def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
input_files = []
for input_pattern in FLAGS.input_file.split(","):
input_files.extend(tf.gfile.Glob(input_pattern))
tf.logging.info("*** Reading from input files ***")
for input_file in input_files:
tf.logging.info(" %s", input_file)
rng = random.Random(FLAGS.random_seed)
instances = create_training_instances(input_files, tokenizer,
FLAGS.max_seq_length,
FLAGS.dupe_factor,
FLAGS.short_seq_prob,
FLAGS.masked_lm_prob,
FLAGS.max_predictions_per_seq, rng)
output_files = FLAGS.output_file.split(",")
tf.logging.info("*** Writing to output files ***")
for output_file in output_files:
tf.logging.info(" %s", output_file)
write_instance_to_example_files(instances, tokenizer, FLAGS.max_seq_length,
FLAGS.max_predictions_per_seq,
output_files)
def create_tokenizer_from_hub_module(bert_hub_module_handle):
"""Get the vocab file and casing info from the Hub module."""
with tf.Graph().as_default():
bert_module = hub.Module(bert_hub_module_handle)
tokenization_info = bert_module(signature="tokenization_info", as_dict=True)
with tf.Session() as sess:
vocab_file, do_lower_case = sess.run([tokenization_info["vocab_file"],
tokenization_info["do_lower_case"]])
return tokenization.FullTokenizer(
vocab_file=vocab_file, do_lower_case=do_lower_case)
def main(argv: Sequence[str]) -> None:
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
# Download FEVER data
raw_data = {}
for split_name, url in URL_DICT.items():
logging.info('Downloading %s split', split_name)
with request.urlopen(url) as open_url:
sample_list = []
lines = open_url.readlines()
for line in lines:
sample_list.append(json.loads(line))
raw_data[split_name] = sample_list
# Process FEVER data
tokenizer = bert_tokenization.FullTokenizer(FLAGS.vocab_path,
do_lower_case=True)
spacy_model = None
if spacy_model is None:
spacy_model = spacy.load('en_core_web_md')
processed_data = {}
for split_name, split_data in raw_data.items():
logging.info('Processing %s split', split_name)
processed_split_data = process_data(split_data, spacy_model, tokenizer)
processed_data[split_name] = processed_split_data
# Create TFRecords
tf.io.gfile.makedirs(FLAGS.save_dir)
for split_name, split_data in processed_data.items():
file_path = os.path.join(FLAGS.save_dir, split_name)
logging.info('Writing %s split to %s', split_name, file_path)
writer = tf.io.TFRecordWriter(file_path)
for sample in split_data:
features = tf.train.Features(
feature={
key: tf.train.Feature(int64_list=tf.train.Int64List(
value=value))
for key, value in sample.items()
})
record_bytes = tf.train.Example(
features=features).SerializeToString()
writer.write(record_bytes)
def main(argv: Sequence[str]) -> None:
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
tokenizer = bert_tokenization.FullTokenizer(FLAGS.vocab_path,
do_lower_case=True)
spacy_model = None
if spacy_model is None:
spacy_model = spacy.load('en_core_web_md')
raw_data = {}
for split_file_name in FLAGS.split_file_names:
path = os.path.join(FLAGS.data_dir, split_file_name + '.json')
with tf.io.gfile.GFile(path, 'rb') as data_file:
raw_data[split_file_name] = json.load(data_file)
processed_data = {}
relation_vocab = {}
for split_name, split_data in raw_data.items():
logging.info('Processing %s split', split_name)
processed_split_data, relation_vocab = process_data(
split_data, relation_vocab, spacy_model, tokenizer)
processed_data[split_name] = processed_split_data
# Create TFRecords
tf.io.gfile.makedirs(FLAGS.save_dir)
for split_name, split_data in processed_data.items():
file_path = os.path.join(FLAGS.save_dir, split_name)
logging.info('Writing %s split to %s', split_name, file_path)
writer = tf.io.TFRecordWriter(file_path)
for sample in split_data:
features = tf.train.Features(
feature={
key: tf.train.Feature(int64_list=tf.train.Int64List(
value=value))
for key, value in sample.items()
})
record_bytes = tf.train.Example(
features=features).SerializeToString()
writer.write(record_bytes)
# save label vocab
vocab_path = os.path.join(FLAGS.save_dir, 'relation_vocab.json')
with tf.io.gfile.GFile(vocab_path, 'w+') as vocab_file:
json.dump(relation_vocab, vocab_file)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = {
"cola": ColaProcessor,
"mnli": MnliProcessor,
"mrpc": MrpcProcessor,
"xnli": XnliProcessor,
}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
raise ValueError(
"At least one of `do_train`, `do_eval` or `do_predict' must be True.")
