aboutsummaryrefslogtreecommitdiffstats
path: root/scripts
diff options
context:
space:
mode:
Diffstat (limited to 'scripts')
-rw-r--r--scripts/api_squad.py1045
-rw-r--r--scripts/api_squad_offline.py258
-rw-r--r--scripts/api_squad_online.py108
-rw-r--r--scripts/create_squad_features.py746
-rw-r--r--scripts/global_setting.py36
-rw-r--r--scripts/load_model.sh6
6 files changed, 2199 insertions, 0 deletions
diff --git a/scripts/api_squad.py b/scripts/api_squad.py
new file mode 100644
index 0000000..239bbd6
--- /dev/null
+++ b/scripts/api_squad.py
@@ -0,0 +1,1045 @@
+# coding=utf-8
+# squad interface
+# Required parameters：
+# FLAGS_output_dir ：the output path of the model training during training process, the output of the trained model, etc.; the output path of the model prediction during predicting process
+# FLAGS_init_checkpoint_squad ： model initialization path, use bert pre-trained model for training; use the output path during training for prediction
+# FLAGS_predict_file ： the file to be predicted, csv file
+# FLAGS_train_file : file to be trained, csv file
+# FLAGS_do_predict : whether to predict or not
+# FLAGS_do_train ： whether to train or not
+# FLAGS_train_batch_size ： the batch_size for training, default : 16
+# FLAGS_predict_batch_size : the batch_size when predicting, default: 8
+# FLAGS_learning_rate ： the learning_rate at training time, default: 5e-5
+# FLAGS_num_train_epochs ： epochs at training time, default: 3
+# FLAGS_max_answer_length ： the maximum length of the answer, default: 100 characters
+# FLAGS_max_query_length ： the maximum length of the question, default: 64
+# FLAGS_version_2_with_negative ： whether there is no answer to the question, default false, must be set to False when reasoning
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import json
+import math
+import os
+import random
+import modeling
+import optimization
+import tokenization
+import six
+import tensorflow as tf
+import pandas as pd
+from global_setting import FLAGS_bert_config_file, FLAGS_vocab_file, FLAGS_init_checkpoint_squad
+
+
+
+
+FLAGS_max_seq_length = 512
+FLAGS_do_lower_case = True
+FLAGS_doc_stride = 128
+
+
+FLAGS_save_checkpoints_steps = 1000
+FLAGS_iterations_per_loop = 1000
+FLAGS_n_best_size = 20
+FLAGS_tpu_zone = None
+FLAGS_tpu_name = None
+FLAGS_num_tpu_cores = 8
+FLAGS_verbose_logging = False
+FLAGS_master = None
+FLAGS_use_tpu = False
+FLAGS_warmup_proportion = 0.1
+FLAGS_gcp_project = None
+FLAGS_null_score_diff_threshold = 0.0
+
+def make_json(input_file,questions):
+ print(input_file)
+ data_train = pd.read_excel(input_file)
+ print(444)
+ data_train.fillna(0,inplace=True)
+ data_train.index = [i for i in range(len(data_train))]
+ question = questions
+ res = {}
+ res['data'] = []
+ data_inside = {}
+ for i in data_train.index:
+ data_inside['title'] = 'Not available'
+ data_inside['paragraphs'] = []
+ paragraphs_inside = {}
+ paragraphs_inside['context'] = data_train.loc[i,'text']
+ paragraphs_inside['qas'] = []
+ for ques in question:
+ qas_inside = {}
+ qas_inside['answers'] = []
+ if data_train.loc[i,ques]:
+ answer_inside = {}
+ answer_inside['text'] = str(data_train.loc[i,ques])
+ answer_inside['answer_start'] = paragraphs_inside['context'].find(answer_inside['text'])
+ qas_inside['is_impossible'] = 0
+
+ else:
+ qas_inside['is_impossible'] = 1
+ answer_inside = {}
+ qas_inside['id'] = str(i) + ques
+ qas_inside['question'] = ques
+ qas_inside['answers'].append(answer_inside.copy())
+ paragraphs_inside['qas'].append(qas_inside.copy())
+ data_inside['paragraphs'].append(paragraphs_inside.copy())
+
+ res['data'].append(data_inside.copy())
+ print('make json done')
+ return json.dumps(res)
+
+
+
+
+class SquadExample(object):
+ """A single training/test example for simple sequence classification.
+
+ For examples without an answer, the start and end position are -1.
+ """
+
+ def __init__(self,
+ qas_id,
+ question_text,
+ doc_tokens,
+ orig_answer_text=None,
+ start_position=None,
+ end_position=None,
+ is_impossible=False):
+ self.qas_id = qas_id
+ self.question_text = question_text
+ self.doc_tokens = doc_tokens
+ self.orig_answer_text = orig_answer_text
+ self.start_position = start_position
+ self.end_position = end_position
+ self.is_impossible = is_impossible
+
+ def __str__(self):
+ return self.__repr__()
+
+ def __repr__(self):
+ s = ""
+ s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
+ s += ", question_text: %s" % (
+ tokenization.printable_text(self.question_text))
+ s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
+ if self.start_position:
+ s += ", start_position: %d" % (self.start_position)
+ if self.start_position:
+ s += ", end_position: %d" % (self.end_position)
+ if self.start_position:
+ s += ", is_impossible: %r" % (self.is_impossible)
+ return s
+
+
+class InputFeatures(object):
+ """A single set of features of data."""
+
+ def __init__(self,
+ unique_id,
+ example_index,
+ doc_span_index,
+ tokens,
+ token_to_orig_map,
+ token_is_max_context,
+ input_ids,
+ input_mask,
+ segment_ids,
+ start_position=None,
+ end_position=None,
+ is_impossible=None):
+ self.unique_id = unique_id
+ self.example_index = example_index
+ self.doc_span_index = doc_span_index
+ self.tokens = tokens
+ self.token_to_orig_map = token_to_orig_map
+ self.token_is_max_context = token_is_max_context
+ self.input_ids = input_ids
+ self.input_mask = input_mask
+ self.segment_ids = segment_ids
+ self.start_position = start_position
+ self.end_position = end_position
+ self.is_impossible = is_impossible
+
+
+def read_squad_examples(input_file, is_training,questions,FLAGS_version_2_with_negative):
+ """Read a SQuAD json file into a list of SquadExample."""
+ data = make_json(input_file,questions)
+ input_data = json.loads(data)["data"]
+
+ def is_whitespace(c):
+ if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
+ return True
+ return False
+
+ examples = []
+ for entry in input_data:
+ for paragraph in entry["paragraphs"]:
+ paragraph_text = paragraph["context"]
+ doc_tokens = []
+ char_to_word_offset = []
+ prev_is_whitespace = True
+ for c in paragraph_text:
+ if is_whitespace(c):
+ prev_is_whitespace = True
+ else:
+ if prev_is_whitespace:
+ doc_tokens.append(c)
+ else:
+ doc_tokens[-1] += c
+ prev_is_whitespace = False
+ char_to_word_offset.append(len(doc_tokens) - 1)
+
+ for qa in paragraph["qas"]:
+ qas_id = qa["id"]
+ question_text = qa["question"]
+ start_position = None
+ end_position = None
+ orig_answer_text = None
+ is_impossible = False
+ if is_training:
+
+ if FLAGS_version_2_with_negative:
+ is_impossible = qa["is_impossible"]
+ if (len(qa["answers"]) != 1) and (not is_impossible):
+ raise ValueError(
+ "For training, each question should have exactly 1 answer.")
+ if not is_impossible:
+ answer = qa["answers"][0]
+ orig_answer_text = answer["text"]
+ answer_offset = answer["answer_start"]
+ answer_length = len(orig_answer_text)
+ start_position = char_to_word_offset[answer_offset]
+ end_position = char_to_word_offset[answer_offset + answer_length -
+ 1]
+ # Only add answers where the text can be exactly recovered from the
+ # document. If this CAN'T happen it's likely due to weird Unicode
+ # stuff so we will just skip the example.
+ #
+ # Note that this means for training mode, every example is NOT
+ # guaranteed to be preserved.
+ actual_text = " ".join(
+ doc_tokens[start_position:(end_position + 1)])
+ cleaned_answer_text = " ".join(
+ tokenization.whitespace_tokenize(orig_answer_text))
+ if actual_text.find(cleaned_answer_text) == -1:
+ tf.logging.warning("Could not find answer: '%s' vs. '%s'",
+ actual_text, cleaned_answer_text)
+ continue
+ else:
+ start_position = -1
+ end_position = -1
+ orig_answer_text = ""
+
+ example = SquadExample(
+ qas_id=qas_id,
+ question_text=question_text,
+ doc_tokens=doc_tokens,
+ orig_answer_text=orig_answer_text,
+ start_position=start_position,
+ end_position=end_position,
+ is_impossible=is_impossible)
+ examples.append(example)
+
+ return examples
+
+
+def convert_examples_to_features(examples, tokenizer, max_seq_length,
+ doc_stride, max_query_length, is_training,
+ output_fn):
+ """Loads a data file into a list of `InputBatch`s."""
