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| """Tests for FSNS datasets module."""
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| import collections
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| import os
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| import tensorflow as tf
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| from tensorflow.contrib import slim
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| from datasets import fsns
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| from datasets import unittest_utils
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| from tensorflow.compat.v1 import flags
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| FLAGS = flags.FLAGS
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| def get_test_split():
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| config = fsns.DEFAULT_CONFIG.copy()
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| config['splits'] = {'test': {'size': 5, 'pattern': 'fsns-00000-of-00001'}}
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| return fsns.get_split('test', dataset_dir(), config)
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| def dataset_dir():
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| return os.path.join(os.path.dirname(__file__), 'testdata/fsns')
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| class FsnsTest(tf.test.TestCase):
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| def test_decodes_example_proto(self):
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| expected_label = range(37)
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| expected_image, encoded = unittest_utils.create_random_image(
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| 'PNG', shape=(150, 600, 3))
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| serialized = unittest_utils.create_serialized_example({
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| 'image/encoded': [encoded],
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| 'image/format': [b'PNG'],
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| 'image/class':
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| expected_label,
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| 'image/unpadded_class':
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| range(10),
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| 'image/text': [b'Raw text'],
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| 'image/orig_width': [150],
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| 'image/width': [600]
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| })
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| decoder = fsns.get_split('train', dataset_dir()).decoder
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| with self.test_session() as sess:
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| data_tuple = collections.namedtuple('DecodedData', decoder.list_items())
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| data = sess.run(data_tuple(*decoder.decode(serialized)))
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| self.assertAllEqual(expected_image, data.image)
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| self.assertAllEqual(expected_label, data.label)
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| self.assertEqual([b'Raw text'], data.text)
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| self.assertEqual([1], data.num_of_views)
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| def test_label_has_shape_defined(self):
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| serialized = 'fake'
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| decoder = fsns.get_split('train', dataset_dir()).decoder
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|
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| [label_tf] = decoder.decode(serialized, ['label'])
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| self.assertEqual(label_tf.get_shape().dims[0], 37)
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|
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| def test_dataset_tuple_has_all_extra_attributes(self):
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| dataset = fsns.get_split('train', dataset_dir())
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| self.assertTrue(dataset.charset)
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| self.assertTrue(dataset.num_char_classes)
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| self.assertTrue(dataset.num_of_views)
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| self.assertTrue(dataset.max_sequence_length)
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| self.assertTrue(dataset.null_code)
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| def test_can_use_the_test_data(self):
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| batch_size = 1
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| dataset = get_test_split()
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| provider = slim.dataset_data_provider.DatasetDataProvider(
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| dataset,
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| shuffle=True,
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| common_queue_capacity=2 * batch_size,
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| common_queue_min=batch_size)
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| image_tf, label_tf = provider.get(['image', 'label'])
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| with self.test_session() as sess:
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| sess.run(tf.compat.v1.global_variables_initializer())
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| with slim.queues.QueueRunners(sess):
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| image_np, label_np = sess.run([image_tf, label_tf])
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| self.assertEqual((150, 600, 3), image_np.shape)
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| self.assertEqual((37, ), label_np.shape)
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| if __name__ == '__main__':
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| tf.test.main()
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|
