def load_from_bert(vocab_file, input_file_a, input_file_b, do_lower_case=True,
max_seq_length=128, vocab_file1=None, align_file=None, n_max_sent=None,
align_punc=False, policy='1to1'):
tokenizer = tokenization.FullTokenizer(
vocab_file=vocab_file, do_lower_case=do_lower_case)
tokenizer1 = tokenization.FullTokenizer(
vocab_file=vocab_file1, do_lower_case=do_lower_case)
examples = load_bert(input_file_a, input_file_b, n_max_sent=n_max_sent)
aligns = None
if align_file:
aligns = load_aligns(align_file, n_max_sent=n_max_sent, examples=examples,
align_punc=align_punc, policy=policy)
try:
assert len(examples) == len(aligns)
except:
raise ValueError("Number of examples({}) and alignments({}) mismatch!".format(len(examples),len(aligns)))
features = convert_bert_examples_to_features(
examples=examples, seq_length=max_seq_length, tokenizer=tokenizer,
tokenizer1=tokenizer1, aligns=aligns)
unique_id_to_feature = {}
for feature in features:
unique_id_to_feature[feature.unique_id] = feature
#all_input_ids_a = torch.tensor([f.input_ids_a for f in features], dtype=torch.long)
#all_input_ids_b = torch.tensor([f.input_ids_b for f in features], dtype=torch.long)
all_input_embs_a = torch.tensor([f.input_embs_a for f in features], dtype=torch.float)
all_input_embs_b = torch.tensor([f.input_embs_b for f in features], dtype=torch.float)
all_input_mask_a = torch.tensor([f.input_mask_a for f in features], dtype=torch.long)
all_input_mask_b = torch.tensor([f.input_mask_b for f in features], dtype=torch.long)
all_example_index = torch.arange(all_input_mask_a.size(0), dtype=torch.long)
if align_file:
all_align_ids_a = torch.tensor([f.align_ids_a for f in features], dtype=torch.long)
all_align_ids_b = torch.tensor([f.align_ids_b for f in features], dtype=torch.long)
all_align_mask = torch.tensor([f.align_mask for f in features], dtype=torch.long)
dataset = TensorDataset(all_input_embs_a, all_input_mask_a,
all_input_embs_b, all_input_mask_b, all_align_ids_a, all_align_ids_b,
all_align_mask, all_example_index)
else:
dataset = TensorDataset(all_input_embs_a, all_input_mask_a,
all_input_embs_b, all_input_mask_b, all_example_index)
#if local_rank == -1:
# sampler = SequentialSampler(dataset)
#sampler = RandomSampler(dataset)
#else:
# sampler = DistributedSampler(dataset)
#dataloader = DataLoader(dataset, sampler=sampler, batch_size=batch_size)
return dataset, unique_id_to_feature, features
def convert(vocab_file,
sents,
batch_size=32,
do_lower_case=True,
max_seq_length=128,
local_rank=-1):
tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file,
do_lower_case=do_lower_case)
features = convert_sents_to_features(sents=sents,
seq_length=max_seq_length,
tokenizer=tokenizer)
unique_id_to_feature = {}
for feature in features:
unique_id_to_feature[feature.unique_id] = feature
all_input_ids = torch.tensor([f.input_ids for f in features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features],
dtype=torch.long)
all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
dataset = TensorDataset(all_input_ids, all_input_mask, all_example_index)
return dataset, unique_id_to_feature, features
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
# bpe_tokenizer = BertTokenizer.from_pretrained(
# FLAGS.bert_tokenizer_name,
# tokenize_chinese_chars=False
# )
# bpe_tokenizer.tokenize_chinese_chars = False
# print("bpe_tokenizer.tokenize_chinese_chars: ", bpe_tokenizer.tokenize_chinese_chars)
bpe_tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case,
spm_model_file=FLAGS.spm_model_file)
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)
create_training_instances(
input_files,
FLAGS.output_file,
bpe_tokenizer,
FLAGS.max_seq_length,
FLAGS.dupe_factor,
FLAGS.short_seq_prob,
FLAGS.masked_lm_prob,
FLAGS.max_predictions_per_seq,
rng,
)
def submit(model=None,
path="",
vocab_file="",
use_crf="",
label_file="",
tag_to_index=None):
"""
submit task
"""
tokenizer_ = tokenization.FullTokenizer(vocab_file=vocab_file)
data = []
for line in open(path):
if not line.strip():
continue
oneline = json.loads(line.strip())
res = process(model=model,
text=oneline["text"],
tokenizer_=tokenizer_,
use_crf=use_crf,
tag_to_index=tag_to_index,
vocab=vocab_file)
data.append(json.dumps({"label": res}, ensure_ascii=False))
open("ner_predict.json", "w").write("\n".join(data))
labels = []
with open(label_file) as f:
for label in f:
labels.append(label.strip())
get_result(labels, "ner_predict.json", path)
def do_eval(dataset=None,
vocab_file="",
eval_json="",
load_checkpoint_path="",
seq_length=384):
""" do eval """
if load_checkpoint_path == "":
raise ValueError(
"Finetune model missed, evaluation task must load finetune model!")
tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file,
do_lower_case=True)
eval_examples = read_squad_examples(eval_json, False)
eval_features = convert_examples_to_features(examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=seq_length,
doc_stride=128,
max_query_length=64,
is_training=False,
output_fn=None,
verbose_logging=False)
net = BertSquad(bert_net_cfg, False, 2)
net.set_train(False)
param_dict = load_checkpoint(load_checkpoint_path)
load_param_into_net(net, param_dict)
model = Model(net)
output = []
RawResult = collections.namedtuple(
"RawResult", ["unique_id", "start_logits", "end_logits"])
columns_list = ["input_ids", "input_mask", "segment_ids", "unique_ids"]
for data in dataset.create_dict_iterator():
input_data = []
for i in columns_list:
input_data.append(Tensor(data[i]))
input_ids, input_mask, segment_ids, unique_ids = input_data
start_positions = Tensor([1], mstype.float32)
end_positions = Tensor([1], mstype.float32)
is_impossible = Tensor([1], mstype.float32)
logits = model.predict(input_ids, input_mask, segment_ids,
start_positions, end_positions, unique_ids,
is_impossible)
ids = logits[0].asnumpy()
start = logits[1].asnumpy()
end = logits[2].asnumpy()
for i in range(bert_net_cfg.batch_size):
unique_id = int(ids[i])
start_logits = [float(x) for x in start[i].flat]
end_logits = [float(x) for x in end[i].flat]
output.append(
RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
write_predictions(eval_examples, eval_features, output, 20, 30, True,
"./predictions.json", None, None)
def test_eval():
"""Evaluation function for SQuAD task"""
tokenizer = tokenization.FullTokenizer(vocab_file="./vocab.txt",
do_lower_case=True)
input_file = "dataset/v1.1/dev-v1.1.json"
eval_examples = read_squad_examples(input_file, False)
eval_features = convert_examples_to_features(examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=384,
doc_stride=128,
max_query_length=64,
is_training=False,
output_fn=None,
verbose_logging=False)
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE,
device_target='Ascend',
device_id=device_id)
dataset = get_squad_dataset(bert_net_cfg.batch_size, 1)
net = BertSquad(bert_net_cfg, False, 2)
net.set_train(False)
param_dict = load_checkpoint(cfg.finetune_ckpt)
load_param_into_net(net, param_dict)
model = Model(net)
output = []
RawResult = collections.namedtuple(
"RawResult", ["unique_id", "start_logits", "end_logits"])
columns_list = ["input_ids", "input_mask", "segment_ids", "unique_ids"]
for data in dataset.create_dict_iterator():
input_data = []
for i in columns_list:
input_data.append(Tensor(data[i]))
input_ids, input_mask, segment_ids, unique_ids = input_data
start_positions = Tensor([1], mstype.float32)
end_positions = Tensor([1], mstype.float32)
is_impossible = Tensor([1], mstype.float32)
logits = model.predict(input_ids, input_mask, segment_ids,
start_positions, end_positions, unique_ids,
is_impossible)
ids = logits[0].asnumpy()
start = logits[1].asnumpy()
end = logits[2].asnumpy()
for i in range(bert_net_cfg.batch_size):
unique_id = int(ids[i])
start_logits = [float(x) for x in start[i].flat]
end_logits = [float(x) for x in end[i].flat]
output.append(
RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
write_predictions(eval_examples, eval_features, output, 20, 30, True,
"./predictions.json", None, None, False, False)
def submit(model=None,
path="",
vocab_file="",
use_crf="",
label_file="",
tag_to_index=None):
"""
submit task
"""
tokenizer_ = tokenization.FullTokenizer(vocab_file=vocab_file)
data = []
if cfg.schema_file is not None:
f1 = open(cfg.schema_file, 'r')
numRows = json.load(f1)
up_num = numRows["numRows"]
else:
up_num = 600000000000
num = 0
for line in open(path):
num = num + 1
if num > up_num:
break
if not line.strip():
continue
oneline = json.loads(line.strip())
if cfg.task == 'Classification':
res = process(model=model,
text=oneline["sentence"],
tokenizer_=tokenizer_,
use_crf=use_crf,
tag_to_index=tag_to_index,
vocab=vocab_file)
print("text", oneline["sentence"])
elif cfg.task == 'NER':
res = process(model=model,
text=oneline["text"],
tokenizer_=tokenizer_,
use_crf=use_crf,
tag_to_index=tag_to_index,
vocab=vocab_file)
print("text", oneline["text"])
else:
raise Exception("Task error")
print("res:", res)
f.write("result: " + str(res) + '\n')
data.append(json.dumps({"label": res}, ensure_ascii=False))
f.close()