+
+ unique_id = 1000000000
+
+ for (example_index, example) in enumerate(examples):
+ query_tokens = tokenizer.tokenize(example.question_text)
+
+ if len(query_tokens) > max_query_length:
+ query_tokens = query_tokens[0:max_query_length]
+
+ tok_to_orig_index = []
+ orig_to_tok_index = []
+ all_doc_tokens = []
+ for (i, token) in enumerate(example.doc_tokens):
+ orig_to_tok_index.append(len(all_doc_tokens))
+ sub_tokens = tokenizer.tokenize(token)
+ for sub_token in sub_tokens:
+ tok_to_orig_index.append(i)
+ all_doc_tokens.append(sub_token)
+
+ tok_start_position = None
+ tok_end_position = None
+ if is_training and example.is_impossible:
+ tok_start_position = -1
+ tok_end_position = -1
+ if is_training and not example.is_impossible:
+ tok_start_position = orig_to_tok_index[example.start_position]
+ if example.end_position < len(example.doc_tokens) - 1:
+ tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
+ else:
+ tok_end_position = len(all_doc_tokens) - 1
+ (tok_start_position, tok_end_position) = _improve_answer_span(
+ all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
+ example.orig_answer_text)
+
+ # The -3 accounts for [CLS], [SEP] and [SEP]
+ max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
+
+ # We can have documents that are longer than the maximum sequence length.
+ # To deal with this we do a sliding window approach, where we take chunks
+ # of the up to our max length with a stride of `doc_stride`.
+ _DocSpan = collections.namedtuple( # pylint: disable=invalid-name
+ "DocSpan", ["start", "length"])
+ doc_spans = []
+ start_offset = 0
+ while start_offset < len(all_doc_tokens):
+ length = len(all_doc_tokens) - start_offset
+ if length > max_tokens_for_doc:
+ length = max_tokens_for_doc
+ doc_spans.append(_DocSpan(start=start_offset, length=length))
+ if start_offset + length == len(all_doc_tokens):
+ break
+ start_offset += min(length, doc_stride)
+
+ for (doc_span_index, doc_span) in enumerate(doc_spans):
+ tokens = []
+ token_to_orig_map = {}
+ token_is_max_context = {}
+ segment_ids = []
+ tokens.append("[CLS]")
+ segment_ids.append(0)
+ for token in query_tokens:
+ tokens.append(token)
+ segment_ids.append(0)
+ tokens.append("[SEP]")
+ segment_ids.append(0)
+
+ for i in range(doc_span.length):
+ split_token_index = doc_span.start + i
+ token_to_orig_map[len(
+ tokens)] = tok_to_orig_index[split_token_index]
+
+ is_max_context = _check_is_max_context(doc_spans, doc_span_index,
+ split_token_index)
+ token_is_max_context[len(tokens)] = is_max_context
+ tokens.append(all_doc_tokens[split_token_index])
+ segment_ids.append(1)
+ tokens.append("[SEP]")
+ segment_ids.append(1)
+
+ input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+ # The mask has 1 for real tokens and 0 for padding tokens. Only real
+ # tokens are attended to.
+ input_mask = [1] * len(input_ids)
+
+ # Zero-pad up to the sequence length.
+ while len(input_ids) < max_seq_length:
+ input_ids.append(0)
+ input_mask.append(0)
+ segment_ids.append(0)
+
+ assert len(input_ids) == max_seq_length
+ assert len(input_mask) == max_seq_length
+ assert len(segment_ids) == max_seq_length
+
+ start_position = None
+ end_position = None
+ if is_training and not example.is_impossible:
+ # For training, if our document chunk does not contain an annotation
+ # we throw it out, since there is nothing to predict.
+ doc_start = doc_span.start
+ doc_end = doc_span.start + doc_span.length - 1
+ out_of_span = False
+ if not (tok_start_position >= doc_start and
+ tok_end_position <= doc_end):
+ out_of_span = True
+ if out_of_span:
+ start_position = 0
+ end_position = 0
+ else:
+ doc_offset = len(query_tokens) + 2
+ start_position = tok_start_position - doc_start + doc_offset
+ end_position = tok_end_position - doc_start + doc_offset
+
+ if is_training and example.is_impossible:
+ start_position = 0
+ end_position = 0
+
+ if example_index < 20:
+ tf.logging.info("*** Example ***")
+ tf.logging.info("unique_id: %s" % (unique_id))
+ tf.logging.info("example_index: %s" % (example_index))
+ tf.logging.info("doc_span_index: %s" % (doc_span_index))
+ tf.logging.info("tokens: %s" % " ".join(
+ [tokenization.printable_text(x) for x in tokens]))
+ tf.logging.info("token_to_orig_map: %s" % " ".join(
+ ["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
+ tf.logging.info("token_is_max_context: %s" % " ".join([
+ "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
+ ]))
+ tf.logging.info("input_ids: %s" %
+ " ".join([str(x) for x in input_ids]))
+ tf.logging.info(
+ "input_mask: %s" % " ".join([str(x) for x in input_mask]))
+ tf.logging.info(
+ "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
+ if is_training and example.is_impossible:
+ tf.logging.info("impossible example")
+ if is_training and not example.is_impossible:
+ answer_text = " ".join(
+ tokens[start_position:(end_position + 1)])
+ tf.logging.info("start_position: %d" % (start_position))
+ tf.logging.info("end_position: %d" % (end_position))
+ tf.logging.info(
+ "answer: %s" % (tokenization.printable_text(answer_text)))
+
+ feature = InputFeatures(
+ unique_id=unique_id,
+ example_index=example_index,
+ doc_span_index=doc_span_index,
+ tokens=tokens,
+ token_to_orig_map=token_to_orig_map,
+ token_is_max_context=token_is_max_context,
+ input_ids=input_ids,
+ input_mask=input_mask,
+ segment_ids=segment_ids,
+ start_position=start_position,
+ end_position=end_position,
+ is_impossible=example.is_impossible)
+
+ # Run callback
+ output_fn(feature)
+
+ unique_id += 1
+
+
+def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
+ orig_answer_text):
+ """Returns tokenized answer spans that better match the annotated answer."""
+
+ # The SQuAD annotations are character based. We first project them to
+ # whitespace-tokenized words. But then after WordPiece tokenization, we can
+ # often find a "better match". For example:
+ #
+ # Question: What year was John Smith born?
+ # Context: The leader was John Smith (1895-1943).
+ # Answer: 1895
+ #
+ # The original whitespace-tokenized answer will be "(1895-1943).". However
+ # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
+ # the exact answer, 1895.
+ #
+ # However, this is not always possible. Consider the following:
+ #
+ # Question: What country is the top exporter of electornics?
+ # Context: The Japanese electronics industry is the lagest in the world.
+ # Answer: Japan
+ #
+ # In this case, the annotator chose "Japan" as a character sub-span of
+ # the word "Japanese". Since our WordPiece tokenizer does not split
+ # "Japanese", we just use "Japanese" as the annotation. This is fairly rare
+ # in SQuAD, but does happen.
+ tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
+
+ for new_start in range(input_start, input_end + 1):
+ for new_end in range(input_end, new_start - 1, -1):
+ text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
+ if text_span == tok_answer_text:
+ return (new_start, new_end)
+
+ return (input_start, input_end)
+
+
+def _check_is_max_context(doc_spans, cur_span_index, position):
+ """Check if this is the 'max context' doc span for the token."""
+
+ # Because of the sliding window approach taken to scoring documents, a single
+ # token can appear in multiple documents. E.g.
+ # Doc: the man went to the store and bought a gallon of milk
+ # Span A: the man went to the
+ # Span B: to the store and bought
+ # Span C: and bought a gallon of
+ # ...
+ #
+ # Now the word 'bought' will have two scores from spans B and C. We only
+ # want to consider the score with "maximum context", which we define as
+ # the *minimum* of its left and right context (the *sum* of left and
+ # right context will always be the same, of course).
+ #
+ # In the example the maximum context for 'bought' would be span C since
+ # it has 1 left context and 3 right context, while span B has 4 left context
+ # and 0 right context.
+ best_score = None
+ best_span_index = None
+ for (span_index, doc_span) in enumerate(doc_spans):
+ end = doc_span.start + doc_span.length - 1
+ if position < doc_span.start:
+ continue
+ if position > end:
+ continue
+ num_left_context = position - doc_span.start
+ num_right_context = end - position
+ score = min(num_left_context, num_right_context) + \
+ 0.01 * doc_span.length
+ if best_score is None or score > best_score:
+ best_score = score
+ best_span_index = span_index
+
+ return cur_span_index == best_span_index
+
+
+def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
+ use_one_hot_embeddings):
+ """Creates a classification model."""
