def testInferSpinnThrowsErrorIfOnlyOneSentenceIsSpecified(self):
snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0")
self._create_test_data(snli_1_0_dir)
vocab = data.load_vocabulary(self._temp_data_dir)
word2index, embed = data.load_word_vectors(self._temp_data_dir, vocab)
config = _test_spinn_config(
data.WORD_VECTOR_LEN, 4,
logdir=os.path.join(self._temp_data_dir, "logdir"),
inference_sentences=("( foo ( bar . ) )", None))
with self.assertRaises(ValueError):
spinn.train_or_infer_spinn(embed, word2index, None, None, None, config)
def testInferSpinnThrowsErrorIfOnlyOneSentenceIsSpecified(self):
snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0")
self._create_test_data(snli_1_0_dir)
vocab = data.load_vocabulary(self._temp_data_dir)
word2index, embed = data.load_word_vectors(self._temp_data_dir, vocab)
config = _test_spinn_config(
data.WORD_VECTOR_LEN,
4,
logdir=os.path.join(self._temp_data_dir, "logdir"),
inference_sentences=("( foo ( bar . ) )", None))
with self.assertRaises(ValueError):
spinn.train_or_infer_spinn(embed, word2index, None, None, None,
config)
def testTrainSpinn(self):
"""Test with fake toy SNLI data and GloVe vectors."""
# 1. Create and load a fake SNLI data file and a fake GloVe embedding file.
snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0")
fake_train_file = self._create_test_data(snli_1_0_dir)
vocab = data.load_vocabulary(self._temp_data_dir)
word2index, embed = data.load_word_vectors(self._temp_data_dir, vocab)
train_data = data.SnliData(fake_train_file, word2index)
dev_data = data.SnliData(fake_train_file, word2index)
test_data = data.SnliData(fake_train_file, word2index)
# 2. Create a fake config.
config = _test_spinn_config(data.WORD_VECTOR_LEN,
4,
logdir=os.path.join(
self._temp_data_dir, "logdir"))
# 3. Test training of a SPINN model.
trainer = spinn.train_or_infer_spinn(embed, word2index, train_data,
dev_data, test_data, config)
# 4. Load train loss values from the summary files and verify that they
# decrease with training.
summary_file = glob.glob(os.path.join(config.logdir,
"events.out.*"))[0]
events = summary_test_util.events_from_file(summary_file)
train_losses = [
event.summary.value[0].simple_value for event in events if
event.summary.value and event.summary.value[0].tag == "train/loss"
]
self.assertEqual(config.epochs, len(train_losses))
# 5. Verify that checkpoints exist and contains all the expected variables.
self.assertTrue(glob.glob(os.path.join(config.logdir, "ckpt*")))
object_graph_string = checkpoint_utils.load_variable(
config.logdir, name="_CHECKPOINTABLE_OBJECT_GRAPH")
object_graph = checkpointable_object_graph_pb2.CheckpointableObjectGraph(
)
object_graph.ParseFromString(object_graph_string)
ckpt_variable_names = set()
for node in object_graph.nodes:
for attribute in node.attributes:
ckpt_variable_names.add(attribute.full_name)
self.assertIn("global_step", ckpt_variable_names)
for v in trainer.variables:
variable_name = v.name[:v.name.
index(":")] if ":" in v.name else v.name
self.assertIn(variable_name, ckpt_variable_names)
def testInferSpinnWorks(self):
"""Test inference with the spinn model."""
snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0")
self._create_test_data(snli_1_0_dir)
vocab = data.load_vocabulary(self._temp_data_dir)
word2index, embed = data.load_word_vectors(self._temp_data_dir, vocab)
config = _test_spinn_config(
data.WORD_VECTOR_LEN, 4,
logdir=os.path.join(self._temp_data_dir, "logdir"),
inference_sentences=("( foo ( bar . ) )", "( bar ( foo . ) )"))
logits = spinn.train_or_infer_spinn(
embed, word2index, None, None, None, config)
self.assertEqual(tf.float32, logits.dtype)
self.assertEqual((3,), logits.shape)
def testTrainSpinn(self):
"""Test with fake toy SNLI data and GloVe vectors."""
# 1. Create and load a fake SNLI data file and a fake GloVe embedding file.
snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0")
fake_train_file = self._create_test_data(snli_1_0_dir)
vocab = data.load_vocabulary(self._temp_data_dir)
word2index, embed = data.load_word_vectors(self._temp_data_dir, vocab)
train_data = data.SnliData(fake_train_file, word2index)
dev_data = data.SnliData(fake_train_file, word2index)
test_data = data.SnliData(fake_train_file, word2index)
# 2. Create a fake config.
config = _test_spinn_config(
data.WORD_VECTOR_LEN, 4,
logdir=os.path.join(self._temp_data_dir, "logdir"))
# 3. Test training of a SPINN model.
trainer = spinn.train_or_infer_spinn(
embed, word2index, train_data, dev_data, test_data, config)
# 4. Load train loss values from the summary files and verify that they
# decrease with training.
summary_file = glob.glob(os.path.join(config.logdir, "events.out.*"))[0]
events = summary_test_util.events_from_file(summary_file)
train_losses = [event.summary.value[0].simple_value for event in events
if event.summary.value
and event.summary.value[0].tag == "train/loss"]
self.assertEqual(config.epochs, len(train_losses))
# 5. Verify that checkpoints exist and contains all the expected variables.
self.assertTrue(glob.glob(os.path.join(config.logdir, "ckpt*")))
object_graph_string = checkpoint_utils.load_variable(
config.logdir, name="_CHECKPOINTABLE_OBJECT_GRAPH")
object_graph = checkpointable_object_graph_pb2.CheckpointableObjectGraph()
object_graph.ParseFromString(object_graph_string)
ckpt_variable_names = set()
for node in object_graph.nodes:
for attribute in node.attributes:
ckpt_variable_names.add(attribute.full_name)
self.assertIn("global_step", ckpt_variable_names)
for v in trainer.variables:
variable_name = v.name[:v.name.index(":")] if ":" in v.name else v.name
self.assertIn(variable_name, ckpt_variable_names)