def submit(model=None, path="", vocab_file="", use_crf="", label2id_file=""):
"""
submit task
"""
tokenizer_ = tokenization.FullTokenizer(vocab_file=vocab_file)
data = []
for line in open(path):
if not line.strip():
continue
oneline = json.loads(line.strip())
res = process(model=model,
text=oneline["text"],
tokenizer_=tokenizer_,
use_crf=use_crf,
label2id_file=label2id_file)
print("text", oneline["text"])
print("res:", res)
data.append(json.dumps({"label": res}, ensure_ascii=False))
open("ner_predict.json", "w").write("\n".join(data))
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = {
"chn": data_processors.ChnSentiCorpDataProcessor,
"lcqmc": data_processors.LCQMCProcessor,
"xnli": data_processors.XnliProcessor,
"book_review": data_processors.BookReviewProcessor,
"shopping": data_processors.ShoppingProcessor,
"weibo": data_processors.WeiboProcessor,
"law_qa": data_processors.LawQAProcessor,
"nlpcc_dbqa": data_processors.NlpccDbqaProcessor,
}
# 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."
)
if not FLAGS.albert_config_file and not FLAGS.albert_hub_module_handle:
raise ValueError("At least one of `--albert_config_file` and "
"`--albert_hub_module_handle` must be set")
if FLAGS.albert_config_file:
albert_config = modeling.AlbertConfig.from_json_file(
FLAGS.albert_config_file)
if FLAGS.max_seq_length > albert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the ALBERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, albert_config.max_position_embeddings))
else:
albert_config = None # Get the config from TF-Hub.
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](FLAGS)
label_list = processor.get_labels()
# bpe_tokenizer = BertTokenizer.from_pretrained(
# FLAGS.bert_tokenizer_name,
# tokenize_chinese_chars=False
# )
# bpe_tokenizer.tokenize_chinese_chars = False
# print("bpe_tokenizer.tokenize_chinese_chars: ", bpe_tokenizer.tokenize_chinese_chars)
bpe_tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case,
spm_model_file=FLAGS.spm_model_file)
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
if FLAGS.do_train:
iterations_per_loop = int(
min(FLAGS.iterations_per_loop, FLAGS.save_checkpoints_steps))
else:
iterations_per_loop = FLAGS.iterations_per_loop
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=int(FLAGS.save_checkpoints_steps),
keep_checkpoint_max=0,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
train_examples = None
if FLAGS.do_train:
train_examples = processor.get_train_examples(FLAGS.data_dir)
model_fn = classifier_utils.model_fn_builder(
albert_config=albert_config,
num_labels=len(label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=FLAGS.train_step,
num_warmup_steps=FLAGS.warmup_step,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu,
task_name=task_name,
hub_module=FLAGS.albert_hub_module_handle,
optimizer=FLAGS.optimizer)
# 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:
cached_dir = FLAGS.cached_dir
if not cached_dir:
cached_dir = FLAGS.output_dir
train_file = os.path.join(cached_dir, task_name + "_train.tf_record")
# if not tf.gfile.Exists(train_file):
classifier_utils.file_based_convert_examples_to_features(
train_examples,
label_list,
FLAGS.max_seq_length,
bpe_tokenizer,
train_file,
task_name,
)
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", FLAGS.train_step)
train_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True,
task_name=task_name,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.train_batch_size)
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.train_step)
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(data_processors.PaddingInputExample())
cached_dir = FLAGS.cached_dir
if not cached_dir:
cached_dir = FLAGS.output_dir
eval_file = os.path.join(cached_dir, task_name + "_eval.tf_record")
# if not tf.gfile.Exists(eval_file):
classifier_utils.file_based_convert_examples_to_features(
eval_examples,
label_list,
FLAGS.max_seq_length,
bpe_tokenizer,
eval_file,
task_name,
)
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 = classifier_utils.file_based_input_fn_builder(
input_file=eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder,
task_name=task_name,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.eval_batch_size)
best_trial_info_file = os.path.join(FLAGS.output_dir, "best_trial.txt")
def _best_trial_info():
"""Returns information about which checkpoints have been evaled so far."""
