def main(_):
config = FLAGS
# Load embedding vectors.
vocab = data.load_vocabulary(FLAGS.data_root)
word2index, embed = data.load_word_vectors(FLAGS.data_root, vocab)
if not (config.inference_premise or config.inference_hypothesis):
print("Loading train, dev and test data...")
train_data = data.SnliData(
os.path.join(FLAGS.data_root, "snli/snli_1.0/snli_1.0_train.txt"),
word2index, sentence_len_limit=FLAGS.sentence_len_limit)
dev_data = data.SnliData(
os.path.join(FLAGS.data_root, "snli/snli_1.0/snli_1.0_dev.txt"),
word2index, sentence_len_limit=FLAGS.sentence_len_limit)
test_data = data.SnliData(
os.path.join(FLAGS.data_root, "snli/snli_1.0/snli_1.0_test.txt"),
word2index, sentence_len_limit=FLAGS.sentence_len_limit)
else:
train_data = None
dev_data = None
test_data = None
train_or_infer_spinn(
embed, word2index, train_data, dev_data, test_data, 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 testSnliData(self):
snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0")
fake_train_file = os.path.join(snli_1_0_dir, "snli_1.0_train.txt")
os.makedirs(snli_1_0_dir)
self._createFakeSnliData(fake_train_file)
glove_dir = os.path.join(self._temp_data_dir, "glove")
os.makedirs(glove_dir)
glove_file = os.path.join(glove_dir, "glove.42B.300d.txt")
self._createFakeGloveData(glove_file)
vocab = data.load_vocabulary(self._temp_data_dir)
word2index, _ = data.load_word_vectors(self._temp_data_dir, vocab)
train_data = data.SnliData(fake_train_file, word2index)
self.assertEqual(4, train_data.num_batches(1))
self.assertEqual(2, train_data.num_batches(2))
self.assertEqual(2, train_data.num_batches(3))
self.assertEqual(1, train_data.num_batches(4))
generator = train_data.get_generator(2)()
for _ in range(2):
label, prem, prem_trans, hypo, hypo_trans = next(generator)
self.assertEqual(2, len(label))
self.assertEqual((4, 2), prem.shape)
self.assertEqual((5, 2), prem_trans.shape)
self.assertEqual((3, 2), hypo.shape)
self.assertEqual((3, 2), hypo_trans.shape)
def main(_):
config = FLAGS
# Load embedding vectors.
vocab = data.load_vocabulary(FLAGS.data_root)
word2index, embed = data.load_word_vectors(FLAGS.data_root, vocab)
print("Loading train, dev and test data...")
train_data = data.SnliData(os.path.join(
FLAGS.data_root, "snli/snli_1.0/snli_1.0_train.txt"),
word2index,
sentence_len_limit=FLAGS.sentence_len_limit)
dev_data = data.SnliData(os.path.join(FLAGS.data_root,
"snli/snli_1.0/snli_1.0_dev.txt"),
word2index,
sentence_len_limit=FLAGS.sentence_len_limit)
test_data = data.SnliData(os.path.join(FLAGS.data_root,
"snli/snli_1.0/snli_1.0_test.txt"),
word2index,
sentence_len_limit=FLAGS.sentence_len_limit)
train_spinn(embed, train_data, dev_data, test_data, 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 = os.path.join(snli_1_0_dir, "snli_1.0_train.txt")
os.makedirs(snli_1_0_dir)
# Four sentences in total.
with open(fake_train_file, "wt") as f:
f.write(
"gold_label\tsentence1_binary_parse\tsentence2_binary_parse\t"
"sentence1_parse\tsentence2_parse\tsentence1\tsentence2\t"
"captionID\tpairID\tlabel1\tlabel2\tlabel3\tlabel4\tlabel5\n")
f.write("neutral\t( ( Foo bar ) . )\t( ( foo . )\t"
"DummySentence1Parse\tDummySentence2Parse\t"
"Foo bar.\tfoo baz.\t"
"4705552913.jpg#2\t4705552913.jpg#2r1n\t"
"neutral\tentailment\tneutral\tneutral\tneutral\n")
f.write("contradiction\t( ( Bar foo ) . )\t( ( baz . )\t"
"DummySentence1Parse\tDummySentence2Parse\t"
"Foo bar.\tfoo baz.\t"
"4705552913.jpg#2\t4705552913.jpg#2r1n\t"
"neutral\tentailment\tneutral\tneutral\tneutral\n")
f.write("entailment\t( ( Quux quuz ) . )\t( ( grault . )\t"
"DummySentence1Parse\tDummySentence2Parse\t"
"Foo bar.\tfoo baz.\t"
"4705552913.jpg#2\t4705552913.jpg#2r1n\t"
"neutral\tentailment\tneutral\tneutral\tneutral\n")
f.write("entailment\t( ( Quuz quux ) . )\t( ( garply . )\t"
"DummySentence1Parse\tDummySentence2Parse\t"
"Foo bar.\tfoo baz.\t"
"4705552913.jpg#2\t4705552913.jpg#2r1n\t"
"neutral\tentailment\tneutral\tneutral\tneutral\n")
glove_dir = os.path.join(self._temp_data_dir, "glove")
os.makedirs(glove_dir)
glove_file = os.path.join(glove_dir, "glove.42B.300d.txt")
words = [".", "foo", "bar", "baz", "quux", "quuz", "grault", "garply"]
with open(glove_file, "wt") as f:
for i, word in enumerate(words):
f.write("%s " % word)
for j in range(data.WORD_VECTOR_LEN):
f.write("%.5f" % (i * 0.1))
if j < data.WORD_VECTOR_LEN - 1:
f.write(" ")
else:
f.write("\n")
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)
print(embed)