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
tf.gfile.MakeDirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=contrib_tpu.TPUConfig(
tpu_job_name=FLAGS.tpu_job_name,
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
train_examples = None
num_train_steps = None
num_warmup_steps = None
if FLAGS.do_train:
train_examples = processor.get_train_examples(FLAGS.data_dir)
num_train_steps = int(
len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
model_fn = model_fn_builder(
bert_config=bert_config,
num_labels=len(label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
file_based_convert_examples_to_features(
train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
train_input_fn = file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
if FLAGS.do_eval:
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
num_actual_eval_examples = len(eval_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on. These do NOT count towards the metric (all tf.metrics
# support a per-instance weight, and these get a weight of 0.0).
while len(eval_examples) % FLAGS.eval_batch_size != 0:
eval_examples.append(PaddingInputExample())
eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
file_based_convert_examples_to_features(
eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(eval_examples), num_actual_eval_examples,
len(eval_examples) - num_actual_eval_examples)
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
# This tells the estimator to run through the entire set.
eval_steps = None
# However, if running eval on the TPU, you will need to specify the
# number of steps.
if FLAGS.use_tpu:
assert len(eval_examples) % FLAGS.eval_batch_size == 0
eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
eval_input_fn = file_based_input_fn_builder(
input_file=eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder)
result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
with tf.gfile.GFile(output_eval_file, "w") as writer:
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if FLAGS.do_predict:
predict_examples = processor.get_test_examples(FLAGS.data_dir)
num_actual_predict_examples = len(predict_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on.
while len(predict_examples) % FLAGS.predict_batch_size != 0:
predict_examples.append(PaddingInputExample())
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
file_based_convert_examples_to_features(predict_examples, label_list,
FLAGS.max_seq_length, tokenizer,
predict_file)
tf.logging.info("***** Running prediction*****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(predict_examples), num_actual_predict_examples,
len(predict_examples) - num_actual_predict_examples)
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_drop_remainder = True if FLAGS.use_tpu else False
predict_input_fn = file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=predict_drop_remainder)
result = estimator.predict(input_fn=predict_input_fn)
output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
with tf.gfile.GFile(output_predict_file, "w") as writer:
num_written_lines = 0
tf.logging.info("***** Predict results *****")
for (i, prediction) in enumerate(result):
probabilities = prediction["probabilities"]
if i >= num_actual_predict_examples:
break
output_line = "\t".join(
str(class_probability)
for class_probability in probabilities) + "\n"
writer.write(output_line)
num_written_lines += 1
assert num_written_lines == num_actual_predict_examples
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
layer_indexes = [int(x) for x in FLAGS.layers.split(",")]
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
run_config = contrib_tpu.RunConfig(
master=FLAGS.master,
tpu_config=contrib_tpu.TPUConfig(
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
examples = read_examples(FLAGS.input_file)
features = convert_examples_to_features(
examples=examples, seq_length=FLAGS.max_seq_length, tokenizer=tokenizer)
unique_id_to_feature = {}
for feature in features:
unique_id_to_feature[feature.unique_id] = feature
model_fn = model_fn_builder(
bert_config=bert_config,
init_checkpoint=FLAGS.init_checkpoint,
layer_indexes=layer_indexes,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_one_hot_embeddings)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
predict_batch_size=FLAGS.batch_size)
input_fn = input_fn_builder(
features=features, seq_length=FLAGS.max_seq_length)
with codecs.getwriter("utf-8")(tf.gfile.Open(FLAGS.output_file,
"w")) as writer:
for result in estimator.predict(input_fn, yield_single_examples=True):
unique_id = int(result["unique_id"])
feature = unique_id_to_feature[unique_id]
output_json = collections.OrderedDict()
output_json["linex_index"] = unique_id
all_features = []
for (i, token) in enumerate(feature.tokens):
all_layers = []
for (j, layer_index) in enumerate(layer_indexes):
layer_output = result["layer_output_%d" % j]
layers = collections.OrderedDict()
layers["index"] = layer_index
layers["values"] = [
round(float(x), 6) for x in layer_output[i:(i + 1)].flat
]
all_layers.append(layers)
features = collections.OrderedDict()
features["token"] = token
features["layers"] = all_layers
all_features.append(features)
output_json["features"] = all_features
writer.write(json.dumps(output_json) + "\n")
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
validate_flags_or_throw(bert_config)
tf.gfile.MakeDirs(FLAGS.output_dir)
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
train_examples = None
num_train_steps = None
num_warmup_steps = None
if FLAGS.do_train:
train_examples = read_squad_examples(
input_file=FLAGS.train_file, is_training=True)