if tf.gfile.Exists(best_trial_info_file):
with tf.gfile.GFile(best_trial_info_file, "r") as best_info:
global_step, best_metric_global_step, metric_value = (
best_info.read().split(":"))
global_step = int(global_step)
best_metric_global_step = int(best_metric_global_step)
metric_value = float(metric_value)
else:
metric_value = -1
best_metric_global_step = -1
global_step = -1
tf.logging.info(
"Best trial info: Step: %s, Best Value Step: %s, "
"Best Value: %s", global_step, best_metric_global_step,
metric_value)
return global_step, best_metric_global_step, metric_value
def _remove_checkpoint(checkpoint_path):
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tf.logging.info("removing {}".format(src_ckpt))
tf.gfile.Remove(src_ckpt)
def _find_valid_cands(curr_step):
filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
candidates = []
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
idx = ckpt_name.split("-")[-1]
if int(idx) > curr_step:
candidates.append(filename)
return candidates
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
if task_name == "sts-b":
key_name = "pearson"
elif task_name == "cola":
key_name = "matthew_corr"
elif task_name == "nlpcc_dbqa":
key_name = "f1_score"
else:
key_name = "eval_accuracy"
global_step, best_perf_global_step, best_perf = _best_trial_info()
writer = tf.gfile.GFile(output_eval_file, "w")
while global_step < FLAGS.train_step:
steps_and_files = {}
filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
cur_filename = os.path.join(FLAGS.output_dir, ckpt_name)
if cur_filename.split("-")[-1] == "best":
continue
gstep = int(cur_filename.split("-")[-1])
if gstep not in steps_and_files:
tf.logging.info(
"Add {} to eval list.".format(cur_filename))
steps_and_files[gstep] = cur_filename
tf.logging.info("found {} files.".format(len(steps_and_files)))
if not steps_and_files:
tf.logging.info(
"found 0 file, global step: {}. Sleeping.".format(
global_step))
time.sleep(60)
else:
for checkpoint in sorted(steps_and_files.items()):
step, checkpoint_path = checkpoint
if global_step >= step:
if (best_perf_global_step != step
and len(_find_valid_cands(step)) > 1):
_remove_checkpoint(checkpoint_path)
continue
result = estimator.evaluate(
input_fn=eval_input_fn,
steps=eval_steps,
checkpoint_path=checkpoint_path)
global_step = result["global_step"]
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])))
writer.write("best = {}\n".format(best_perf))
if result[key_name] > best_perf:
best_perf = result[key_name]
best_perf_global_step = global_step
elif len(_find_valid_cands(global_step)) > 1:
_remove_checkpoint(checkpoint_path)
writer.write("=" * 50 + "\n")
writer.flush()
with tf.gfile.GFile(best_trial_info_file,
"w") as best_info:
best_info.write("{}:{}:{}".format(
global_step, best_perf_global_step, best_perf))
writer.close()
# output_eval_file_local = os.path.join("./tmp/", "_".join(output_eval_file.split("/")[4:]))
# tf.gfile.Copy(output_eval_file, output_eval_file_local, overwrite=True)
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = "model.ckpt-{}.{}".format(best_perf_global_step, ext)
tgt_ckpt = "model.ckpt-best.{}".format(ext)
tf.logging.info("saving {} to {}".format(src_ckpt, tgt_ckpt))
tf.io.gfile.rename(os.path.join(FLAGS.output_dir, src_ckpt),
os.path.join(FLAGS.output_dir, tgt_ckpt),
overwrite=True)
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(data_processors.PaddingInputExample())
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
classifier_utils.file_based_convert_examples_to_features(
predict_examples,
label_list,
FLAGS.max_seq_length,
bpe_tokenizer,
predict_file,
task_name,
)
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 = classifier_utils.file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=predict_drop_remainder,
task_name=task_name,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.predict_batch_size)
checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
result = estimator.predict(input_fn=predict_input_fn,
checkpoint_path=checkpoint_path)
output_predict_file = os.path.join(FLAGS.output_dir,
"test_results.tsv")
output_submit_file = os.path.join(FLAGS.output_dir,
"submit_results.tsv")
with tf.gfile.GFile(output_predict_file, "w") as pred_writer, \
tf.gfile.GFile(output_submit_file, "w") as sub_writer:
sub_writer.write("index" + "\t" + "prediction\n")
