def start_bundle(self):
convert_impl = ConverterImplType(self._convert_impl_value)
if convert_impl == ConverterImplType.PYTHON:
self._converter = tf_example_utils.ToClassifierTensorflowExample(
self._config)
else:
raise ValueError(f'Unsupported implementation: {convert_impl.name}')
def test_convert(self):
max_seq_length = 12
with tempfile.TemporaryDirectory() as input_dir:
vocab_file = os.path.join(input_dir, 'vocab.txt')
_create_vocab(vocab_file, range(10))
converter = tf_example_utils.ToClassifierTensorflowExample(
config=tf_example_utils.ClassifierConversionConfig(
vocab_file=vocab_file,
max_seq_length=max_seq_length,
max_column_id=max_seq_length,
max_row_id=max_seq_length,
strip_column_names=False,
add_aggregation_candidates=False,
))
interaction = interaction_pb2.Interaction(
table=interaction_pb2.Table(
columns=[
interaction_pb2.Cell(text='A'),
interaction_pb2.Cell(text='B'),
interaction_pb2.Cell(text='C'),
],
rows=[
interaction_pb2.Cells(cells=[
interaction_pb2.Cell(text='0'),
interaction_pb2.Cell(text='4'),
interaction_pb2.Cell(text='5'),
]),
interaction_pb2.Cells(cells=[
interaction_pb2.Cell(text='1'),
interaction_pb2.Cell(text='3'),
interaction_pb2.Cell(text='5'),
]),
],
),
questions=[
interaction_pb2.Question(id='id', original_text='2')
],
)
number_annotation_utils.add_numeric_values(interaction)
example = converter.convert(interaction, 0)
logging.info(example)
self.assertEqual(_get_int_feature(example, 'input_ids'),
[2, 8, 3, 1, 1, 1, 6, 10, 11, 7, 9, 11])
self.assertEqual(_get_int_feature(example, 'row_ids'),
[0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2])
self.assertEqual(_get_int_feature(example, 'column_ids'),
[0, 0, 0, 1, 2, 3, 1, 2, 3, 1, 2, 3])
self.assertEqual(_get_int_feature(example, 'column_ranks'),
[0, 0, 0, 0, 0, 0, 1, 2, 1, 2, 1, 1])
self.assertEqual(_get_int_feature(example, 'numeric_relations'),
[0, 0, 0, 0, 0, 0, 4, 2, 2, 4, 2, 2])
self.assertEqual(
_get_float_feature(example, 'question_numeric_values'),
_clean_nans([2.0] + [_NAN] * (_MAX_NUMERIC_VALUES - 1)))
def test_convert_with_context_heading(self):
max_seq_length = 20
with tempfile.TemporaryDirectory() as input_dir:
vocab_file = os.path.join(input_dir, 'vocab.txt')
_create_vocab(vocab_file, ['a', 'b', 'c', 'd', 'e'])
converter = tf_example_utils.ToClassifierTensorflowExample(
config=tf_example_utils.ClassifierConversionConfig(
vocab_file=vocab_file,
max_seq_length=max_seq_length,
max_column_id=max_seq_length,
max_row_id=max_seq_length,
strip_column_names=False,
add_aggregation_candidates=False,
use_document_title=True,
use_context_title=True,
update_answer_coordinates=True,
))
interaction = interaction_pb2.Interaction(
table=interaction_pb2.Table(
document_title='E E',
columns=[
interaction_pb2.Cell(text='A'),
interaction_pb2.Cell(text='A B C'),
],
rows=[
interaction_pb2.Cells(cells=[
interaction_pb2.Cell(text='A B'),
interaction_pb2.Cell(text='A B C'),
]),
],
context_heading='B',
),
questions=[
interaction_pb2.Question(
id='id',
original_text='D',
answer=interaction_pb2.Answer(answer_texts=['B C']),
)
],
)
example = converter.convert(interaction, 0)
logging.info(example)
self.assertEqual(
_get_int_feature(example, 'input_ids'),
[2, 5, 3, 10, 10, 3, 7, 3, 6, 6, 7, 8, 6, 7, 6, 7, 8, 0, 0, 0])
self.assertEqual(
_get_int_feature(example, 'label_ids'),
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0])
def test_convert_with_trimmed_cell(self):
max_seq_length = 16
with tempfile.TemporaryDirectory() as input_dir:
vocab_file = os.path.join(input_dir, 'vocab.txt')
_create_vocab(vocab_file, range(10))
converter = tf_example_utils.ToClassifierTensorflowExample(
config=tf_example_utils.ClassifierConversionConfig(
vocab_file=vocab_file,
max_seq_length=max_seq_length,
max_column_id=max_seq_length,
max_row_id=max_seq_length,
strip_column_names=False,
add_aggregation_candidates=False,
cell_trim_length=2,
drop_rows_to_fit=True))
interaction = interaction_pb2.Interaction(
table=interaction_pb2.Table(
columns=[
interaction_pb2.Cell(text='A'),
interaction_pb2.Cell(text='A A'),
interaction_pb2.Cell(text='A A A A'),
],
rows=[
interaction_pb2.Cells(cells=[
interaction_pb2.Cell(text='A A A'),
interaction_pb2.Cell(text='A A A'),
interaction_pb2.Cell(text='A A A'),
]),
interaction_pb2.Cells(cells=[
interaction_pb2.Cell(text='A A A'),
interaction_pb2.Cell(text='A A A'),
interaction_pb2.Cell(text='A A A'),
]),
],
),
questions=[
interaction_pb2.Question(id='id', original_text='A')
],
)
number_annotation_utils.add_numeric_values(interaction)
example = converter.convert(interaction, 0)
logging.info(example)
