def setUp(self):
super(BertExampleTest, self).setUp()
vocab_tokens = [
'[CLS]', '[SEP]', '[PAD]', 'a', 'b', 'c', '##d', '##e', "This", "is",
"test", ".", "Test", "1", "2"
]
vocab_file = os.path.join(FLAGS.test_tmpdir, 'vocab.txt')
with tf.io.gfile.GFile(vocab_file, 'w') as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))
label_map = {'KEEP': 1, 'DELETE': 2}
max_seq_length = 8
do_lower_case = False
converter = tagging_converter.TaggingConverter([])
self._builder = bert_example.BertExampleBuilder(label_map, vocab_file,
max_seq_length,
do_lower_case, converter,
"Normal")
self._builder = bert_example.BertExampleBuilder(label_map, vocab_file,
max_seq_length,
do_lower_case, converter,
"Normal")
self._pos_builder = bert_example.BertExampleBuilder(
label_map, vocab_file, max_seq_length, do_lower_case, converter, "POS")
self._sentence_builder = bert_example.BertExampleBuilder(
label_map, vocab_file, max_seq_length, do_lower_case, converter,
"Sentence")
self._label_map = label_map
self._vocab_file = vocab_file
def test_first_deletion_idx_computation(self):
converter = tagging_converter.TaggingConverter([])
tag_strs = ['KEEP', 'DELETE', 'DELETE', 'KEEP']
tags = [tagging.Tag(s) for s in tag_strs]
source_token_idx = 3
idx = converter._find_first_deletion_idx(source_token_idx, tags)
self.assertEqual(idx, 1)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
flags.mark_flag_as_required('input_file')
flags.mark_flag_as_required('input_format')
flags.mark_flag_as_required('output_file')
flags.mark_flag_as_required('label_map_file')
flags.mark_flag_as_required('vocab_file')
flags.mark_flag_as_required('saved_model')
label_map = utils.read_label_map(FLAGS.label_map_file)
converter = tagging_converter.TaggingConverter(
tagging_converter.get_phrase_vocabulary_from_label_map(label_map),
FLAGS.enable_swap_tag)
builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file,
FLAGS.max_seq_length,
FLAGS.do_lower_case, converter)
predictor = predict_utils.LaserTaggerPredictor(
tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder,
label_map)
num_predicted = 0
with tf.gfile.Open(FLAGS.output_file, 'w') as writer:
for i, (sources, target) in enumerate(utils.yield_sources_and_targets(
FLAGS.input_file, FLAGS.input_format)):
logging.log_every_n(
logging.INFO,
f'{i} examples processed, {num_predicted} converted to tf.Example.',
100)
prediction = predictor.predict(sources)
writer.write(f'{" ".join(sources)}\t{prediction}\t{target}\n')
num_predicted += 1
logging.info(f'{num_predicted} predictions saved to:\n{FLAGS.output_file}')
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
flags.mark_flag_as_required('input_file')
flags.mark_flag_as_required('input_format')
flags.mark_flag_as_required('output_tfrecord')
flags.mark_flag_as_required('label_map_file')
flags.mark_flag_as_required('vocab_file')
label_map = utils.read_label_map(FLAGS.label_map_file)
converter = tagging_converter.TaggingConverter(
tagging_converter.get_phrase_vocabulary_from_label_map(label_map),
FLAGS.enable_swap_tag)
builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file,
FLAGS.max_seq_length,
FLAGS.do_lower_case, converter)
num_converted = 0
with tf.io.TFRecordWriter(FLAGS.output_tfrecord) as writer:
for i, (sources, target) in enumerate(utils.yield_sources_and_targets(
FLAGS.input_file, FLAGS.input_format)):
logging.log_every_n(
logging.INFO,
f'{i} examples processed, {num_converted} converted to tf.Example.',
10000)
example = builder.build_bert_example(
sources, target,
FLAGS.output_arbitrary_targets_for_infeasible_examples)
if example is None:
continue
writer.write(example.to_tf_example().SerializeToString())
num_converted += 1
logging.info(f'Done. {num_converted} examples converted to tf.Example.')