+ model = modeling.BertModel(
+ config=bert_config,
+ is_training=is_training,
+ input_ids=input_ids,
+ input_mask=input_mask,
+ token_type_ids=segment_ids,
+ use_one_hot_embeddings=use_one_hot_embeddings)
+
+ final_hidden = model.get_sequence_output()
+
+ final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)
+ batch_size = final_hidden_shape[0]
+ seq_length = final_hidden_shape[1]
+ hidden_size = final_hidden_shape[2]
+
+ output_weights = tf.get_variable(
+ "cls/squad/output_weights", [2, hidden_size],
+ initializer=tf.truncated_normal_initializer(stddev=0.02))
+
+ output_bias = tf.get_variable(
+ "cls/squad/output_bias", [2], initializer=tf.zeros_initializer())
+
+ final_hidden_matrix = tf.reshape(final_hidden,
+ [batch_size * seq_length, hidden_size])
+ logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)
+ logits = tf.nn.bias_add(logits, output_bias)
+
+ logits = tf.reshape(logits, [batch_size, seq_length, 2])
+ logits = tf.transpose(logits, [2, 0, 1])
+
+ unstacked_logits = tf.unstack(logits, axis=0)
+
+ (start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1])
+
+ return (start_logits, end_logits)
+
+
+def model_fn_builder(bert_config, init_checkpoint, learning_rate,
+ num_train_steps, num_warmup_steps, use_tpu,
+ use_one_hot_embeddings):
+ """Returns `model_fn` closure for TPUEstimator."""
+
+ def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
+ """The `model_fn` for TPUEstimator."""
+
+ tf.logging.info("*** Features ***")
+ for name in sorted(features.keys()):
+ tf.logging.info(" name = %s, shape = %s" %
+ (name, features[name].shape))
+
+ unique_ids = features["unique_ids"]
+ input_ids = features["input_ids"]
+ input_mask = features["input_mask"]
+ segment_ids = features["segment_ids"]
+
+ is_training = (mode == tf.estimator.ModeKeys.TRAIN)
+
+ (start_logits, end_logits) = create_model(
+ bert_config=bert_config,
+ is_training=is_training,
+ input_ids=input_ids,
+ input_mask=input_mask,
+ segment_ids=segment_ids,
+ use_one_hot_embeddings=use_one_hot_embeddings)
+
+ tvars = tf.trainable_variables()
+
+ initialized_variable_names = {}
+ scaffold_fn = None
+ if init_checkpoint:
+ (assignment_map, initialized_variable_names
+ ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
+ if use_tpu:
+
+ def tpu_scaffold():
+ tf.train.init_from_checkpoint(
+ init_checkpoint, assignment_map)
+ return tf.train.Scaffold()
+
+ scaffold_fn = tpu_scaffold
+ else:
+ tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
+
+ tf.logging.info("**** Trainable Variables ****")
+ for var in tvars:
+ init_string = ""
+ if var.name in initialized_variable_names:
+ init_string = ", *INIT_FROM_CKPT*"
+ tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
+ init_string)
+
+ output_spec = None
+ if mode == tf.estimator.ModeKeys.TRAIN:
+ seq_length = modeling.get_shape_list(input_ids)[1]
+
+ def compute_loss(logits, positions):
+ one_hot_positions = tf.one_hot(
+ positions, depth=seq_length, dtype=tf.float32)
+ log_probs = tf.nn.log_softmax(logits, axis=-1)
+ loss = -tf.reduce_mean(
+ tf.reduce_sum(one_hot_positions * log_probs, axis=-1))
+ return loss
+
+ start_positions = features["start_positions"]
+ end_positions = features["end_positions"]
+
+ start_loss = compute_loss(start_logits, start_positions)
+ end_loss = compute_loss(end_logits, end_positions)
+
+ total_loss = (start_loss + end_loss) / 2.0
+
+ train_op = optimization.create_optimizer(
+ total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
+
+ output_spec = tf.contrib.tpu.TPUEstimatorSpec(
+ mode=mode,
+ loss=total_loss,
+ train_op=train_op,
+ scaffold_fn=scaffold_fn)
+ elif mode == tf.estimator.ModeKeys.PREDICT:
+ predictions = {
+ # "unique_ids": unique_ids,
+ "start_logits": start_logits,
+ "end_logits": end_logits,
+ }
+ output_spec = tf.contrib.tpu.TPUEstimatorSpec(
+ mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
+ else:
+ raise ValueError(
+ "Only TRAIN and PREDICT modes are supported: %s" % (mode))
+
+ return output_spec
+
+ return model_fn
+
+
+def input_fn_builder(input_file, seq_length, is_training, drop_remainder):
+ """Creates an `input_fn` closure to be passed to TPUEstimator."""
+
+ name_to_features = {
+ "unique_ids": tf.FixedLenFeature([], tf.int64),
+ "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
+ "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
+ "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
+ }
+
+ if is_training:
+ name_to_features["start_positions"] = tf.FixedLenFeature([], tf.int64)
+ name_to_features["end_positions"] = tf.FixedLenFeature([], tf.int64)
+
+ def _decode_record(record, name_to_features):
+ """Decodes a record to a TensorFlow example."""
+ example = tf.parse_single_example(record, name_to_features)
+
+ # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
+ # So cast all int64 to int32.
+ for name in list(example.keys()):
+ t = example[name]
+ if t.dtype == tf.int64:
+ t = tf.to_int32(t)
+ example[name] = t
+
+ return example
+
+ def input_fn(params):
+ """The actual input function."""
+ batch_size = params["batch_size"]
+
+ # For training, we want a lot of parallel reading and shuffling.
+ # For eval, we want no shuffling and parallel reading doesn't matter.
+ d = tf.data.TFRecordDataset(input_file)
+ if is_training:
+ d = d.repeat()
+ d = d.shuffle(buffer_size=100)
+
+ d = d.apply(
+ tf.contrib.data.map_and_batch(
+ lambda record: _decode_record(record, name_to_features),
+ batch_size=batch_size,
+ drop_remainder=drop_remainder))
+
+ return d
+
+ return input_fn
+
+
+RawResult = collections.namedtuple("RawResult",
+ ["unique_id", "start_logits", "end_logits"])
+
+
+def write_predictions(all_examples, all_features, all_results, n_best_size,
+ max_answer_length, do_lower_case, output_prediction_file,
+ output_nbest_file, output_null_log_odds_file):
+ """Write final predictions to the json file and log-odds of null if needed."""
+ tf.logging.info("Writing predictions to: %s" % (output_prediction_file))
+ tf.logging.info("Writing nbest to: %s" % (output_nbest_file))
+
+ example_index_to_features = collections.defaultdict(list)
+ for feature in all_features:
+ example_index_to_features[feature.example_index].append(feature)
+
+ unique_id_to_result = {}
+ for result in all_results:
+ unique_id_to_result[result.unique_id] = result
+
+ _PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
+ "PrelimPrediction",
+ ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
+
+ all_predictions = collections.OrderedDict()
+ all_nbest_json = collections.OrderedDict()
+ scores_diff_json = collections.OrderedDict()
+
+ for (example_index, example) in enumerate(all_examples):
+ features = example_index_to_features[example_index]
+
+ prelim_predictions = []
+ # keep track of the minimum score of null start+end of position 0
+ score_null = 1000000 # large and positive
+ min_null_feature_index = 0 # the paragraph slice with min mull score
+ null_start_logit = 0 # the start logit at the slice with min null score
+ null_end_logit = 0 # the end logit at the slice with min null score
+ for (feature_index, feature) in enumerate(features):
+ result = unique_id_to_result[feature.unique_id]
+ start_indexes = _get_best_indexes(result.start_logits, n_best_size)
+ end_indexes = _get_best_indexes(result.end_logits, n_best_size)
+ # if we could have irrelevant answers, get the min score of irrelevant
+ if FLAGS_version_2_with_negative:
+ feature_null_score = result.start_logits[0] + \
+ result.end_logits[0]
+ if feature_null_score < score_null:
+ score_null = feature_null_score
+ min_null_feature_index = feature_index
+ null_start_logit = result.start_logits[0]
+ null_end_logit = result.end_logits[0]
+ for start_index in start_indexes:
+ for end_index in end_indexes:
+ # We could hypothetically create invalid predictions, e.g., predict
+ # that the start of the span is in the question. We throw out all
+ # invalid predictions.