# We expect the second row to be dropped all cells should be trimmed to
# >= 2 tokens.
self.assertEqual(_get_int_feature(example, 'column_ids'),
[0, 0, 0, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3, 0, 0])
def test_get_empty_example(self):
max_seq_length = 3
with tempfile.TemporaryDirectory() as input_dir:
vocab_file = os.path.join(input_dir, 'vocab.txt')
_create_vocab(vocab_file, [])
converter = tf_example_utils.ToClassifierTensorflowExample(
config=tf_example_utils.ClassifierConversionConfig(
vocab_file=vocab_file,
max_seq_length=max_seq_length,
max_column_id=max_seq_length,
max_row_id=max_seq_length,
strip_column_names=False,
add_aggregation_candidates=False,
))
example = converter.get_empty_example()
logging.info(example)
question_id = _get_byte_feature(example, 'question_id')[0].decode('utf-8')
self.assertEqual(question_id, text_utils.get_padded_question_id())
def __init__(self,
model_dir: Text,
task: Text,
tf_record_filename="test.tfrecord"):
self.tf_record_filename = tf_record_filename
self.task = task
vocab_file = os.path.join(model_dir, "vocab.txt")
bert_config_file = os.path.join(model_dir, "bert_config.json")
classifier_conversion_config = tf_example_utils.ClassifierConversionConfig(
vocab_file=vocab_file,
max_seq_length=_MAX_SEQ_LENGTH,
max_column_id=_MAX_TABLE_ID,
max_row_id=_MAX_TABLE_ID,
strip_column_names=False,
add_aggregation_candidates=False)
self.converter = tf_example_utils.ToClassifierTensorflowExample(
classifier_conversion_config)
self.bert_config = json.load(open(bert_config_file))
self.tapas_config = get_config(task, self.bert_config, model_dir)
self.tapas = get_model(self.tapas_config, _MAX_SEQ_LENGTH)
def _create_examples(
interaction_dir,
example_dir,
vocab_file,
filename,
batch_size,
test_mode,
):
"""Creates TF example for a single dataset."""
filename = f'{filename}.tfrecord'
interaction_path = os.path.join(interaction_dir, filename)
example_path = os.path.join(example_dir, filename)
config = tf_example_utils.ClassifierConversionConfig(
vocab_file=vocab_file,
max_seq_length=FLAGS.max_seq_length,
max_column_id=_MAX_TABLE_ID,
max_row_id=_MAX_TABLE_ID,
strip_column_names=False,
add_aggregation_candidates=False,
)
converter = tf_example_utils.ToClassifierTensorflowExample(config)
examples = []
num_questions = 0
num_conversion_errors = 0
for interaction in prediction_utils.iterate_interactions(interaction_path):
number_annotation_utils.add_numeric_values(interaction)
for i in range(len(interaction.questions)):
num_questions += 1
try:
examples.append(converter.convert(interaction, i))
except ValueError as e:
num_conversion_errors += 1
logging.info("Can't convert interaction: %s error: %s", interaction.id,
e)
if test_mode and len(examples) >= 100:
break
_print(f'Processed: {filename}')
_print(f'Num questions processed: {num_questions}')
_print(f'Num examples: {len(examples)}')
_print(f'Num conversion errors: {num_conversion_errors}')
if batch_size is None:
random.shuffle(examples)
else:
# Make sure the eval sets are divisible by the test batch size since
# otherwise examples will be dropped on TPU.
# These examples will later be ignored when writing the predictions.
originial_num_examples = len(examples)
while len(examples) % batch_size != 0:
examples.append(converter.get_empty_example())
if originial_num_examples != len(examples):
_print(f'Padded with {len(examples) - originial_num_examples} examples.')
