def _makeDataset(self, inputter, data_file, metadata=None, dataset_size=1, shapes=None):
if metadata is not None:
inputter.initialize(metadata)
self.assertEqual(dataset_size, inputter.get_dataset_size(data_file))
dataset = inputter.make_dataset(data_file)
dataset = dataset.map(lambda *arg: inputter.process(item_or_tuple(arg)))
dataset = dataset.padded_batch(1, padded_shapes=data.get_padded_shapes(dataset))
iterator = dataset.make_initializable_iterator()
tf.add_to_collection(tf.GraphKeys.TABLE_INITIALIZERS, iterator.initializer)
next_element = iterator.get_next()
if shapes is not None:
all_features = [next_element]
if not inputter.is_target:
all_features.append(inputter.get_serving_input_receiver().features)
else:
with self.assertRaises(ValueError):
_ = inputter.get_serving_input_receiver()
for features in all_features:
self.assertNotIn("raw", features)
for field, shape in six.iteritems(shapes):
self.assertIn(field, features)
self.assertAllEqual(shape, features[field].get_shape().as_list())
transformed = inputter.transform_data(next_element)
return next_element, transformed
def testSequenceRecord(self):
vector = np.array([[0.2, 0.3], [0.4, 0.5]], dtype=np.float32)
writer = tf.python_io.TFRecordWriter(record_file)
record_inputter.write_sequence_record(vector, writer)
writer.close()
inputter = record_inputter.SequenceRecordInputter()
data, transformed = _first_element(inputter, record_file)
input_receiver = inputter.get_serving_input_receiver()
self.assertIn("length", data)
self.assertIn("tensor", data)
self.assertIn("length", input_receiver.features)
self.assertIn("tensor", input_receiver.features)
self.assertAllEqual([None, None, 2], transformed.get_shape().as_list())
self.assertAllEqual([None, None, 2], input_receiver.features["tensor"].get_shape().as_list())
with self.test_session() as sess:
sess.run(tf.tables_initializer())
data, transformed = sess.run([data, transformed])
self.assertEqual(2, data["length"])
self.assertAllEqual(vector, data["tensor"])
self.assertAllEqual([vector], transformed)
def _makeDataset(self, inputter, data_file, metadata=None, dataset_size=1, shapes=None):
if metadata is not None:
inputter.initialize(metadata)
self.assertEqual(dataset_size, inputter.get_dataset_size(data_file))
dataset = inputter.make_dataset(data_file)
dataset = dataset.map(lambda *arg: inputter.process(item_or_tuple(arg)))
dataset = dataset.padded_batch(1, padded_shapes=data.get_padded_shapes(dataset))
if compat.is_tf2():
iterator = None
features = iter(dataset).next()
else:
iterator = dataset.make_initializable_iterator()
features = iterator.get_next()
if shapes is not None:
all_features = [features]
if not compat.is_tf2() and not inputter.is_target:
all_features.append(inputter.get_serving_input_receiver().features)
for f in all_features:
for field, shape in six.iteritems(shapes):
self.assertIn(field, f)
self.assertTrue(f[field].shape.is_compatible_with(shape))
inputs = inputter.make_inputs(features, training=True)
if not compat.is_tf2():
with self.test_session() as sess:
sess.run(tf.tables_initializer())
sess.run(tf.global_variables_initializer())
sess.run(iterator.initializer)
return self.evaluate((features, inputs))