count_fname = _write_example_count(num_converted)
logging.info(f'Wrote:\n{FLAGS.output_tfrecord}\n{count_fname}')
def test_construct_example(self):
vocab_file = "gs://bert_traning_yechen/trained_bert_uncased/bert_POS/vocab.txt"
label_map_file = "gs://publicly_available_models_yechen/best_hypertuned_POS/label_map.txt"
enable_masking = False
do_lower_case = True
embedding_type = "POS"
label_map = utils.read_label_map(label_map_file)
converter = tagging_converter.TaggingConverter(
tagging_converter.get_phrase_vocabulary_from_label_map(label_map),
True)
id_2_tag = {
tag_id: tagging.Tag(tag)
for tag, tag_id in label_map.items()
}
builder = bert_example.BertExampleBuilder(label_map, vocab_file, 10,
do_lower_case, converter,
embedding_type,
enable_masking)
inputs, example = construct_example("This is a test", builder)
self.assertEqual(
inputs, {
'input_ids': [2, 12, 1016, 6, 9, 6, 9, 10, 12, 3],
'input_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'segment_ids': [2, 16, 14, 14, 32, 14, 32, 5, 14, 41]
})
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
flags.mark_flag_as_required('input_file')
flags.mark_flag_as_required('input_format')
flags.mark_flag_as_required('output_file')
flags.mark_flag_as_required('label_map_file')
flags.mark_flag_as_required('vocab_file')
flags.mark_flag_as_required('saved_model')
label_map = utils.read_label_map(FLAGS.label_map_file)
converter = tagging_converter.TaggingConverter(
tagging_converter.get_phrase_vocabulary_from_label_map(label_map),
FLAGS.enable_swap_tag)
builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file,
FLAGS.max_seq_length,
FLAGS.do_lower_case, converter)
predictor = predict_utils.LaserTaggerPredictor(
tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder,
label_map)
print(colored("%s input file:%s" % (curLine(), FLAGS.input_file), "red"))
sources_list = []
target_list = []
with tf.gfile.GFile(FLAGS.input_file) as f:
for line in f:
sources, target, lcs_rate = line.rstrip('\n').split('\t')
sources_list.append([sources])
target_list.append(target)
number = len(sources_list) # 总样本数
predict_batch_size = min(64, number)
batch_num = math.ceil(float(number) / predict_batch_size)
start_time = time.time()
num_predicted = 0
with tf.gfile.Open(FLAGS.output_file, 'w') as writer:
writer.write(f'source\tprediction\ttarget\n')
for batch_id in range(batch_num):
sources_batch = sources_list[batch_id *
predict_batch_size:(batch_id + 1) *
predict_batch_size]
prediction_batch = predictor.predict_batch(
sources_batch=sources_batch)
assert len(prediction_batch) == len(sources_batch)
num_predicted += len(prediction_batch)
for id, [prediction,
sources] in enumerate(zip(prediction_batch,
sources_batch)):
target = target_list[batch_id * predict_batch_size + id]
writer.write(f'{"".join(sources)}\t{prediction}\t{target}\n')
if batch_id % 20 == 0:
cost_time = (time.time() - start_time) / 60.0
print(
"%s batch_id=%d/%d, predict %d/%d examples, cost %.2fmin."
% (curLine(), batch_id + 1, batch_num, num_predicted,
number, cost_time))
cost_time = (time.time() - start_time) / 60.0
logging.info(
f'{curLine()} {num_predicted} predictions saved to:{FLAGS.output_file}, cost {cost_time} min, ave {cost_time / num_predicted} min.'
