def testDecodeExampleWithBranchedLookup(self):
example = example_pb2.Example(features=feature_pb2.Features(
feature={
'image/object/class/text':
self._BytesFeatureFromList(
np.array(['cat', 'dog', 'guinea pig'])),
}))
serialized_example = example.SerializeToString()
# 'dog' -> 0, 'guinea pig' -> 1, 'cat' -> 2
table = lookup_ops.index_table_from_tensor(
constant_op.constant(['dog', 'guinea pig', 'cat']))
with self.test_session() as sess:
sess.run(lookup_ops.tables_initializer())
serialized_example = array_ops.reshape(serialized_example,
shape=[])
keys_to_features = {
'image/object/class/text':
parsing_ops.VarLenFeature(dtypes.string),
}
items_to_handlers = {
'labels':
tf_example_decoder.LookupTensor('image/object/class/text',
table),
}
decoder = slim_example_decoder.TFExampleDecoder(
keys_to_features, items_to_handlers)
obtained_class_ids = decoder.decode(serialized_example)[0].eval()
self.assertAllClose([2, 0, 1], obtained_class_ids)
def testDecodeExampleWithBranchedBackupHandler(self):
example1 = example_pb2.Example(
features=feature_pb2.Features(
feature={
'image/object/class/text':
self._BytesFeatureFromList(
np.array(['cat', 'dog', 'guinea pig'])),
'image/object/class/label':
self._Int64FeatureFromList(np.array([42, 10, 900]))
}))
example2 = example_pb2.Example(
features=feature_pb2.Features(
feature={
'image/object/class/text':
self._BytesFeatureFromList(
np.array(['cat', 'dog', 'guinea pig'])),
}))
example3 = example_pb2.Example(
features=feature_pb2.Features(
feature={
'image/object/class/label':
self._Int64FeatureFromList(np.array([42, 10, 901]))
}))
# 'dog' -> 0, 'guinea pig' -> 1, 'cat' -> 2
table = lookup_ops.index_table_from_tensor(
constant_op.constant(['dog', 'guinea pig', 'cat']))
keys_to_features = {
'image/object/class/text': parsing_ops.VarLenFeature(dtypes.string),
'image/object/class/label': parsing_ops.VarLenFeature(dtypes.int64),
}
backup_handler = tf_example_decoder.BackupHandler(
handler=slim_example_decoder.Tensor('image/object/class/label'),
backup=tf_example_decoder.LookupTensor('image/object/class/text',
table))
items_to_handlers = {
'labels': backup_handler,
}
decoder = slim_example_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
obtained_class_ids_each_example = []
with self.test_session() as sess:
sess.run(lookup_ops.tables_initializer())
for example in [example1, example2, example3]:
serialized_example = array_ops.reshape(
example.SerializeToString(), shape=[])
obtained_class_ids_each_example.append(
decoder.decode(serialized_example)[0].eval())
self.assertAllClose([42, 10, 900], obtained_class_ids_each_example[0])
self.assertAllClose([2, 0, 1], obtained_class_ids_each_example[1])
self.assertAllClose([42, 10, 901], obtained_class_ids_each_example[2])