def test_build_tf_record_input_reader(self):
tf_record_path = self.create_tf_record()
input_reader_text_proto = """
shuffle: false
num_readers: 1
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
tensor_dict = dataset_builder.make_initializable_iterator(
dataset_builder.build(input_reader_proto, batch_size=1)).get_next()
sv = tf.train.Supervisor(logdir=self.get_temp_dir())
with sv.prepare_or_wait_for_session() as sess:
sv.start_queue_runners(sess)
output_dict = sess.run(tensor_dict)
self.assertTrue(
fields.InputDataFields.groundtruth_instance_masks not in output_dict)
self.assertEquals((1, 4, 5, 3),
output_dict[fields.InputDataFields.image].shape)
self.assertAllEqual([[2]],
output_dict[fields.InputDataFields.groundtruth_classes])
self.assertEquals(
(1, 1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
self.assertAllEqual(
[0.0, 0.0, 1.0, 1.0],
output_dict[fields.InputDataFields.groundtruth_boxes][0][0])
def test_build_tf_record_input_reader_with_batch_size_two_and_masks(self):
tf_record_path = self.create_tf_record()
input_reader_text_proto = """
shuffle: false
num_readers: 1
load_instance_masks: true
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
def one_hot_class_encoding_fn(tensor_dict):
tensor_dict[fields.InputDataFields.groundtruth_classes] = tf.one_hot(
tensor_dict[fields.InputDataFields.groundtruth_classes] - 1, depth=3)
return tensor_dict
tensor_dict = dataset_builder.make_initializable_iterator(
dataset_builder.build(
input_reader_proto,
transform_input_data_fn=one_hot_class_encoding_fn,
batch_size=2)).get_next()
sv = tf.train.Supervisor(logdir=self.get_temp_dir())
with sv.prepare_or_wait_for_session() as sess:
sv.start_queue_runners(sess)
output_dict = sess.run(tensor_dict)
self.assertAllEqual(
[2, 1, 4, 5],
output_dict[fields.InputDataFields.groundtruth_instance_masks].shape)
def test_make_initializable_iterator_with_hashTable(self):
keys = [1, 0, -1]
dataset = tf.data.Dataset.from_tensor_slices([[1, 2, -1, 5]])
table = tf.contrib.lookup.HashTable(
initializer=tf.contrib.lookup.KeyValueTensorInitializer(
keys=keys,
values=list(reversed(keys))),
default_value=100)
dataset = dataset.map(table.lookup)
data = dataset_builder.make_initializable_iterator(dataset).get_next()
init = tf.tables_initializer()
with self.test_session() as sess:
sess.run(init)
self.assertAllEqual(sess.run(data), [-1, 100, 1, 100])
def get_next(config):
return dataset_builder.make_initializable_iterator(
dataset_builder.build(config)).get_next()
def get_next(config):
return dataset_builder.make_initializable_iterator(dataset_builder.build(config)).get_next()
def graph_fn():
return dataset_builder.make_initializable_iterator(
dataset_builder.build(
input_reader_proto,
transform_input_data_fn=one_hot_class_encoding_fn,
batch_size=2)).get_next()