num_written_lines = 0
tf.logging.info("***** Predict results *****")
for (i, (example, prediction)) in \
enumerate(zip(predict_examples, result)):
probabilities = prediction["probabilities"]
if i >= num_actual_predict_examples:
break
output_line = "\t".join(
str(class_probability)
for class_probability in probabilities) + "\n"
pred_writer.write(output_line)
if task_name != "sts-b":
actual_label = label_list[int(prediction["predictions"])]
else:
actual_label = str(prediction["predictions"])
sub_writer.write(example.guid + "\t" + actual_label + "\n")
num_written_lines += 1
assert num_written_lines == num_actual_predict_examples
truth_json_dir = os.path.join(FLAGS.data_dir, "test.json")
pred_tsv_dir = output_submit_file
accuracy, results = cal_metrics(pred_tsv_dir, truth_json_dir)
tf.logging.info("***** Predict metrics *****")
tf.logging.info("***** Predict metrics *****")
tf.logging.info("accuracy: %f" % accuracy)
tf.logging.info("results: ")
tf.logging.info(json.dumps(results, ensure_ascii=False, indent=2))
tf.logging.info("***** Predict metrics *****")
tf.logging.info("***** Predict metrics *****")
def run_squad():
"""run squad task"""
parser = argparse.ArgumentParser(description="run squad")
parser.add_argument("--device_target",
type=str,
default="Ascend",
choices=["Ascend", "GPU"],
help="Device type, default is Ascend")
parser.add_argument("--do_train",
type=str,
default="false",
choices=["true", "false"],
help="Eable train, default is false")
parser.add_argument("--do_eval",
type=str,
default="false",
choices=["true", "false"],
help="Eable eval, default is false")
parser.add_argument("--device_id",
type=int,
default=0,
help="Device id, default is 0.")
parser.add_argument("--epoch_num",
type=int,
default=3,
help="Epoch number, default is 1.")
parser.add_argument("--num_class",
type=int,
default=2,
help="The number of class, default is 2.")
parser.add_argument("--train_data_shuffle",
type=str,
default="true",
choices=["true", "false"],
help="Enable train data shuffle, default is true")
parser.add_argument("--eval_data_shuffle",
type=str,
default="false",
choices=["true", "false"],
help="Enable eval data shuffle, default is false")
parser.add_argument("--train_batch_size",
type=int,
default=32,
help="Train batch size, default is 32")
parser.add_argument("--eval_batch_size",
type=int,
default=1,
help="Eval batch size, default is 1")
parser.add_argument("--vocab_file_path",
type=str,
default="",
help="Vocab file path")
parser.add_argument("--eval_json_path",
type=str,
default="",
help="Evaluation json file path, can be eval.json")
parser.add_argument("--save_finetune_checkpoint_path",
type=str,
default="",
help="Save checkpoint path")
parser.add_argument("--load_pretrain_checkpoint_path",
type=str,
default="",
help="Load checkpoint file path")
parser.add_argument("--load_finetune_checkpoint_path",
type=str,
default="",
help="Load checkpoint file path")
parser.add_argument("--train_data_file_path",
type=str,
default="",
help="Data path, it is better to use absolute path")
parser.add_argument("--schema_file_path",
type=str,
default="",
help="Schema path, it is better to use absolute path")
args_opt = parser.parse_args()
epoch_num = args_opt.epoch_num
load_pretrain_checkpoint_path = args_opt.load_pretrain_checkpoint_path
save_finetune_checkpoint_path = args_opt.save_finetune_checkpoint_path
load_finetune_checkpoint_path = args_opt.load_finetune_checkpoint_path
if args_opt.do_train.lower() == "false" and args_opt.do_eval.lower(
) == "false":
raise ValueError(
"At least one of 'do_train' or 'do_eval' must be true")
if args_opt.do_train.lower(
) == "true" and args_opt.train_data_file_path == "":
raise ValueError(
"'train_data_file_path' must be set when do finetune task")
if args_opt.do_eval.lower() == "true":
if args_opt.vocab_file_path == "":
raise ValueError(
"'vocab_file_path' must be set when do evaluation task")
if args_opt.eval_json_path == "":
raise ValueError(
"'tokenization_file_path' must be set when do evaluation task")
target = args_opt.device_target
if target == "Ascend":
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
device_id=args_opt.device_id)
elif target == "GPU":
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
if bert_net_cfg.compute_type != mstype.float32:
logger.warning('GPU only support fp32 temporarily, run with fp32.')