)
def test_no_match(self):
input_texts = ['Turing was born in 1912 .', 'Turing died in 1954 .']
target = 'Turing was born in 1912 and died in 1954 .'
task = tagging.EditingTask(input_texts)
phrase_vocabulary = ['but']
converter = tagging_converter.TaggingConverter(phrase_vocabulary)
tags = converter.compute_tags(task, target)
# Vocabulary doesn't contain "and" so the inputs can't be converted to the
# target.
self.assertFalse(tags)
def main_sentence(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
# flags.mark_flag_as_required('input_file')
flags.mark_flag_as_required('input_format')
flags.mark_flag_as_required('output_file')
flags.mark_flag_as_required('label_map_file')
flags.mark_flag_as_required('vocab_file')
flags.mark_flag_as_required('saved_model')
label_map = utils.read_label_map(FLAGS.label_map_file)
converter = tagging_converter.TaggingConverter(
tagging_converter.get_phrase_vocabulary_from_label_map(label_map),
FLAGS.enable_swap_tag)
print("FLAGS.vocab_file", FLAGS.vocab_file)
print("FLAGS.max_seq_length", FLAGS.max_seq_length)
print("FLAGS.do_lower_case", FLAGS.do_lower_case)
print("converter", converter)
builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file,
FLAGS.max_seq_length,
FLAGS.do_lower_case, converter)
predictor = predict_utils.LaserTaggerPredictor(
tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder,
label_map)
# print(colored("%s input file:%s" % (curLine(), FLAGS.input_file), "red"))
# sources_list = []
# target_list = []
# with tf.io.gfile.GFile(FLAGS.input_file) as f:
# for line in f:
# sources = line.rstrip('\n')
# sources_list.append([sources])
# # target_list.append(target)
while True:
sentence = input(">> ")
batch_num = 1
start_time = time.time()
num_predicted = 0
for batch_id in range(batch_num):
# sources_batch = sources_list[batch_id * predict_batch_size: (batch_id + 1) * predict_batch_size]
sources_batch = [sentence]
prediction_batch = predictor.predict_batch(
sources_batch=sources_batch)
assert len(prediction_batch) == len(sources_batch)
num_predicted += len(prediction_batch)
for id, [prediction,
sources] in enumerate(zip(prediction_batch,
sources_batch)):
# target = target_list[batch_id * predict_batch_size + id]
print("原句sources: %s 拓展句predict: %s" % (sentence, prediction))
# cost_time = (time.time() - start_time) / 60.0
print("耗时", (time.time() - start_time) / 60.0, "s")
def setUp(self):
super(BertExampleTest, self).setUp()
vocab_tokens = ['[CLS]', '[SEP]', '[PAD]', 'a', 'b', 'c', '##d', '##e']
vocab_file = os.path.join(FLAGS.test_tmpdir, 'vocab.txt')
with tf.io.gfile.GFile(vocab_file, 'w') as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))
max_seq_length = 8
do_lower_case = False
converter = tagging_converter.TaggingConverter([])
self._builder = bert_example.BertExampleBuilder(vocab_file, max_seq_length,
do_lower_case)
def setUp(self):
super(PredictUtilsTest, self).setUp()
vocab_tokens = ['[CLS]', '[SEP]', '[PAD]', 'a', 'b', 'c', '##d', '##e']
vocab_file = os.path.join(FLAGS.test_tmpdir, 'vocab.txt')
with tf.io.gfile.GFile(vocab_file, 'w') as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))
self._label_map = {'KEEP': 0, 'DELETE': 1, 'KEEP|and': 2}
max_seq_length = 8
do_lower_case = False
converter = tagging_converter.TaggingConverter([])
self._builder = bert_example.BertExampleBuilder(
self._label_map, vocab_file, max_seq_length, do_lower_case, converter)
def __init__(self):