num_train_steps = int(
len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
# Pre-shuffle the input to avoid having to make a very large shuffle
# buffer in in the `input_fn`.
rng = random.Random(12345)
rng.shuffle(train_examples)
model_fn = model_fn_builder(
bert_config=bert_config,
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
# We write to a temporary file to avoid storing very large constant tensors
# in memory.
train_writer = FeatureWriter(
filename=os.path.join(FLAGS.output_dir, "train.tf_record"),
is_training=True)
convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=True,
output_fn=train_writer.process_feature)
train_writer.close()
tf.logging.info("***** Running training *****")
tf.logging.info(" Num orig examples = %d", len(train_examples))
tf.logging.info(" Num split examples = %d", train_writer.num_features)
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
del train_examples
train_input_fn = input_fn_builder(
input_file=train_writer.filename,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
if FLAGS.do_predict:
eval_examples = read_squad_examples(
input_file=FLAGS.predict_file, is_training=False)
eval_writer = FeatureWriter(
filename=os.path.join(FLAGS.output_dir, "eval.tf_record"),
is_training=False)
eval_features = []
def append_feature(feature):
eval_features.append(feature)
eval_writer.process_feature(feature)
convert_examples_to_features(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=False,
output_fn=append_feature)
eval_writer.close()
tf.logging.info("***** Running predictions *****")
tf.logging.info(" Num orig examples = %d", len(eval_examples))
tf.logging.info(" Num split examples = %d", len(eval_features))
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
all_results = []
predict_input_fn = input_fn_builder(
input_file=eval_writer.filename,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=False)
# If running eval on the TPU, you will need to specify the number of
# steps.
all_results = []
for result in estimator.predict(
predict_input_fn, yield_single_examples=True):
if len(all_results) % 1000 == 0:
tf.logging.info("Processing example: %d" % (len(all_results)))
unique_id = int(result["unique_ids"])
start_logits = [float(x) for x in result["start_logits"].flat]
end_logits = [float(x) for x in result["end_logits"].flat]
all_results.append(
RawResult(
unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
output_prediction_file = os.path.join(FLAGS.output_dir, "predictions.json")
output_nbest_file = os.path.join(FLAGS.output_dir, "nbest_predictions.json")
output_null_log_odds_file = os.path.join(FLAGS.output_dir, "null_odds.json")
write_predictions(eval_examples, eval_features, all_results,
FLAGS.n_best_size, FLAGS.max_answer_length,
FLAGS.do_lower_case, output_prediction_file,
output_nbest_file, output_null_log_odds_file)
def __init__(self,
data_sources,
env_config,
debug_writer=None,
worker_idx=0):
"""Construct the StreetView environment.
Args:
data_sources: A list of strings containing one or more of 'train', 'dev'
and 'test'.
env_config: The configuration object containing settings for the current
run.
debug_writer: Class for writing execution traces to dir.
worker_idx: Restrict regions that this worker explores to those
where worker_idx % region_idx == 0. Default 0 means include all regions.
region_idx is defined as the order in a sorted list of region names.
