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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.
# ==============================================================================
"""Script to evaluate accuracy of TFLite flatbuffer model on mnist dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf # pylint: disable=g-bad-import-order
from tensorflow.contrib.lite.tutorials import dataset
flags = tf.app.flags
flags.DEFINE_string('data_dir', '/tmp/data_dir',
'Directory where data is stored.')
flags.DEFINE_string('model_file', '',
'The path to the TFLite flatbuffer model file.')
flags = flags.FLAGS
def test_image_generator():
# Generates an iterator over images
with tf.Session() as sess:
input_data = dataset.test(
flags.data_dir).make_one_shot_iterator().get_next()
try:
while True:
yield sess.run(input_data)
except tf.errors.OutOfRangeError:
pass
def run_eval(interpreter, input_image):
"""Performs evaluation for input image over specified model.
Args:
interpreter: TFLite interpreter initialized with model to execute.
input_image: Image input to the model.
Returns:
output: output tensor of model being executed.
"""
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Test model on the input images.
input_image = np.reshape(input_image, input_details[0]['shape'])
interpreter.set_tensor(input_details[0]['index'], input_image)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
output = np.squeeze(output_data)
return output
def main(_):
interpreter = tf.contrib.lite.Interpreter(model_path=flags.model_file)
interpreter.allocate_tensors()
num_correct, total = 0, 0
for input_data in test_image_generator():
output = run_eval(interpreter, input_data[0])
total += 1
if output == input_data[1]:
num_correct += 1
if total % 500 == 0:
print('Accuracy after %i images: %f' %
(total, float(num_correct) / float(total)))
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run(main)
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