# if len(argv) > 1:
# raise app.UsageError('Too many command-line arguments.')
flags.mark_flag_as_required('input_file')
flags.mark_flag_as_required('input_format')
flags.mark_flag_as_required('output_file')
flags.mark_flag_as_required('label_map_file')
flags.mark_flag_as_required('vocab_file')
flags.mark_flag_as_required('saved_model')
label_map = utils.read_label_map(FLAGS.label_map_file)
converter = tagging_converter.TaggingConverter(
tagging_converter.get_phrase_vocabulary_from_label_map(label_map),
FLAGS.enable_swap_tag)
builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file,
FLAGS.max_seq_length,
FLAGS.do_lower_case,
converter)
self.predictor = predict_utils.LaserTaggerPredictor(
tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder,
label_map)
def test_invalid_embedding_type(self):
with self.assertRaises(ValueError):
# The embedding type is wrong, and return raise ValueError
invalid_builder = bert_example.BertExampleBuilder(
self._label_map, self._vocab_file, 8, True,
tagging_converter.TaggingConverter([]), "Wrong Type")
def test_matching_conversion(self, input_texts, target, phrase_vocabulary,
target_tags):
task = tagging.EditingTask(input_texts)
converter = tagging_converter.TaggingConverter(phrase_vocabulary)
tags = converter.compute_tags(task, target)
self.assertEqual(tags_to_str(tags), tags_to_str(target_tags))
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
flags.mark_flag_as_required('input_file')
flags.mark_flag_as_required('input_format')
flags.mark_flag_as_required('output_file')
flags.mark_flag_as_required('label_map_file')
flags.mark_flag_as_required('vocab_file')
flags.mark_flag_as_required('saved_model')
label_map = utils.read_label_map(FLAGS.label_map_file)
converter = tagging_converter.TaggingConverter(
tagging_converter.get_phrase_vocabulary_from_label_map(label_map),
FLAGS.enable_swap_tag)
builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file,
FLAGS.max_seq_length,
FLAGS.do_lower_case, converter)
predictor = predict_utils.LaserTaggerPredictor(
tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder,
label_map)
print(colored("%s input file:%s" % (curLine(), FLAGS.input_file), "red"))
sourcesA_list = []
sourcesB_list = []
target_list = []
with tf.gfile.GFile(FLAGS.input_file) as f:
for line in f:
sourceA, sourceB, label = line.rstrip('\n').split('\t')
sourcesA_list.append([sourceA.strip(".")])
sourcesB_list.append([sourceB.strip(".")])
target_list.append(label)
number = len(sourcesA_list) # 总样本数
predict_batch_size = min(32, number)
batch_num = math.ceil(float(number) / predict_batch_size)
start_time = time.time()
num_predicted = 0
prediction_list = []
with tf.gfile.Open(FLAGS.output_file, 'w') as writer:
for batch_id in range(batch_num):
sources_batch = sourcesA_list[batch_id *
predict_batch_size:(batch_id + 1) *
predict_batch_size]
batch_b = sourcesB_list[batch_id *
predict_batch_size:(batch_id + 1) *
predict_batch_size]
location_batch = []
sources_batch.extend(batch_b)
for source in sources_batch:
location = list()
for char in source[0]:
if (char >= '0' and char <= '9') or char in '.- ' or (
char >= 'a' and char <= 'z') or (char >= 'A'
and char <= 'Z'):
location.append("1") # TODO TODO
else:
location.append("0")
location_batch.append("".join(location))
prediction_batch = predictor.predict_batch(
sources_batch=sources_batch, location_batch=location_batch)
current_batch_size = int(len(sources_batch) / 2)
assert len(prediction_batch) == current_batch_size * 2
for id in range(0, current_batch_size):
target = target_list[num_predicted + id]
prediction_A = prediction_batch[id]
prediction_B = prediction_batch[current_batch_size + id]
