87663eecd0
This is mostly the result of an internal cleanup and formatting pass. PiperOrigin-RevId: 286480567 Change-Id: I618a77590ff607edbc93fc503190701b490aa0ad
89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Script to evaluate accuracy of TFLite flatbuffer model on mnist dataset."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import tensorflow as tf # pylint: disable=g-bad-import-order
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from tensorflow.lite.tutorials import dataset
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flags = tf.app.flags
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flags.DEFINE_string('data_dir', '/tmp/data_dir',
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'Directory where data is stored.')
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flags.DEFINE_string('model_file', '',
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'The path to the TFLite flatbuffer model file.')
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flags = flags.FLAGS
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def test_image_generator():
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# Generates an iterator over images
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with tf.compat.v1.Session() as sess:
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input_data = tf.compat.v1.data.make_one_shot_iterator(dataset.test(
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flags.data_dir)).get_next()
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try:
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while True:
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yield sess.run(input_data)
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except tf.errors.OutOfRangeError:
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pass
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def run_eval(interpreter, input_image):
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"""Performs evaluation for input image over specified model.
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Args:
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interpreter: TFLite interpreter initialized with model to execute.
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input_image: Image input to the model.
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Returns:
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output: output tensor of model being executed.
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"""
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# Get input and output tensors.
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input_details = interpreter.get_input_details()
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output_details = interpreter.get_output_details()
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# Test model on the input images.
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input_image = np.reshape(input_image, input_details[0]['shape'])
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interpreter.set_tensor(input_details[0]['index'], input_image)
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interpreter.invoke()
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output_data = interpreter.get_tensor(output_details[0]['index'])
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output = np.squeeze(output_data)
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return output
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def main(_):
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interpreter = tf.lite.Interpreter(model_path=flags.model_file)
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interpreter.allocate_tensors()
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num_correct, total = 0, 0
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for input_data in test_image_generator():
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output = run_eval(interpreter, input_data[0])
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total += 1
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if output == input_data[1]:
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num_correct += 1
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if total % 500 == 0:
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print('Accuracy after %i images: %f' %
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(total, float(num_correct) / float(total)))
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if __name__ == '__main__':
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tf.logging.set_verbosity(tf.logging.INFO)
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tf.compat.v1.app.run(main)
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