def test_created_example_has_correct_values(self):
example_serialized = unittest_utils.create_serialized_example({
'labels': [1, 2, 3],
'data': ['FAKE']
})
example = tf.train.Example()
example.ParseFromString(example_serialized)
self.assertProtoEquals(
"""
features {
feature {
key: "labels"
value { int64_list {
value: 1
value: 2
value: 3
}}
}
feature {
key: "data"
value { bytes_list {
value: "FAKE"
}}
}
}
""", example)
def test_decodes_example_proto(self):
expected_label = range(37)
expected_image, encoded = unittest_utils.create_random_image(
'PNG', shape=(150, 600, 3))
serialized = unittest_utils.create_serialized_example({
'image/encoded': [encoded],
'image/format': ['PNG'],
'image/class':
expected_label,
'image/unpadded_class':
range(10),
'image/text': ['Raw text'],
'image/orig_width': [150],
'image/width': [600]
})
decoder = fsns.get_split('train', dataset_dir()).decoder
with self.test_session() as sess:
data_tuple = collections.namedtuple('DecodedData',
decoder.list_items())
data = sess.run(data_tuple(*decoder.decode(serialized)))
self.assertAllEqual(expected_image, data.image)
self.assertAllEqual(expected_label, data.label)
self.assertEqual(['Raw text'], data.text)
self.assertEqual([1], data.num_of_views)
def test_created_example_has_correct_values(self):
example_serialized = unittest_utils.create_serialized_example({
'labels': [1, 2, 3],
'data': ['FAKE']
})
example = tf.train.Example()
example.ParseFromString(example_serialized)
self.assertProtoEquals("""
features {
feature {
key: "labels"
value { int64_list {
value: 1
value: 2
value: 3
}}
}
feature {
key: "data"
value { bytes_list {
value: "FAKE"
}}
}
}
""", example)
def test_decodes_example_proto(self):
expected_label = range(37)
expected_image, encoded = unittest_utils.create_random_image(
'PNG', shape=(150, 600, 3))
serialized = unittest_utils.create_serialized_example({
'image/encoded': [encoded],
'image/format': ['PNG'],
'image/class':
expected_label,
'image/unpadded_class':
range(10),
'image/text': ['Raw text'],
'image/orig_width': [150],
'image/width': [600]
})
decoder = fsns.get_split('train', dataset_dir()).decoder
with self.test_session() as sess:
data_tuple = collections.namedtuple('DecodedData', decoder.list_items())
data = sess.run(data_tuple(*decoder.decode(serialized)))
self.assertAllEqual(expected_image, data.image)
self.assertAllEqual(expected_label, data.label)
self.assertEqual(['Raw text'], data.text)
self.assertEqual([1], data.num_of_views)