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# Lint as: python2, python3
# 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.
# ==============================================================================
"""Tests for deeplab.datasets.data_generator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from six.moves import range
import tensorflow as tf
from deeplab import common
from deeplab.datasets import data_generator
ImageAttributes = collections.namedtuple(
'ImageAttributes', ['image', 'label', 'height', 'width', 'image_name'])
class DatasetTest(tf.test.TestCase):
# Note: training dataset cannot be tested since there is shuffle operation.
# When disabling the shuffle, training dataset is operated same as validation
# dataset. Therefore it is not tested again.
def testPascalVocSegTestData(self):
dataset = data_generator.Dataset(
dataset_name='pascal_voc_seg',
split_name='val',
dataset_dir=
'deeplab/testing/pascal_voc_seg',
batch_size=1,
crop_size=[3, 3], # Use small size for testing.
min_resize_value=3,
max_resize_value=3,
resize_factor=None,
min_scale_factor=0.01,
max_scale_factor=2.0,
scale_factor_step_size=0.25,
is_training=False,
model_variant='mobilenet_v2')
self.assertAllEqual(dataset.num_of_classes, 21)
self.assertAllEqual(dataset.ignore_label, 255)
num_of_images = 3
with self.test_session() as sess:
iterator = dataset.get_one_shot_iterator()
for i in range(num_of_images):
batch = iterator.get_next()
batch, = sess.run([batch])
image_attributes = _get_attributes_of_image(i)
self.assertEqual(batch[common.HEIGHT][0], image_attributes.height)
self.assertEqual(batch[common.WIDTH][0], image_attributes.width)
self.assertEqual(batch[common.IMAGE_NAME][0],
image_attributes.image_name.encode())
# All data have been read.
with self.assertRaisesRegexp(tf.errors.OutOfRangeError, ''):
sess.run([iterator.get_next()])
def _get_attributes_of_image(index):
"""Gets the attributes of the image.
Args:
index: Index of image in all images.
Returns:
Attributes of the image in the format of ImageAttributes.
Raises:
ValueError: If index is of wrong value.
"""
if index == 0:
return ImageAttributes(
image=None,
label=None,
height=366,
width=500,
image_name='2007_000033')
elif index == 1:
return ImageAttributes(
image=None,
label=None,
height=335,
width=500,
image_name='2007_000042')
elif index == 2:
return ImageAttributes(
image=None,
label=None,
height=333,
width=500,
image_name='2007_000061')
else:
raise ValueError('Index can only be 0, 1 or 2.')
if __name__ == '__main__':
tf.test.main()
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