+ if start_index >= len(feature.tokens):
+ continue
+ if end_index >= len(feature.tokens):
+ continue
+ if start_index not in feature.token_to_orig_map:
+ continue
+ if end_index not in feature.token_to_orig_map:
+ continue
+ if not feature.token_is_max_context.get(start_index, False):
+ continue
+ if end_index < start_index:
+ continue
+ length = end_index - start_index + 1
+ if length > max_answer_length:
+ continue
+ prelim_predictions.append(
+ _PrelimPrediction(
+ feature_index=feature_index,
+ start_index=start_index,
+ end_index=end_index,
+ start_logit=result.start_logits[start_index],
+ end_logit=result.end_logits[end_index]))
+
+ prelim_predictions = sorted(
+ prelim_predictions,
+ key=lambda x: (x.start_logit + x.end_logit),
+ reverse=True)
+
+ _NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
+ "NbestPrediction", ["text", "start_logit", "end_logit"])
+
+ seen_predictions = {}
+ nbest = []
+ for pred in prelim_predictions:
+ if len(nbest) >= n_best_size:
+ break
+ feature = features[pred.feature_index]
+ if pred.start_index > 0: # this is a non-null prediction
+ tok_tokens = feature.tokens[pred.start_index:(
+ pred.end_index + 1)]
+ orig_doc_start = feature.token_to_orig_map[pred.start_index]
+ orig_doc_end = feature.token_to_orig_map[pred.end_index]
+ orig_tokens = example.doc_tokens[orig_doc_start:(
+ orig_doc_end + 1)]
+ tok_text = " ".join(tok_tokens)
+
+ # De-tokenize WordPieces that have been split off.
+ tok_text = tok_text.replace(" ##", "")
+ tok_text = tok_text.replace("##", "")
+
+ # Clean whitespace
+ tok_text = tok_text.strip()
+ tok_text = " ".join(tok_text.split())
+ orig_text = " ".join(orig_tokens)
+
+ final_text = get_final_text(tok_text, orig_text, do_lower_case)
+ if final_text in seen_predictions:
+ continue
+
+ seen_predictions[final_text] = True
+ else:
+ final_text = ""
+ seen_predictions[final_text] = True
+
+ nbest.append(
+ _NbestPrediction(
+ text=final_text,
+ start_logit=pred.start_logit,
+ end_logit=pred.end_logit))
+
+ # In very rare edge cases we could have no valid predictions. So we
+ # just create a nonce prediction in this case to avoid failure.
+ if not nbest:
+ nbest.append(
+ _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
+
+ assert len(nbest) >= 1
+
+ total_scores = []
+ best_non_null_entry = None
+ for entry in nbest:
+ total_scores.append(entry.start_logit + entry.end_logit)
+ if not best_non_null_entry:
+ if entry.text:
+ best_non_null_entry = entry
+
+ probs = _compute_softmax(total_scores)
+
+ nbest_json = []
+ for (i, entry) in enumerate(nbest):
+ output = collections.OrderedDict()
+ output["text"] = entry.text
+ output["probability"] = probs[i]
+ output["start_logit"] = entry.start_logit
+ output["end_logit"] = entry.end_logit
+ nbest_json.append(output)
+
+ assert len(nbest_json) >= 1
+
+ all_predictions[example.qas_id] = nbest_json[0]["text"]
+
+ all_nbest_json[example.qas_id] = nbest_json
+
+ with tf.gfile.GFile(output_prediction_file, "w") as writer:
+ writer.write(json.dumps(all_predictions, indent=4) + "\n")
+
+
+def get_final_text(pred_text, orig_text, do_lower_case):
+ """Project the tokenized prediction back to the original text."""
+
+ # When we created the data, we kept track of the alignment between original
+ # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
+ # now `orig_text` contains the span of our original text corresponding to the
+ # span that we predicted.
+ #
+ # However, `orig_text` may contain extra characters that we don't want in
+ # our prediction.
+ #
+ # For example, let's say:
+ # pred_text = steve smith
+ # orig_text = Steve Smith's
+ #
+ # We don't want to return `orig_text` because it contains the extra "'s".
+ #
+ # We don't want to return `pred_text` because it's already been normalized
+ # (the SQuAD eval script also does punctuation stripping/lower casing but
+ # our tokenizer does additional normalization like stripping accent
+ # characters).
+ #
+ # What we really want to return is "Steve Smith".
+ #
+ # Therefore, we have to apply a semi-complicated alignment heruistic between
+ # `pred_text` and `orig_text` to get a character-to-charcter alignment. This
+ # can fail in certain cases in which case we just return `orig_text`.
+
+ def _strip_spaces(text):
+ ns_chars = []
+ ns_to_s_map = collections.OrderedDict()
+ for (i, c) in enumerate(text):
+ if c == " ":
+ continue
+ ns_to_s_map[len(ns_chars)] = i
+ ns_chars.append(c)
+ ns_text = "".join(ns_chars)
+ return (ns_text, ns_to_s_map)
+
+ # We first tokenize `orig_text`, strip whitespace from the result
+ # and `pred_text`, and check if they are the same length. If they are
+ # NOT the same length, the heuristic has failed. If they are the same
+ # length, we assume the characters are one-to-one aligned.
+ tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
+
+ tok_text = " ".join(tokenizer.tokenize(orig_text))
+
+ start_position = tok_text.find(pred_text)
+ if start_position == -1:
+ if FLAGS_verbose_logging:
+ tf.logging.info(
+ "Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
+ return orig_text
+ end_position = start_position + len(pred_text) - 1
+
+ (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
+ (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
+
+ if len(orig_ns_text) != len(tok_ns_text):
+ if FLAGS_verbose_logging:
+ tf.logging.info("Length not equal after stripping spaces: '%s' vs '%s'",
+ orig_ns_text, tok_ns_text)
+ return orig_text
+
+ # We then project the characters in `pred_text` back to `orig_text` using
+ # the character-to-character alignment.
+ tok_s_to_ns_map = {}
+ for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
+ tok_s_to_ns_map[tok_index] = i
+
+ orig_start_position = None
+ if start_position in tok_s_to_ns_map:
+ ns_start_position = tok_s_to_ns_map[start_position]
+ if ns_start_position in orig_ns_to_s_map:
+ orig_start_position = orig_ns_to_s_map[ns_start_position]
+
+ if orig_start_position is None:
+ if FLAGS_verbose_logging:
+ tf.logging.info("Couldn't map start position")
+ return orig_text
+
+ orig_end_position = None
+ if end_position in tok_s_to_ns_map:
+ ns_end_position = tok_s_to_ns_map[end_position]
+ if ns_end_position in orig_ns_to_s_map:
+ orig_end_position = orig_ns_to_s_map[ns_end_position]
+
+ if orig_end_position is None:
+ if FLAGS_verbose_logging:
+ tf.logging.info("Couldn't map end position")
+ return orig_text
+
+ output_text = orig_text[orig_start_position:(orig_end_position + 1)]
+ return output_text
+
+
+def _get_best_indexes(logits, n_best_size):
+ """Get the n-best logits from a list."""
+ index_and_score = sorted(
+ enumerate(logits), key=lambda x: x[1], reverse=True)
+
+ best_indexes = []
+ for i in range(len(index_and_score)):
+ if i >= n_best_size:
+ break
+ best_indexes.append(index_and_score[i][0])
+ return best_indexes
+
+
+def _compute_softmax(scores):
+ """Compute softmax probability over raw logits."""
+ if not scores:
+ return []
+
+ max_score = None
+ for score in scores:
+ if max_score is None or score > max_score:
+ max_score = score
+
+ exp_scores = []
+ total_sum = 0.0
+ for score in scores:
+ x = math.exp(score - max_score)
+ exp_scores.append(x)
+ total_sum += x
+
+ probs = []
+ for score in exp_scores:
+ probs.append(score / total_sum)
+ return probs
+
+
+class FeatureWriter(object):
+ """Writes InputFeature to TF example file."""
+
+ def __init__(self, filename, is_training):
+ self.filename = filename
+ self.is_training = is_training
+ self.num_features = 0
+ self._writer = tf.python_io.TFRecordWriter(filename)
+
+ def process_feature(self, feature):
+ """Write a InputFeature to the TFRecordWriter as a tf.train.Example."""
+ self.num_features += 1
+
+ def create_int_feature(values):
+ feature = tf.train.Feature(
+ int64_list=tf.train.Int64List(value=list(values)))
+ return feature
+
+ features = collections.OrderedDict()
+ features["unique_ids"] = create_int_feature([feature.unique_id])
+ features["input_ids"] = create_int_feature(feature.input_ids)
+ features["input_mask"] = create_int_feature(feature.input_mask)
+ features["segment_ids"] = create_int_feature(feature.segment_ids)
+
+ if self.is_training:
+ features["start_positions"] = create_int_feature(
+ [feature.start_position])
+ features["end_positions"] = create_int_feature(
+ [feature.end_position])
+ impossible = 0
+ if feature.is_impossible:
+ impossible = 1
+ features["is_impossible"] = create_int_feature([impossible])
+
+ tf_example = tf.train.Example(
+ features=tf.train.Features(feature=features))
+ self._writer.write(tf_example.SerializeToString())
+
+ def close(self):
+ self._writer.close()
+
+
+def validate_flags_or_throw(bert_config):
+ """Validate the input FLAGS or throw an exception."""