sourceA = "".join(sources_batch[id])
sourceB = "".join(sources_batch[current_batch_size + id])
if prediction_A == prediction_B: # 其中一个换为source
lcsA = len(_compute_lcs(sourceA, prediction_A))
if lcsA < 8: # A的变化大
prediction_B = sourceB
else:
lcsB = len(_compute_lcs(sourceB, prediction_B))
if lcsA <= lcsB: # A的变化大
prediction_B = sourceB
else:
prediction_A = sourceA
print(curLine(), batch_id, prediction_A,
prediction_B, "target:", target,
"current_batch_size=", current_batch_size,
"lcsA=%d,lcsB=%d" % (lcsA, lcsB))
writer.write(f'{prediction_A}\t{prediction_B}\t{target}\n')
prediction_list.append("%s\t%s\n" % (sourceA, prediction_A))
# print(curLine(), id,"sourceA:", sourceA, "sourceB:",sourceB, "target:", target)
prediction_list.append("%s\t%s\n" % (sourceB, prediction_B))
num_predicted += current_batch_size
if batch_id % 20 == 0:
cost_time = (time.time() - start_time) / 60.0
print(curLine(), id, prediction_A, prediction_B, "target:",
target, "current_batch_size=", current_batch_size)
print(curLine(), id, "sourceA:", sourceA, "sourceB:", sourceB,
"target:", target)
print(
"%s batch_id=%d/%d, predict %d/%d examples, cost %.2fmin."
% (curLine(), batch_id + 1, batch_num, num_predicted,
number, cost_time))
with open("prediction.txt", "w") as prediction_file:
prediction_file.writelines(prediction_list)
print(curLine(), "save to prediction_qa.txt.")
cost_time = (time.time() - start_time) / 60.0
print(curLine(), id, prediction_A, prediction_B, "target:", target,
"current_batch_size=", current_batch_size)
print(curLine(), id, "sourceA:", sourceA, "sourceB:", sourceB, "target:",
target)
logging.info(
f'{curLine()} {num_predicted} predictions saved to:{FLAGS.output_file}, cost {cost_time} min, ave {cost_time / num_predicted*60000}ms.'
)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
flags.mark_flag_as_required('input_file')
flags.mark_flag_as_required('input_format')
flags.mark_flag_as_required('output_file')
flags.mark_flag_as_required('label_map_file')
flags.mark_flag_as_required('vocab_file')
flags.mark_flag_as_required('saved_model')
label_map = utils.read_label_map(FLAGS.label_map_file)
converter = tagging_converter.TaggingConverter(
tagging_converter.get_phrase_vocabulary_from_label_map(label_map),
FLAGS.enable_swap_tag)
builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file,
FLAGS.max_seq_length,
FLAGS.do_lower_case, converter)
predictor = predict_utils.LaserTaggerPredictor(
tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder,
label_map)
print(colored("%s input file:%s" % (curLine(), FLAGS.input_file), "red"))
num_predicted = 0
sources_list = []
location_list = []
corpus_id_list = []
entity_list = []
domainname_list = []
intentname_list = []
context_list = []
template_id_list = []
with open(FLAGS.input_file, "r") as f:
corpus_json_list = json.load(f)
# corpus_json_list = corpus_json_list[:100]
for corpus_json in corpus_json_list:
sources_list.append([corpus_json["oriText"]])
location_list.append(corpus_json["location"])
corpus_id_list.append(corpus_json["corpus_id"])
entity_list.append(corpus_json["entity"])
domainname_list.append(corpus_json["domainname"])
intentname_list.append(corpus_json["intentname"])
context_list.append(corpus_json["context"])
template_id_list.append(corpus_json["template_id"])
number = len(sources_list) # 总样本数
predict_batch_size = min(64, number)
batch_num = math.ceil(float(number) / predict_batch_size)
start_time = time.time()
index = 0
for batch_id in range(batch_num):
sources_batch = sources_list[batch_id *
predict_batch_size:(batch_id + 1) *
predict_batch_size]
location_batch = location_list[batch_id *
predict_batch_size:(batch_id + 1) *