with tf.io.TFRecordWriter(
example_path,
options=_to_tf_compression_type(FLAGS.compression_type),
) as writer:
for example in examples:
writer.write(example.SerializeToString())
def test_convert_with_token_selection(self):
max_seq_length = 12
with tempfile.TemporaryDirectory() as input_dir:
vocab_file = os.path.join(input_dir, 'vocab.txt')
_create_vocab(vocab_file, range(10))
converter = tf_example_utils.ToClassifierTensorflowExample(
config=tf_example_utils.ClassifierConversionConfig(
vocab_file=vocab_file,
max_seq_length=max_seq_length,
max_column_id=max_seq_length,
max_row_id=max_seq_length,
strip_column_names=False,
add_aggregation_candidates=False,
))
interaction = interaction_pb2.Interaction(
table=interaction_pb2.Table(
columns=[
interaction_pb2.Cell(text='A'),
interaction_pb2.Cell(text='B'),
interaction_pb2.Cell(text='C'),
],
rows=[
interaction_pb2.Cells(cells=[
interaction_pb2.Cell(text='0 6'),
interaction_pb2.Cell(text='4 7'),
interaction_pb2.Cell(text='5 6'),
]),
interaction_pb2.Cells(cells=[
interaction_pb2.Cell(text='1 7'),
interaction_pb2.Cell(text='3 6'),
interaction_pb2.Cell(text='5 5'),
]),
],
),
questions=[
interaction_pb2.Question(id='id', original_text='2')
],
)
table_coordinates = []
for r, c, t in [(0, 0, 0), (1, 0, 0), (1, 2, 0), (2, 0, 0),
(2, 2, 0), (2, 2, 1)]:
table_coordinates.append(
table_selection_pb2.TableSelection.TokenCoordinates(
row_index=r, column_index=c, token_index=t))
interaction.questions[0].Extensions[
table_selection_pb2.TableSelection.
table_selection_ext].CopyFrom(
table_selection_pb2.TableSelection(
selected_tokens=table_coordinates))
number_annotation_utils.add_numeric_values(interaction)
example = converter.convert(interaction, 0)
logging.info(example)
self.assertEqual(_get_int_feature(example, 'input_ids'),
[2, 8, 3, 1, 6, 11, 7, 11, 11, 0, 0, 0])
self.assertEqual(_get_int_feature(example, 'row_ids'),
[0, 0, 0, 0, 1, 1, 2, 2, 2, 0, 0, 0])
self.assertEqual(_get_int_feature(example, 'column_ids'),
[0, 0, 0, 1, 1, 3, 1, 3, 3, 0, 0, 0])
self.assertEqual(_get_int_feature(example, 'column_ranks'),
[0, 0, 0, 0, 1, 1, 2, 1, 1, 0, 0, 0])
self.assertEqual(_get_int_feature(example, 'numeric_relations'),
[0, 0, 0, 0, 4, 2, 4, 2, 2, 0, 0, 0])
f.write('model_checkpoint_path: "model.ckpt-0"')
for suffix in ['.data-00000-of-00001', '.index', '.meta']:
shutil.copyfile(f'tapas_sqa_base/model.ckpt{suffix}',
f'results/sqa/model/model.ckpt-0{suffix}')
max_seq_length = 512
vocab_file = "tapas_sqa_base/vocab.txt"
config = tf_example_utils.ClassifierConversionConfig(
vocab_file=vocab_file,
max_seq_length=max_seq_length,
max_column_id=max_seq_length,
max_row_id=max_seq_length,
strip_column_names=False,
add_aggregation_candidates=False,
)
converter = tf_example_utils.ToClassifierTensorflowExample(config)
def convert_interactions_to_examples(tables_and_queries):
"""Calls Tapas converter to convert interaction to example."""
for idx, (table, queries) in enumerate(tables_and_queries):
interaction = interaction_pb2.Interaction()
for position, query in enumerate(queries):
question = interaction.questions.add()
question.original_text = query
question.id = f"{idx}-0_{position}"
for header in table[0]:
interaction.table.columns.add().text = header
for line in table[1:]:
row = interaction.table.rows.add()
for cell in line:
def start_bundle(self):
self._converter = tf_example_utils.ToClassifierTensorflowExample(
self._config)