+ tokenization.validate_case_matches_checkpoint(FLAGS_do_lower_case,
+ FLAGS_init_checkpoint_squad)
+
+ # if not FLAGS_do_train and not FLAGS_do_predict:
+ # raise ValueError(
+ # "At least one of `do_train` or `do_predict` must be True.")
+
+ # if FLAGS_do_train:
+ # if not FLAGS_train_file:
+ # raise ValueError(
+ # "If `do_train` is True, then `train_file` must be specified.")
+ # if FLAGS_do_predict:
+ # if not FLAGS_predict_file:
+ # raise ValueError(
+ # "If `do_predict` is True, then `predict_file` must be specified.")
+
+ # 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))
+
+ # if FLAGS_max_seq_length <= FLAGS_max_query_length + 3:
+ # raise ValueError(
+ # "The max_seq_length (%d) must be greater than max_query_length "
+ # "(%d) + 3" % (FLAGS_max_seq_length, FLAGS_max_query_length))
diff --git a/scripts/api_squad_offline.py b/scripts/api_squad_offline.py
new file mode 100644
index 0000000..1c98a10
--- /dev/null
+++ b/scripts/api_squad_offline.py
@@ -0,0 +1,258 @@
+
+#!/usr/bin/env python
+# coding: utf-8
+
+# auther = 'liuzhiyong'
+# date = 20201204
+
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import json
+import datetime
+import threading
+import time
+from flask import Flask, abort, request, jsonify
+from concurrent.futures import ThreadPoolExecutor
+
+import collections
+import math
+import os
+import random
+import modeling
+import optimization
+import tokenization
+import six
+import tensorflow as tf
+import sys
+from api_squad import *
+from global_setting import *
+from global_setting import FLAGS_bert_config_file, FLAGS_vocab_file, FLAGS_init_checkpoint_squad, questions
+
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = str(CUDA_VISIBLE_DEVICES)
+
+app = Flask(__name__)
+
+def serving_input_fn():
+ input_ids = tf.placeholder(tf.int32, [None, FLAGS_max_seq_length], name='input_ids')
+ unique_id = tf.placeholder(tf.int32,[None])
+ input_mask = tf.placeholder(tf.int32, [None, FLAGS_max_seq_length], name='input_mask')
+ segment_ids = tf.placeholder(tf.int32, [None, FLAGS_max_seq_length], name='segment_ids')
+ input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({
+ 'input_ids': input_ids,
+ 'input_mask': input_mask,
+ 'segment_ids': segment_ids,
+ 'unique_ids': unique_id,
+ })()
+ return input_fn
+
+def main(FLAGS_output_dir, FLAGS_init_checkpoint_squad, FLAGS_export_dir, FLAGS_predict_file=None, FLAGS_train_file=None, FLAGS_do_predict=False,
+ FLAGS_do_train=False, FLAGS_train_batch_size=16, FLAGS_predict_batch_size=8, FLAGS_learning_rate=5e-5, FLAGS_num_train_epochs=3.0,
+ FLAGS_max_answer_length=100, FLAGS_max_query_length=64, FLAGS_version_2_with_negative=False):
+
+ 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 = tf.contrib.cluster_resolver.TPUClusterResolver(
+ FLAGS_tpu_name, zone=FLAGS_tpu_zone, project=FLAGS_gcp_project)
+
+ is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
+ run_config = tf.contrib.tpu.RunConfig(
+ cluster=tpu_cluster_resolver,
+ master=FLAGS_master,
+ model_dir=FLAGS_output_dir,
+ save_checkpoints_steps=FLAGS_save_checkpoints_steps,
+ tpu_config=tf.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,questions = questions,FLAGS_version_2_with_negative = FLAGS_version_2_with_negative)
+
+ 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_squad,
+ 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 = tf.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)
+ estimator._export_to_tpu = False
+ estimator.export_savedmodel(FLAGS_export_dir, serving_input_fn)
+ return 'success'
+
+
+class AI2Flask:
+
+ def __init__(self, port=5000, workers=4):
+ self.app = app
+ self.port = port
+ p = ThreadPoolExecutor(max_workers=workers)
+ threads_mapping = {}
+
+ def check_threads():
+ flag = False
+ pop_keys = set()
+ if len(threads_mapping) >= workers:
+ for k, v in threads_mapping.items():
+ if v.running():
+ flag = True
+ else:
+ pop_keys.add(k)
+
+ for k in pop_keys:
+ threads_mapping.pop(k)
+
+ return flag
+
+ @app.route('/api/offline/train', methods=['POST'])
+ def text_analyse():
+ if not request.json or not 'task_id' in request.json:
+ abort(400)
+ if check_threads():
+ return jsonify({"Des": "Task list is full. Can not submit new task! ", "Result": "Failed to submit the training task ", "Status": "ERROR"})
+
+ else:
+ try:
+ FLAGS_train_batch_size = request.json['FLAGS_train_batch_size']
+ except:
+ FLAGS_train_batch_size = 16
+ try:
+ FLAGS_learning_rate = request.json['FLAGS_learning_rate']
+ except:
+ FLAGS_learning_rate = 5e-5
+ try:
+ FLAGS_num_train_epochs = request.json['FLAGS_num_train_epochs']
+ except:
+ FLAGS_num_train_epochs = 3.0
+ try:
+ FLAGS_max_answer_length = request.json['FLAGS_max_answer_length']
+ except:
+ FLAGS_max_answer_length = 100
+ try:
+ FLAGS_max_query_length = request.json['FLAGS_max_query_length']
+ except:
+ FLAGS_max_query_length = 64
+ try:
+ FLAGS_version_2_with_negative = request.json['FLAGS_version_2_with_negative']
+ except:
+ FLAGS_version_2_with_negative = True
+
+ try:
+ FLAGS_predict_file = None
+ FLAGS_predict_batch_size = 8
+ FLAGS_do_predict = False
+ FLAGS_do_train = True
+ FLAGS_output_dir = request.json['FLAGS_output_dir']
+ FLAGS_train_file = request.json['FLAGS_train_file']
+ FLAGS_export_dir = request.json['FLAGS_export_dir']
+ task_id = request.json['task_id']
+
+ task = p.submit(main, FLAGS_output_dir, FLAGS_init_checkpoint_squad, FLAGS_export_dir, FLAGS_predict_file, FLAGS_train_file, FLAGS_do_predict,
+ FLAGS_do_train, FLAGS_train_batch_size, FLAGS_predict_batch_size, FLAGS_learning_rate, FLAGS_num_train_epochs,
+ FLAGS_max_answer_length, FLAGS_max_query_length, FLAGS_version_2_with_negative)
+ threads_mapping[task_id] = task
+
+ return jsonify({"message": "Task submitted successfully", "status": "0"})
+
+ except KeyError as e:
+ return jsonify({"Des": 'KeyError: {}'.format(str(e)), "Result": 'None', "Status": "Error"})
+ except Exception as e:
+ return jsonify({"Des": str(e), "Result": 'None', "Status": "Error"})
+
+
+
+ @app.route('/api/offline/status', methods=['POST'])
+ def todo_status():
+ task_id = request.json['task_id']
+ task = threads_mapping.get(task_id, None)
+ try:
+ if task is None:
+ return jsonify({'Des': 'The task was not found', 'Status': 'ERROR'})
+ else:
+ if task.done():
+ print(task.result)
+ if task.result() == 'success':
+ return jsonify({'Des': 'DONE', 'Status': 'OK'})
+ else:
+ return jsonify({'Des': 'Program execution error. Please check the execution log ', 'Status': 'ERROR'})
+
+ else:
+ return jsonify({'Des': 'RUNNING', 'Status': 'OK'})
+ except Exception as e:
+ return jsonify({'Des': str(e), 'Status': 'ERROR'})
+
+ def start(self):
+ self.app.run(host="0.0.0.0", port=self.port, threaded=True)
+
+
+if __name__ == '__main__':
+ port = sys.argv[1]
+ AI2Flask(port=port).start()
diff --git a/scripts/api_squad_online.py b/scripts/api_squad_online.py
new file mode 100644
index 0000000..9cc6b08
--- /dev/null
+++ b/scripts/api_squad_online.py
@@ -0,0 +1,108 @@
+
+#!/usr/bin/env python
+# coding: utf-8
+
+# auther = 'liuzhiyong'
+# date = 20201204
+
+
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import json
+import datetime
+import threading
+import sys
+from flask import Flask, abort, request, jsonify
+from concurrent.futures import ThreadPoolExecutor
+
+import collections
+import math
+import os
+import random
+import modeling
+import optimization
+import tokenization
+import six
+import tensorflow as tf
+import pandas as pd
+import numpy as np
+import requests
+from global_setting import *
+from create_squad_features import get_squad_feature_result
+
+
+app = Flask(__name__)
+
+
+class AI2Flask:
+
+ def __init__(self, port=5000,workers=4):
+ self.app = app
+ self.port = port
+
+
+
+ @app.route('/api/online/predict', methods=['POST'])
+ def text_analyse():
+ if not request.json:
+ abort(400)
+
+ else:
+ try:
+ try:
+ title = request.json['title']
+ except:
+ title = 'Not available'
+ text_origin = request.json['text']
+
+
+ if len(text_origin) > 800:
+ text = text_origin[:800]
+ else:
+ text = text_origin
+
+ result = {}
+ for ques in questions:
+ tmp = get_squad_feature_result(title=title,text=text,tokenizer=tokenizer_ch,question=[ques],url='http://localhost:8502/v1/models/predict:predict')
+ result[ques] = dict(tmp)[ques]
+
+
+ print('finished!!')