predict_batch_size]
prediction_batch = predictor.predict_batch(
sources_batch=sources_batch, location_batch=location_batch)
assert len(prediction_batch) == len(sources_batch)
num_predicted += len(prediction_batch)
for id, [prediction,
sources] in enumerate(zip(prediction_batch, sources_batch)):
index = batch_id * predict_batch_size + id
output_json = {
"corpus_id": corpus_id_list[index],
"oriText": prediction,
"sources": sources[0],
"entity": entity_list[index],
"location": location_list[index],
"domainname": domainname_list[index],
"intentname": intentname_list[index],
"context": context_list[index],
"template_id": template_id_list[index]
}
corpus_json_list[index] = output_json
if batch_id % 20 == 0:
cost_time = (time.time() - start_time) / 60.0
print("%s batch_id=%d/%d, predict %d/%d examples, cost %.2fmin." %
(curLine(), batch_id + 1, batch_num, num_predicted, number,
cost_time))
assert len(corpus_json_list) == index + 1
with open(FLAGS.output_file, 'w', encoding='utf-8') as writer:
json.dump(corpus_json_list, writer, ensure_ascii=False, indent=4)
cost_time = (time.time() - start_time) / 60.0
logging.info(
f'{curLine()} {num_predicted} predictions saved to:{FLAGS.output_file}, cost {cost_time} min, ave {cost_time/num_predicted} min.'
)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
flags.mark_flag_as_required('input_file')
flags.mark_flag_as_required('input_format')
flags.mark_flag_as_required('output_file')
flags.mark_flag_as_required('label_map_file')
flags.mark_flag_as_required('vocab_file')
flags.mark_flag_as_required('saved_model')
label_map = utils.read_label_map(FLAGS.label_map_file)
converter = tagging_converter.TaggingConverter(
tagging_converter.get_phrase_vocabulary_from_label_map(label_map),
FLAGS.enable_swap_tag)
builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file,
FLAGS.max_seq_length,
FLAGS.do_lower_case, converter)
predictor = predict_utils.LaserTaggerPredictor(
tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder,
label_map)
print(colored("%s input file:%s" % (curLine(), FLAGS.input_file), "red"))
predict_batch_size = 64
batch_num = 0
num_predicted = 0
with tf.gfile.Open(FLAGS.output_file, 'w') as writer:
with open(FLAGS.input_file, "r") as f:
sources_batch = []
previous_line_list = []
context_list = []
line_number = 0
start_time = time.time()
while True:
line_number += 1
line = f.readline().rstrip('\n').strip("\"").strip(" ")
if len(line) == 0:
break
column_index = line.index(",")
text = line[column_index + 1:].strip("\"") # context and query
# for charChinese_id, char in enumerate(line[column_index+1:]):
# if (char>='a' and char<='z') or (char>='A' and char<='Z'):
# continue
# else:
# break
source = remove_p(text)
if source not in text: # TODO ｉｇｎｏｒｅ的就给空字符串，这样输出也是空字符串
print(curLine(),
"line_number=%d, ignore:%s" % (line_number, text),
",source:", len(source), source)
source = ""
# continue
context_list.append(text[:text.index(source)])
previous_line_list.append(line)
sources_batch.append(source)
if len(sources_batch) == predict_batch_size:
num_predicted, batch_num = predict_and_write(
predictor, sources_batch, previous_line_list,
context_list, writer, num_predicted, start_time,
batch_num)
sources_batch = []
previous_line_list = []
context_list = []
# if num_predicted > 1000:
# break
if len(context_list) > 0:
num_predicted, batch_num = predict_and_write(
predictor, sources_batch, previous_line_list, context_list,
writer, num_predicted, start_time, batch_num)
cost_time = (time.time() - start_time) / 60.0
logging.info(
f'{curLine()} {num_predicted} predictions saved to:{FLAGS.output_file}, cost {cost_time} min, ave {cost_time / num_predicted/60} hours.'
