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March 13, 2020 01:24
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2020年3月13日:可正常在Jetson Nano上執行的Tensorflow Lite官方範例
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# 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.""" | |
from __future__ import absolute_import | |
from __future__ import division | |
from __future__ import print_function | |
import argparse | |
import numpy as np | |
from PIL import Image | |
import tflite_runtime.interpreter as tflite #引入tflite模組 | |
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='/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, type=float, | |
help='input_mean') | |
parser.add_argument( | |
'--input_std', | |
default=127.5, type=float, | |
help='input standard deviation') | |
args = parser.parse_args() | |
interpreter = tflite.Interpreter(model_path=args.model_file) | |
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) | |
interpreter.invoke() | |
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])) |
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