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 argparseimport timeimport numpy as npfrom PIL import Imageimport tflite_runtime.interpreter as tfliteimport sysnp.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?