+ return json.dumps(result)
+
+
+ except KeyError as e:
+ return jsonify({"Des": 'KeyError: {}'.format(str(e)), "Result": 'None', "Status": "Error"})
+ except Exception as e:
+ return jsonify({"Des": str(e), "Result": 'None', "Status": "Error"})
+
+
+
+
+
+
+ @app.route('/api/online/load', methods=['POST'])
+ def load_model():
+ if not request.json:
+ abort(400)
+ else:
+ try:
+ path = request.json['path']
+ flag = os.system('./load_model.sh ' + path + ' ' + CUDA_VISIBLE_DEVICES)
+ if flag == 0:
+ return jsonify({"Des": "Model loaded successfully !", "Status": "OK"})
+ else:
+ return jsonify({"Des": "Model loaded failed , check the logs !", "Status": "Error"})
+ except Exception as e:
+ return jsonify({"Des": str(e), "Status": "Error"})
+
+ def start(self):
+ self.app.run(host="0.0.0.0", port=self.port, threaded=True)
+
+
+if __name__ == '__main__':
+ port = sys.argv[1]
+ AI2Flask(port=port).start()
+
diff --git a/scripts/create_squad_features.py b/scripts/create_squad_features.py
new file mode 100644
index 0000000..e779b9e
--- /dev/null
+++ b/scripts/create_squad_features.py
@@ -0,0 +1,746 @@
+
+ #!/usr/bin/env python
+ # coding: utf-8
+
+ # auther = 'liuzhiyong'
+ # date = 20201204
+
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import json
+import datetime
+import threading
+import time
+from flask import Flask, abort, request, jsonify
+from concurrent.futures import ThreadPoolExecutor
+
+import collections
+import math
+import os
+import random
+import modeling
+import optimization
+import tokenization
+import six
+import tensorflow as tf
+import sys
+import requests
+
+from global_setting import *
+
+version_2_with_negative = True
+
+def get_squad_feature_result(title,text,tokenizer,question, url):
+
+ def make_json(title, text, question):
+ res = {}
+ res['data'] = []
+ data_inside = {}
+
+ data_inside['title'] = title
+ data_inside['paragraphs'] = []
+ paragraphs_inside = {}
+ paragraphs_inside['context'] = text
+ paragraphs_inside['qas'] = []
+ for ques in question:
+ qas_inside = {}
+ qas_inside['answers'] = []
+
+ answer_inside = {}
+
+ qas_inside['id'] = ques
+ qas_inside['question'] = ques
+ qas_inside['answers'].append(answer_inside.copy())
+ paragraphs_inside['qas'].append(qas_inside.copy())
+ data_inside['paragraphs'].append(paragraphs_inside.copy())
+
+ res['data'].append(data_inside.copy())
+ return json.dumps(res)
+
+
+ def _compute_softmax(scores):
+ """Compute softmax probability over raw logits."""
+ if not scores:
+ return []
+
+ max_score = None
+ for score in scores:
+ if max_score is None or score > max_score:
+ max_score = score
+
+ exp_scores = []
+ total_sum = 0.0
+ for score in scores:
+ x = math.exp(score - max_score)
+ exp_scores.append(x)
+ total_sum += x
+
+ probs = []
+ for score in exp_scores:
+ probs.append(score / total_sum)
+ return probs
+
+ def get_final_text(pred_text, orig_text, do_lower_case):
+
+ def _strip_spaces(text):
+ ns_chars = []
+ ns_to_s_map = collections.OrderedDict()
+ for (i, c) in enumerate(text):
+ if c == " ":
+ continue
+ ns_to_s_map[len(ns_chars)] = i
+ ns_chars.append(c)
+ ns_text = "".join(ns_chars)
+ return (ns_text, ns_to_s_map)
+
+ # We first tokenize `orig_text`, strip whitespace from the result
+ # and `pred_text`, and check if they are the same length. If they are
+ # NOT the same length, the heuristic has failed. If they are the same
+ # length, we assume the characters are one-to-one aligned.
+ tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
+
+ tok_text = " ".join(tokenizer.tokenize(orig_text))
+
+ start_position = tok_text.find(pred_text)
+ if start_position == -1:
+ if 0:
+ tf.logging.info(
+ "Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
+ return orig_text
+ end_position = start_position + len(pred_text) - 1
+
+ (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
+ (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
+
+ if len(orig_ns_text) != len(tok_ns_text):
+ if 0:
+ tf.logging.info("Length not equal after stripping spaces: '%s' vs '%s'",
+ orig_ns_text, tok_ns_text)
+ return orig_text
+
+ # We then project the characters in `pred_text` back to `orig_text` using
+ # the character-to-character alignment.
+ tok_s_to_ns_map = {}
+ for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
+ tok_s_to_ns_map[tok_index] = i
+
+ orig_start_position = None
+ if start_position in tok_s_to_ns_map:
+ ns_start_position = tok_s_to_ns_map[start_position]
+ if ns_start_position in orig_ns_to_s_map:
+ orig_start_position = orig_ns_to_s_map[ns_start_position]
+
+ if orig_start_position is None:
+ if 0:
+ tf.logging.info("Couldn't map start position")
+ return orig_text
+
+ orig_end_position = None
+ if end_position in tok_s_to_ns_map:
+ ns_end_position = tok_s_to_ns_map[end_position]
+ if ns_end_position in orig_ns_to_s_map:
+ orig_end_position = orig_ns_to_s_map[ns_end_position]
+
+ if orig_end_position is None:
+ if 0:
+ tf.logging.info("Couldn't map end position")
+ return orig_text
+
+ output_text = orig_text[orig_start_position:(orig_end_position + 1)]
+ return output_text
+
+ def _get_best_indexes(logits, n_best_size):
+
+ index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
+
+ best_indexes = []
+ for i in range(len(index_and_score)):
+ if i >= n_best_size:
+ break
+ best_indexes.append(index_and_score[i][0])
+ return best_indexes
+
+ RawResult = collections.namedtuple("RawResult", ["unique_id", "start_logits", "end_logits"])
+
+ def write_predictions(all_examples, all_features, all_results, n_best_size,
+ max_answer_length, do_lower_case):
+ """Write final predictions to the json file and log-odds of null if needed."""
+
+ example_index_to_features = collections.defaultdict(list)
+ for feature in all_features:
+ example_index_to_features[feature.example_index].append(feature)
+
+ unique_id_to_result = {}
+ for result in all_results:
+ unique_id_to_result[result.unique_id] = result
+
+ _PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
+ "PrelimPrediction",
+ ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
+
+ all_predictions = collections.OrderedDict()
+ all_nbest_json = collections.OrderedDict()
+ scores_diff_json = collections.OrderedDict()
+
+ for (example_index, example) in enumerate(all_examples):
+ features = example_index_to_features[example_index]
+
+ prelim_predictions = []
+ # keep track of the minimum score of null start+end of position 0
+ score_null = 1000000 # large and positive
+ min_null_feature_index = 0 # the paragraph slice with min mull score
+ null_start_logit = 0 # the start logit at the slice with min null score
+ null_end_logit = 0 # the end logit at the slice with min null score
+ for (feature_index, feature) in enumerate(features):
+ result = unique_id_to_result[feature.unique_id]
+ start_indexes = _get_best_indexes(result.start_logits, n_best_size)
+ end_indexes = _get_best_indexes(result.end_logits, n_best_size)
+ # if we could have irrelevant answers, get the min score of irrelevant
+ if version_2_with_negative:
+ feature_null_score = result.start_logits[0] + result.end_logits[0]
+ if feature_null_score < score_null:
+ score_null = feature_null_score
+ min_null_feature_index = feature_index
+ null_start_logit = result.start_logits[0]
+ null_end_logit = result.end_logits[0]
+
+ for start_index in start_indexes:
+ for end_index in end_indexes:
+ # We could hypothetically create invalid predictions, e.g., predict
+ # that the start of the span is in the question. We throw out all
+ # invalid predictions.