)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
flags.mark_flag_as_required('input_file')
flags.mark_flag_as_required('input_format')
flags.mark_flag_as_required('output_file')
flags.mark_flag_as_required('label_map_file')
flags.mark_flag_as_required('vocab_file')
flags.mark_flag_as_required('saved_model')
label_map = utils.read_label_map(FLAGS.label_map_file)
converter = tagging_converter.TaggingConverter(
tagging_converter.get_phrase_vocabulary_from_label_map(label_map),
FLAGS.enable_swap_tag)
builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file,
FLAGS.max_seq_length,
FLAGS.do_lower_case, converter)
predictor = predict_utils.LaserTaggerPredictor(
tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder,
label_map)
print(colored("%s input file:%s" % (curLine(), FLAGS.input_file), "red"))
sourcesA_list = []
with open(FLAGS.input_file) as f:
for line in f:
json_map = json.loads(line.rstrip('\n'))
sourcesA_list.append(json_map["questions"])
print(curLine(), len(sourcesA_list), "sourcesA_list:", sourcesA_list[-1])
start_time = time.time()
num_predicted = 0
with tf.gfile.Open(FLAGS.output_file, 'w') as writer:
for batch_id, sources_batch in enumerate(sourcesA_list):
# sources_batch = sourcesA_list[batch_id * predict_batch_size: (batch_id + 1) * predict_batch_size]
location_batch = []
for source in sources_batch:
location = list()
for char in source[0]:
if (char >= '0' and char <= '9') or char in '.- ' or (
char >= 'a' and char <= 'z') or (char >= 'A'
and char <= 'Z'):
location.append("1") # TODO TODO
else:
location.append("0")
location_batch.append("".join(location))
prediction_batch = predictor.predict_batch(
sources_batch=sources_batch, location_batch=location_batch)
expand_list = []
for prediction in prediction_batch: # TODO
if prediction in sources_batch:
continue
expand_list.append(prediction)
json_map = {"questions": sources_batch, "expands": expand_list}
json_str = json.dumps(json_map, ensure_ascii=False)
writer.write("%s\n" % json_str)
# input(curLine())
num_predicted += len(expand_list)
if batch_id % 20 == 0:
cost_time = (time.time() - start_time) / 60.0
print(
"%s batch_id=%d/%d, predict %d/%d examples, cost %.2fmin."
% (curLine(), batch_id + 1, len(sourcesA_list),
num_predicted, num_predicted, cost_time))
cost_time = (time.time() - start_time) / 60.0
try:
nltk.download('averaged_perceptron_tagger')
except FileExistsError:
print("NLTK averaged_perceptron_tagger exist")
if embedding_type == "Normal" or embedding_type == "Sentence":
vocab_file = "gs://lasertagger_training_yechen/cased_L-12_H-768_A-12/vocab.txt"
elif embedding_type == "POS":
vocab_file = "gs://bert_traning_yechen/trained_bert_uncased/bert_POS/vocab.txt"
elif embedding_type == "POS_concise":
vocab_file = "gs://bert_traning_yechen/trained_bert_uncased/bert_POS_concise/vocab.txt"
else:
raise ValueError("Unrecognized embedding type")
label_map = utils.read_label_map(label_map_file)
converter = tagging_converter.TaggingConverter(
tagging_converter.get_phrase_vocabulary_from_label_map(label_map), True)
id_2_tag = {tag_id: tagging.Tag(tag) for tag, tag_id in label_map.items()}
builder = bert_example.BertExampleBuilder(label_map, vocab_file, 128,
do_lower_case, converter,
embedding_type, enable_masking)
grammar_vocab_file = "gs://publicly_available_models_yechen/grammar_checker/vocab.txt"
grammar_builder = bert_example_classifier.BertGrammarExampleBuilder(
grammar_vocab_file, 128, False)
def predict_json(project, model, instances, version=None):
""" Send a json object to GCP deployed model for prediction.
Args:
project: name of the project where the model is in
