_wrapper.Interpreter(model_path=args.model_file, num_threads=int(args.num_threads))

  interpreter.allocate_tensors()
  input_details = interpreter.get_input_details()
  output_details = interpreter.get_output_details()
  # check the type of the input tensor
  if input_details[0]['dtype'] == np.float32:
    floating_model = True
  # NxHxWxC, H:1, W:2
  height = input_details[0]['shape'][1]
  width = input_details[0]['shape'][2]
  img = Image.open(args.image)
  img = img.resize((width, height))
  # add N dim
  input_data = np.expand_dims(img, axis=0)
  if floating_model:
    input_data = (np.float32(input_data) - args.input_mean) / args.input_std

  ### Tensorflow -v2.2.0
  #interpreter.set_num_threads(int(args.num_threads))
  interpreter.set_tensor(input_details[0]['index'], input_data)

  start_time = time.time()
  interpreter.invoke()
  stop_time = time.time()

  output_data = interpreter.get_tensor(output_details[0]['index'])
  results = np.squeeze(output_data)
  top_k = results.argsort()[-5:][::-1]
  labels = load_labels(args.label_file)
  for i in top_k:
    if floating_model:
      print('{0:08.6f}'.format(float(results[i]))+":", labels[i])
    else:
      print('{0:08.6f}'.format(float(results[i]/255.0))+":", labels[i])

  print("time: ", stop_time - start_time)

============================================================

Tensorflow v1.11.0

============================================================

Python2.x - Bazel 0.17.2

$ sudo apt-get install -y openmpi-bin libopenmpi-dev libhdf5-dev

$ cd ~
$ git clone https://github.com/tensorflow/tensorflow.git
$ cd tensorflow
$ git checkout -b v1.11.0
$ ./configure

Please specify the location of python. [Default is /usr/bin/python]:


Found possible Python library paths:
  /usr/local/lib/python2.7/dist-packages
  /usr/local/lib
  /home/pi/tensorflow/tensorflow/contrib/lite/tools/make/gen/rpi_armv7l/lib
  /usr/lib/python2.7/dist-packages
  /opt/movidius/caffe/python
Please input the desired Python library path to use.  Default is [/usr/local/lib/python2.7/dist-packages]

Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: y
No jemalloc as malloc support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: n
No Google Cloud Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: n
No Hadoop File System support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Amazon AWS Platform support? [Y/n]: n
No Amazon AWS Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Apache Kafka Platform support? [Y/n]: n
No Apache Kafka Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with XLA JIT support? [y/N]: n
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with GDR support? [y/N]: n
No GDR support will be enabled for TensorFlow.

Do you wish to build TensorFlow with VERBS support? [y/N]: n
No VERBS support will be enabled for TensorFlow.

Do you wish to build TensorFlow with nGraph support? [y/N]: n
No nGraph support will be enabled for TensorFlow.

Do you wish to