"""
super(StreetViewEnv, self).__init__()
# Logging execution traces if enabled
self._run_writer = debug_writer
self._env_config = env_config
# Action space
self._panoramic_actions = env_config.panoramic_action_space
self._panoramic_action_bins = env_config.panoramic_action_bins
# Attributes prefixed by _all* are dictionaries indexed by region name
self._all_regions = self._select_regions(env_config, worker_idx)
self._all_graphs = self._init_graphs(env_config, self._all_regions)
# Load entry sequences for each region, and activate the current region
self._all_entry_sequences = self._init_entry_sequences(
data_sources, env_config, self._all_regions)
logging.info('Loaded %d regions with data sources %s',
len(self._all_entry_sequences), data_sources)
for region_name, entry_sequence in self._all_entry_sequences.items():
logging.info('Region %s has %d entries', region_name,
len(entry_sequence))
self._all_feature_loaders = self._init_feature_loaders(
env_config, self._all_regions)
# Using BERT word piece tokenizer.
self._tokenizer = tokenization.FullTokenizer(
vocab_file=env_config.vocab_file, do_lower_case=True)
# Constants from env_config.
self._max_actions_per_episode = env_config.max_agent_actions
self._instruction_tok_len = env_config.instruction_tok_len
# Constant parameter
self._base_yaw_angle = env_config.base_yaw_angle
# Class members.
# Variables that identify current instruction (assigned in reset)
self._graph = None
self._current_region = ''
self._current_sequence_idx = -1
self._current_entry_idx = 0
# State variables
self._frame_count = 0
self._goal_pano_id = -1
self._graph_state = GraphState(-1, 0., 0., 0.)
self._distance_to_goal = 0.
# Information about current instruction
self._golden_actions = None
self._golden_path = None
def main(argv: Sequence[str]) -> None:
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
tokenizer = bert_tokenization.FullTokenizer(FLAGS.vocab_path,
do_lower_case=True)
spacy_model = None
if spacy_model is None:
spacy_model = spacy.load('en_core_web_md')
def get_feature(sample, feature_name, idx=0):
feature = sample.features.feature[feature_name]
return getattr(feature, feature.WhichOneof('kind')).value[idx]
path = os.path.join(FLAGS.data_dir, 'webred_21.tfrecord')
samples = []
dataset = tf.data.TFRecordDataset(path)
for raw_sample in dataset:
sample = {}
example = tf.train.Example()
example.ParseFromString(raw_sample.numpy())
sample['annotated_text'] = get_feature(example,
'sentence').decode('utf-8')
sample['relation'] = get_feature(example,
'relation_name').decode('utf-8')
sample['num_pos_raters'] = get_feature(example, 'num_pos_raters')
samples.append(sample)
np.random.seed(0)
shuffled_indices = np.random.permutation(len(samples))
shuffled_samples = [samples[idx] for idx in shuffled_indices]
raw_data = {}
eval_split_size = int(0.1 * len(samples))
raw_data['test'] = shuffled_samples[:eval_split_size]
raw_data['dev'] = shuffled_samples[eval_split_size:2 * eval_split_size]
raw_data['train'] = shuffled_samples[2 * eval_split_size:]
processed_data = {}
relation_vocab = {}
for split_name, split_data in raw_data.items():
logging.info('Processing %s split', split_name)
processed_split_data, relation_vocab = process_data(
split_data, relation_vocab, spacy_model, tokenizer)
processed_data[split_name] = processed_split_data
# Create TFRecords
tf.io.gfile.makedirs(FLAGS.save_dir)
for split_name, split_data in processed_data.items():
file_path = os.path.join(FLAGS.save_dir, split_name)
logging.info('Writing %s split to %s', split_name, file_path)
writer = tf.io.TFRecordWriter(file_path)
for sample in split_data:
features = tf.train.Features(
feature={
key: tf.train.Feature(int64_list=tf.train.Int64List(
value=value))
for key, value in sample.items()
})
record_bytes = tf.train.Example(
features=features).SerializeToString()
writer.write(record_bytes)
# save label vocab
vocab_path = os.path.join(FLAGS.save_dir, 'relation_vocab.json')
with tf.io.gfile.GFile(vocab_path, 'w+') as vocab_file:
json.dump(relation_vocab, vocab_file)
