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# Copyright 2015 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.
# ==============================================================================
# pylint: disable=g-bad-import-order,unused-import
"""Tests the graph freezing tool."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import os
from tensorflow.examples.image_retraining import retrain
from tensorflow.python.framework import test_util
class ImageRetrainingTest(test_util.TensorFlowTestCase):
def dummyImageLists(self):
return {'label_one': {'dir': 'somedir', 'training': ['image_one.jpg',
'image_two.jpg'],
'testing': ['image_three.jpg', 'image_four.jpg'],
'validation': ['image_five.jpg', 'image_six.jpg']},
'label_two': {'dir': 'otherdir', 'training': ['image_one.jpg',
'image_two.jpg'],
'testing': ['image_three.jpg', 'image_four.jpg'],
'validation': ['image_five.jpg', 'image_six.jpg']}}
def testGetImagePath(self):
image_lists = self.dummyImageLists()
self.assertEqual('image_dir/somedir/image_one.jpg', retrain.get_image_path(
image_lists, 'label_one', 0, 'image_dir', 'training'))
self.assertEqual('image_dir/otherdir/image_four.jpg',
retrain.get_image_path(image_lists, 'label_two', 1,
'image_dir', 'testing'))
def testGetBottleneckPath(self):
image_lists = self.dummyImageLists()
self.assertEqual('bottleneck_dir/somedir/image_five.jpg_imagenet_v3.txt',
retrain.get_bottleneck_path(
image_lists, 'label_one', 0, 'bottleneck_dir',
'validation', 'imagenet_v3'))
def testShouldDistortImage(self):
self.assertEqual(False, retrain.should_distort_images(False, 0, 0, 0))
self.assertEqual(True, retrain.should_distort_images(True, 0, 0, 0))
self.assertEqual(True, retrain.should_distort_images(False, 10, 0, 0))
self.assertEqual(True, retrain.should_distort_images(False, 0, 1, 0))
self.assertEqual(True, retrain.should_distort_images(False, 0, 0, 50))
def testAddInputDistortions(self):
with tf.Graph().as_default():
with tf.Session() as sess:
retrain.add_input_distortions(True, 10, 10, 10, 299, 299, 3, 128, 128)
self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortJPGInput:0'))
self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortResult:0'))
@tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01)
def testAddFinalTrainingOps(self, flags_mock):
with tf.Graph().as_default():
with tf.Session() as sess:
bottleneck = tf.placeholder(
tf.float32, [1, 1024],
name='bottleneck')
retrain.add_final_training_ops(5, 'final', bottleneck, 1024)
self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0'))
def testAddEvaluationStep(self):
with tf.Graph().as_default():
final = tf.placeholder(tf.float32, [1], name='final')
gt = tf.placeholder(tf.float32, [1], name='gt')
self.assertIsNotNone(retrain.add_evaluation_step(final, gt))
def testAddJpegDecoding(self):
with tf.Graph().as_default():
jpeg_data, mul_image = retrain.add_jpeg_decoding(10, 10, 3, 0, 255)
self.assertIsNotNone(jpeg_data)
self.assertIsNotNone(mul_image)
def testCreateModelInfo(self):
did_raise_value_error = False
try:
retrain.create_model_info('no_such_model_name')
except ValueError:
did_raise_value_error = True
self.assertTrue(did_raise_value_error)
model_info = retrain.create_model_info('inception_v3')
self.assertIsNotNone(model_info)
self.assertEqual(299, model_info['input_width'])
if __name__ == '__main__':
tf.test.main()
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