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# Copyright 2019 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.
# ==============================================================================
"""Test a tflite model using random input data."""
from __future__ import print_function
from absl import flags
import numpy as np
import tensorflow.compat.v1 as tf
flags.DEFINE_string('model_path', None, 'Path to model.')
FLAGS = flags.FLAGS
def main(_):
flags.mark_flag_as_required('model_path')
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=FLAGS.model_path)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
print('input_details:', input_details)
output_details = interpreter.get_output_details()
print('output_details:', output_details)
# Test model on random input data.
input_shape = input_details[0]['shape']
# change the following line to feed into your own data.
input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)
if __name__ == '__main__':
tf.app.run()
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