+ if start_index >= len(feature.tokens):
+ continue
+ if end_index >= len(feature.tokens):
+ continue
+ if start_index not in feature.token_to_orig_map:
+ continue
+ if end_index not in feature.token_to_orig_map:
+ continue
+ if not feature.token_is_max_context.get(start_index, False):
+ continue
+ if end_index < start_index:
+ continue
+ length = end_index - start_index + 1
+ if length > max_answer_length:
+ continue
+ prelim_predictions.append(
+ _PrelimPrediction(
+ feature_index=feature_index,
+ start_index=start_index,
+ end_index=end_index,
+ start_logit=result.start_logits[start_index],
+ end_logit=result.end_logits[end_index]))
+
+ if version_2_with_negative:
+ prelim_predictions.append(
+ _PrelimPrediction(
+ feature_index=min_null_feature_index,
+ start_index=0,
+ end_index=0,
+ start_logit=null_start_logit,
+ end_logit=null_end_logit))
+ prelim_predictions = sorted(
+ prelim_predictions,
+ key=lambda x: (x.start_logit + x.end_logit),
+ reverse=True)
+
+ _NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
+ "NbestPrediction", ["text", "start_logit", "end_logit"])
+
+ seen_predictions = {}
+ nbest = []
+ for pred in prelim_predictions:
+ if len(nbest) >= n_best_size:
+ break
+ feature = features[pred.feature_index]
+ if pred.start_index > 0: # this is a non-null prediction
+ tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
+ orig_doc_start = feature.token_to_orig_map[pred.start_index]
+ orig_doc_end = feature.token_to_orig_map[pred.end_index]
+ orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
+ tok_text = " ".join(tok_tokens)
+
+ # De-tokenize WordPieces that have been split off.
+ tok_text = tok_text.replace(" ##", "")
+ tok_text = tok_text.replace("##", "")
+
+ # Clean whitespace
+ tok_text = tok_text.strip()
+ tok_text = " ".join(tok_text.split())
+ orig_text = " ".join(orig_tokens)
+
+ final_text = get_final_text(tok_text, orig_text, do_lower_case)
+ if final_text in seen_predictions:
+ continue
+
+ seen_predictions[final_text] = True
+ else:
+ final_text = ""
+ seen_predictions[final_text] = True
+
+ nbest.append(
+ _NbestPrediction(
+ text=final_text,
+ start_logit=pred.start_logit,
+ end_logit=pred.end_logit))
+
+ # if we didn't inlude the empty option in the n-best, inlcude it
+ if version_2_with_negative:
+ if "" not in seen_predictions:
+ nbest.append(
+ _NbestPrediction(
+ text="", start_logit=null_start_logit,
+ end_logit=null_end_logit))
+
+ # In very rare edge cases we could have no valid predictions. So we
+ # just create a nonce prediction in this case to avoid failure.
+ if not nbest:
+ nbest.append(
+ _NbestPrediction(text="", start_logit=0.0, end_logit=0.0))
+
+ assert len(nbest) >= 1
+
+ total_scores = []
+ best_non_null_entry = None
+ for entry in nbest:
+ total_scores.append(entry.start_logit + entry.end_logit)
+ if not best_non_null_entry:
+ if entry.text:
+ best_non_null_entry = entry
+
+ probs = _compute_softmax(total_scores)
+
+ nbest_json = []
+ for (i, entry) in enumerate(nbest):
+ output = collections.OrderedDict()
+ output["text"] = entry.text
+ output["probability"] = probs[i]
+ output["start_logit"] = entry.start_logit
+ output["end_logit"] = entry.end_logit
+ nbest_json.append(output)
+
+ assert len(nbest_json) >= 1
+
+ if not version_2_with_negative:
+ all_predictions[example.qas_id] = nbest_json[0]["text"]
+ else:
+ # predict "" iff the null score - the score of best non-null > threshold
+ score_diff = score_null - best_non_null_entry.start_logit - (
+ best_non_null_entry.end_logit)
+ scores_diff_json[example.qas_id] = score_diff
+ if score_diff > 0:
+ all_predictions[example.qas_id] = ""
+ else:
+ all_predictions[example.qas_id] = best_non_null_entry.text
+
+ all_nbest_json[example.qas_id] = nbest_json
+ return all_predictions
+
+
+ def create_int_feature(values):
+
+ feature = tf.train.Feature(
+ int64_list=tf.train.Int64List(value=list(values)))
+ return feature
+
+
+ class InputFeatures(object):
+ """A single set of features of data."""
+
+ def __init__(self,
+ unique_id,
+ example_index,
+ doc_span_index,
+ tokens,
+ token_to_orig_map,
+ token_is_max_context,
+ input_ids,
+ input_mask,
+ segment_ids,
+ start_position=None,
+ end_position=None,
+ is_impossible=None):
+ self.unique_id = unique_id
+ self.example_index = example_index
+ self.doc_span_index = doc_span_index
+ self.tokens = tokens
+ self.token_to_orig_map = token_to_orig_map
+ self.token_is_max_context = token_is_max_context
+ self.input_ids = input_ids
+ self.input_mask = input_mask
+ self.segment_ids = segment_ids
+ self.start_position = start_position
+ self.end_position = end_position
+ self.is_impossible = is_impossible
+
+ def _check_is_max_context(doc_spans, cur_span_index, position):
+ """Check if this is the 'max context' doc span for the token."""
+
+ # Because of the sliding window approach taken to scoring documents, a single
+ # token can appear in multiple documents. E.g.
+ # Doc: the man went to the store and bought a gallon of milk
+ # Span A: the man went to the
+ # Span B: to the store and bought
+ # Span C: and bought a gallon of
+ # ...
+ #
+ # Now the word 'bought' will have two scores from spans B and C. We only
+ # want to consider the score with "maximum context", which we define as
+ # the *minimum* of its left and right context (the *sum* of left and
+ # right context will always be the same, of course).
+ #
+ # In the example the maximum context for 'bought' would be span C since
+ # it has 1 left context and 3 right context, while span B has 4 left context
+ # and 0 right context.
+ best_score = None
+ best_span_index = None
+ for (span_index, doc_span) in enumerate(doc_spans):
+ end = doc_span.start + doc_span.length - 1
+ if position < doc_span.start:
+ continue
+ if position > end:
+ continue
+ num_left_context = position - doc_span.start
+ num_right_context = end - position
+ score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
+ if best_score is None or score > best_score:
+ best_score = score
+ best_span_index = span_index
+
+ return cur_span_index == best_span_index
+
+ def convert_examples_to_features(examples, tokenizer, max_seq_length,
+ doc_stride, max_query_length, is_training):
+ """Loads a data file into a list of `InputBatch`s."""
+
+ unique_id = 1000000000
+ result = []
+
+ for (example_index, example) in enumerate(examples):
+ query_tokens = tokenizer.tokenize(example.question_text)
+
+ if len(query_tokens) > max_query_length:
+ query_tokens = query_tokens[0:max_query_length]
+
+ tok_to_orig_index = []
+ orig_to_tok_index = []
+ all_doc_tokens = []
+ for (i, token) in enumerate(example.doc_tokens):
+ orig_to_tok_index.append(len(all_doc_tokens))
+ sub_tokens = tokenizer.tokenize(token)
+ for sub_token in sub_tokens:
+ tok_to_orig_index.append(i)
+ all_doc_tokens.append(sub_token)
+
+ tok_start_position = None
+ tok_end_position = None
+ if is_training and example.is_impossible:
+ tok_start_position = -1
+ tok_end_position = -1
+ if is_training and not example.is_impossible:
+ tok_start_position = orig_to_tok_index[example.start_position]
+ if example.end_position < len(example.doc_tokens) - 1:
+ tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
+ else:
+ tok_end_position = len(all_doc_tokens) - 1
+ (tok_start_position, tok_end_position) = _improve_answer_span(
+ all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
+ example.orig_answer_text)
+
+ # The -3 accounts for [CLS], [SEP] and [SEP]
+ max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
+
+ # We can have documents that are longer than the maximum sequence length.
+ # To deal with this we do a sliding window approach, where we take chunks
+ # of the up to our max length with a stride of `doc_stride`.