bert_net_cfg.compute_type = mstype.float32
else:
raise Exception("Target error, GPU or Ascend is supported.")
netwithloss = BertSquad(bert_net_cfg, True, 2, dropout_prob=0.1)
if args_opt.do_train.lower() == "true":
ds = create_squad_dataset(
batch_size=args_opt.train_batch_size,
repeat_count=1,
data_file_path=args_opt.train_data_file_path,
schema_file_path=args_opt.schema_file_path,
do_shuffle=(args_opt.train_data_shuffle.lower() == "true"))
do_train(ds, netwithloss, load_pretrain_checkpoint_path,
save_finetune_checkpoint_path, epoch_num)
if args_opt.do_eval.lower() == "true":
if save_finetune_checkpoint_path == "":
load_finetune_checkpoint_dir = _cur_dir
else:
load_finetune_checkpoint_dir = make_directory(
save_finetune_checkpoint_path)
load_finetune_checkpoint_path = LoadNewestCkpt(
load_finetune_checkpoint_dir, ds.get_dataset_size(), epoch_num,
"squad")
if args_opt.do_eval.lower() == "true":
from src import tokenization
from src.create_squad_data import read_squad_examples, convert_examples_to_features
from src.squad_get_predictions import write_predictions
from src.squad_postprocess import SQuad_postprocess
tokenizer = tokenization.FullTokenizer(
vocab_file=args_opt.vocab_file_path, do_lower_case=True)
eval_examples = read_squad_examples(args_opt.eval_json_path, False)
eval_features = convert_examples_to_features(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=bert_net_cfg.seq_length,
doc_stride=128,
max_query_length=64,
is_training=False,
output_fn=None,
vocab_file=args_opt.vocab_file_path)
ds = create_squad_dataset(
batch_size=args_opt.eval_batch_size,
repeat_count=1,
data_file_path=eval_features,
schema_file_path=args_opt.schema_file_path,
is_training=False,
do_shuffle=(args_opt.eval_data_shuffle.lower() == "true"))
outputs = do_eval(ds, load_finetune_checkpoint_path,
args_opt.eval_batch_size)
all_predictions = write_predictions(eval_examples, eval_features,
outputs, 20, 30, True)
SQuad_postprocess(args_opt.eval_json_path,
all_predictions,
output_metrics="output.json")
else:
sent_new_ += char_
# drop redundent blank
sent_new_ = drop_extra_blank(sent_new_)
return sent_new_
def tokenize_single_sent(sent, tokenizer=None):
# sent = proc_single_sent(sent)
line_seg = tokenizer.tokenize(sent)
return line_seg
if __name__ == "__main__":
bpe_tokenizer = tokenization.FullTokenizer(
vocab_file=
"data_proc/tokenizers/sentencepiece/char_no_space-21128-clean.vocab",
do_lower_case=True,
spm_model_file=
"data_proc/tokenizers/sentencepiece/char_no_space-21128-clean.model")
sent = "我喜欢篮球"
line_seg = tokenize_single_sent(sent, tokenizer=bpe_tokenizer)
line_seg = ["[CLS]"] + line_seg + ["[SEP]"]
print(line_seg)
print(" ".join(line_seg))
# [CLS] ▁我 喜欢 篮球 [SEP]
