eIQ Toolkit tflite-model format

[fzfhw]

I try to run an tflite-model but always get this error:

Traceback (most recent call last): File "label_image.py", line 133, in print('{:08.6f}: {}'.format(float(results[i]), labels[i])) TypeError: only size-1 arrays can be converted to Python scalars

It's the example code from the tensorflow-website with my own model. It seems lite the model is giving it's results in the wrong format.

Here is the code:

# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""label_image for tflite."""

import argparse
import time

import numpy as np
from PIL import Image
import tflite_runtime.interpreter as tflite
import sys

np.set_printoptions(threshold=sys.maxsize)

def load_labels(filename):
  with open(filename, 'r') as f:
    return [line.strip() for line in f.readlines()]


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument(
      '-i',
      '--image',
      default='Images/sh_17_11_23_s70m_0048.JPG',
      help='image to be classified')
  parser.add_argument(
      '-m',
      '--model_file',
      default='detection-balanced-mcu-2024-02-09T11-36-31.710Z_in-uint8.tflite',
      help='.tflite model to be executed')
  parser.add_argument(
      '-l',
      '--label_file',
      default='labels.txt',
      help='name of file containing labels')
  parser.add_argument(
      '--input_mean',
      default=127.5, type=float,
      help='input_mean')
  parser.add_argument(
      '--input_std',
      default=127.5, type=float,
      help='input standard deviation')
  parser.add_argument(
      '--num_threads', default=None, type=int, help='number of threads')
  parser.add_argument(
      '-e', '--ext_delegate', help='external_delegate_library path')
  parser.add_argument(
      '-o',
      '--ext_delegate_options',
      help='external delegate options, \
            format: "option1: value1; option2: value2"')

  args = parser.parse_args()

  ext_delegate = None
  ext_delegate_options = {}

  # parse extenal delegate options
  if args.ext_delegate_options is not None:
    options = args.ext_delegate_options.split(';')
    for o in options:
      kv = o.split(':')
      if (len(kv) == 2):
        ext_delegate_options[kv[0].strip()] = kv[1].strip()
      else:
        raise RuntimeError('Error parsing delegate option: ' + o)

  # load external delegate
  if args.ext_delegate is not None:
    print('Loading external delegate from {} with args: {}'.format(
        args.ext_delegate, ext_delegate_options))
    ext_delegate = [
        tflite.load_delegate(args.ext_delegate, ext_delegate_options)
    ]

  interpreter = tflite.Interpreter(
      model_path=args.model_file,
      experimental_delegates=ext_delegate,
      num_threads=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
  floating_model = input_details[0]['dtype'] == np.float32

  # NxHxWxC, H:1, W:2
  height = input_details[0]['shape'][1]
  width = input_details[0]['shape'][2]
  img = Image.open(args.image).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

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

  # ignore the 1st invoke
  startTime = time.time()
  interpreter.invoke()
  delta = time.time() - startTime
  print("Warm-up time:", '%.1f' % (delta * 1000), "ms\n")

  startTime = time.time()
  interpreter.invoke()
  delta = time.time() - startTime
  print("Inference time:", '%.1f' % (delta * 1000), "ms\n")

  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('{:08.6f}: {}'.format(float(results[i]), labels[i]))
    else:
      print('{:08.6f}: {}'.format(float(results[i] / 255.0), labels[i]))

For a test I created a tflite-model via the iIQ - Toolkit from NXP. I tried to set the output data type to "int8", "uint8" and "float32" but nothing worked.

Got anyone an idea beside creating a model on the "normal" way?

I uploaded my model.