# 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.
spinn.train_spinn(embed, 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))
self.assertLess(train_losses[-1], train_losses[0])
def testSnliData(self):
"""Unit test for SnliData objects."""
snli_1_0_dir = os.path.join(self._temp_data_dir, "snli/snli_1.0")
fake_train_file = os.path.join(snli_1_0_dir, "snli_1.0_train.txt")
os.makedirs(snli_1_0_dir)
# Four sentences in total.
with open(fake_train_file, "wt") as f:
f.write("gold_label\tsentence1_binary_parse\tsentence2_binary_parse\t"
"sentence1_parse\tsentence2_parse\tsentence1\tsentence2\t"
"captionID\tpairID\tlabel1\tlabel2\tlabel3\tlabel4\tlabel5\n")
f.write("neutral\t( ( Foo bar ) . )\t( ( foo . )\t"
"DummySentence1Parse\tDummySentence2Parse\t"
"Foo bar.\tfoo baz.\t"
"4705552913.jpg#2\t4705552913.jpg#2r1n\t"
"neutral\tentailment\tneutral\tneutral\tneutral\n")
f.write("contradiction\t( ( Bar foo ) . )\t( ( baz . )\t"
"DummySentence1Parse\tDummySentence2Parse\t"
"Foo bar.\tfoo baz.\t"
"4705552913.jpg#2\t4705552913.jpg#2r1n\t"
"neutral\tentailment\tneutral\tneutral\tneutral\n")
f.write("entailment\t( ( Quux quuz ) . )\t( ( grault . )\t"
"DummySentence1Parse\tDummySentence2Parse\t"
"Foo bar.\tfoo baz.\t"
"4705552913.jpg#2\t4705552913.jpg#2r1n\t"
"neutral\tentailment\tneutral\tneutral\tneutral\n")
f.write("entailment\t( ( Quuz quux ) . )\t( ( garply . )\t"
"DummySentence1Parse\tDummySentence2Parse\t"
"Foo bar.\tfoo baz.\t"
"4705552913.jpg#2\t4705552913.jpg#2r1n\t"
"neutral\tentailment\tneutral\tneutral\tneutral\n")
glove_dir = os.path.join(self._temp_data_dir, "glove")
os.makedirs(glove_dir)
glove_file = os.path.join(glove_dir, "glove.42B.300d.txt")
words = [".", "foo", "bar", "baz", "quux", "quuz", "grault", "garply"]
with open(glove_file, "wt") as f:
for i, word in enumerate(words):
f.write("%s " % word)
for j in range(data.WORD_VECTOR_LEN):
f.write("%.5f" % (i * 0.1))
if j < data.WORD_VECTOR_LEN - 1:
f.write(" ")
else:
f.write("\n")
vocab = data.load_vocabulary(self._temp_data_dir)
word2index, _ = data.load_word_vectors(self._temp_data_dir, vocab)
train_data = data.SnliData(fake_train_file, word2index)
self.assertEqual(4, train_data.num_batches(1))
self.assertEqual(2, train_data.num_batches(2))
self.assertEqual(2, train_data.num_batches(3))
self.assertEqual(1, train_data.num_batches(4))
generator = train_data.get_generator(2)()
for i in range(2):
label, prem, prem_trans, hypo, hypo_trans = next(generator)
self.assertEqual(2, len(label))
self.assertEqual((4, 2), prem.shape)
self.assertEqual((5, 2), prem_trans.shape)
self.assertEqual((3, 2), hypo.shape)
self.assertEqual((3, 2), hypo_trans.shape)