+ _DocSpan = collections.namedtuple( # pylint: disable=invalid-name
+ "DocSpan", ["start", "length"])
+ doc_spans = []
+ start_offset = 0
+ while start_offset < len(all_doc_tokens):
+ length = len(all_doc_tokens) - start_offset
+ if length > max_tokens_for_doc:
+ length = max_tokens_for_doc
+ doc_spans.append(_DocSpan(start=start_offset, length=length))
+ if start_offset + length == len(all_doc_tokens):
+ break
+ start_offset += min(length, doc_stride)
+
+ for (doc_span_index, doc_span) in enumerate(doc_spans):
+ tokens = []
+ token_to_orig_map = {}
+ token_is_max_context = {}
+ segment_ids = []
+ tokens.append("[CLS]")
+ segment_ids.append(0)
+ for token in query_tokens:
+ tokens.append(token)
+ segment_ids.append(0)
+ tokens.append("[SEP]")
+ segment_ids.append(0)
+
+ for i in range(doc_span.length):
+ split_token_index = doc_span.start + i
+ token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
+
+ is_max_context = _check_is_max_context(doc_spans, doc_span_index,
+ split_token_index)
+ token_is_max_context[len(tokens)] = is_max_context
+ tokens.append(all_doc_tokens[split_token_index])
+ segment_ids.append(1)
+ tokens.append("[SEP]")
+ segment_ids.append(1)
+
+ input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+ # The mask has 1 for real tokens and 0 for padding tokens. Only real
+ # tokens are attended to.
+ input_mask = [1] * len(input_ids)
+
+ # Zero-pad up to the sequence length.
+ while len(input_ids) < max_seq_length:
+ input_ids.append(0)
+ input_mask.append(0)
+ segment_ids.append(0)
+
+ assert len(input_ids) == max_seq_length
+ assert len(input_mask) == max_seq_length
+ assert len(segment_ids) == max_seq_length
+
+ start_position = None
+ end_position = None
+ if is_training and not example.is_impossible:
+ # For training, if our document chunk does not contain an annotation
+ # we throw it out, since there is nothing to predict.
+ doc_start = doc_span.start
+ doc_end = doc_span.start + doc_span.length - 1
+ out_of_span = False
+ if not (tok_start_position >= doc_start and
+ tok_end_position <= doc_end):
+ out_of_span = True
+ if out_of_span:
+ start_position = 0
+ end_position = 0
+ else:
+ doc_offset = len(query_tokens) + 2
+ start_position = tok_start_position - doc_start + doc_offset
+ end_position = tok_end_position - doc_start + doc_offset
+
+ if is_training and example.is_impossible:
+ start_position = 0
+ end_position = 0
+
+ if example_index < 20:
+ tf.logging.info("*** Example ***")
+ tf.logging.info("unique_id: %s" % (unique_id))
+ tf.logging.info("example_index: %s" % (example_index))
+ tf.logging.info("doc_span_index: %s" % (doc_span_index))
+ tf.logging.info("tokens: %s" % " ".join(
+ [tokenization.printable_text(x) for x in tokens]))
+ tf.logging.info("token_to_orig_map: %s" % " ".join(
+ ["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
+ tf.logging.info("token_is_max_context: %s" % " ".join([
+ "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
+ ]))
+ tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
+ tf.logging.info(
+ "input_mask: %s" % " ".join([str(x) for x in input_mask]))
+ tf.logging.info(
+ "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
+ if is_training and example.is_impossible:
+ tf.logging.info("impossible example")
+ if is_training and not example.is_impossible:
+ answer_text = " ".join(tokens[start_position:(end_position + 1)])
+ tf.logging.info("start_position: %d" % (start_position))
+ tf.logging.info("end_position: %d" % (end_position))
+ tf.logging.info(
+ "answer: %s" % (tokenization.printable_text(answer_text)))
+
+ feature = InputFeatures(
+ unique_id=unique_id,
+ example_index=example_index,
+ doc_span_index=doc_span_index,
+ tokens=tokens,
+ token_to_orig_map=token_to_orig_map,
+ token_is_max_context=token_is_max_context,
+ input_ids=input_ids,
+ input_mask=input_mask,
+ segment_ids=segment_ids,
+ start_position=start_position,
+ end_position=end_position,
+ is_impossible=example.is_impossible)
+
+ # Run callback
+
+ result.append(feature)
+ unique_id += 1
+ return result
+
+ class SquadExample(object):
+
+
+ def __init__(self,
+ qas_id,
+ question_text,
+ doc_tokens,
+ orig_answer_text=None,
+ start_position=None,
+ end_position=None,
+ is_impossible=False):
+ self.qas_id = qas_id
+ self.question_text = question_text
+ self.doc_tokens = doc_tokens
+ self.orig_answer_text = orig_answer_text
+ self.start_position = start_position
+ self.end_position = end_position
+ self.is_impossible = is_impossible
+
+ def __str__(self):
+ return self.__repr__()
+
+ def __repr__(self):
+ s = ""
+ s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
+ s += ", question_text: %s" % (
+ tokenization.printable_text(self.question_text))
+ s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
+ if self.start_position:
+ s += ", start_position: %d" % (self.start_position)
+ if self.start_position:
+ s += ", end_position: %d" % (self.end_position)
+ if self.start_position:
+ s += ", is_impossible: %r" % (self.is_impossible)
+ return s
+
+
+
+ def read_squad_examples(input_file, is_training):
+ """Read a SQuAD json file into a list of SquadExample."""
+
+ input_data = json.loads(input_file)["data"]
+
+ def is_whitespace(c):
+ if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
+ return True
+ return False
+
+ examples = []
+ for entry in input_data:
+ for paragraph in entry["paragraphs"]:
+ paragraph_text = paragraph["context"]
+ doc_tokens = []
+ char_to_word_offset = []
+ prev_is_whitespace = True
+ for c in paragraph_text:
+ if is_whitespace(c):
+ prev_is_whitespace = True
+ else:
+ if prev_is_whitespace:
+ doc_tokens.append(c)
+ else:
+ doc_tokens[-1] += c
+ prev_is_whitespace = False
+ char_to_word_offset.append(len(doc_tokens) - 1)
+
+ for qa in paragraph["qas"]:
+ qas_id = qa["id"]
+ question_text = qa["question"]
+ start_position = None
+ end_position = None
+ orig_answer_text = None
+ is_impossible = False
+ if is_training:
+
+
+ if (len(qa["answers"]) != 1) and (not is_impossible):
+ raise ValueError(
+ "For training, each question should have exactly 1 answer.")
+ if not is_impossible:
+ answer = qa["answers"][0]
+ orig_answer_text = answer["text"]
+ answer_offset = answer["answer_start"]
+ answer_length = len(orig_answer_text)
+ start_position = char_to_word_offset[answer_offset]
+ end_position = char_to_word_offset[answer_offset + answer_length -
+ 1]
+ # Only add answers where the text can be exactly recovered from the
+ # document. If this CAN'T happen it's likely due to weird Unicode
+ # stuff so we will just skip the example.
+ #
+ # Note that this means for training mode, every example is NOT
+ # guaranteed to be preserved.
+ actual_text = " ".join(
+ doc_tokens[start_position:(end_position + 1)])
+ cleaned_answer_text = " ".join(
+ tokenization.whitespace_tokenize(orig_answer_text))
+ if actual_text.find(cleaned_answer_text) == -1:
+ tf.logging.warning("Could not find answer: '%s' vs. '%s'",
+ actual_text, cleaned_answer_text)
+ continue
+ else:
+ start_position = -1
+ end_position = -1
+ orig_answer_text = ""
+
+ example = SquadExample(
+ qas_id=qas_id,
+ question_text=question_text,
+ doc_tokens=doc_tokens,
+ orig_answer_text=orig_answer_text,
+ start_position=start_position,
+ end_position=end_position,
+ is_impossible=is_impossible)
+ examples.append(example)
+
+ return examples
+
+
+ def get_result(title,text,question,url):
+
+ data = make_json(title,text,question)
+
+
+ examples = read_squad_examples(data,False)
+
+
+ predict_files = convert_examples_to_features(
+ examples=examples,
+ tokenizer=tokenizer,
+ max_seq_length=512,
+ doc_stride=128,
+ max_query_length=100,
+ is_training=False,
+ )
+
+ headers = {"content-type": "application/json"}
+ all_results = []
+ for predict_file in predict_files:
+ features = {}
+ features["unique_ids"] = predict_file.unique_id
+ features["input_mask"] = predict_file.input_mask
+ features["segment_ids"] = predict_file.segment_ids
+ features["input_ids"] = predict_file.input_ids
+ data_list = []
+ data_list.append(features)
+
+ data = json.dumps({"instances": data_list})
+
+ json_response = requests.post(url, data=data, headers=headers)
+
+
+ x = json.loads(json_response.text)
+
+ all_results.append(
+ RawResult(
+ unique_id=predict_file.unique_id,
+ start_logits=x['predictions'][0]['start_logits'],
+ end_logits=x['predictions'][0]['end_logits']))
+
+ result = write_predictions(examples, predict_files, all_results,20, 64,True)
+ return result
+
+ return get_result(title, text, question, url)
+
diff --git a/scripts/global_setting.py b/scripts/global_setting.py
new file mode 100644
index 0000000..bb035f9
--- /dev/null
+++ b/scripts/global_setting.py
@@ -0,0 +1,36 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+import collections
+import math
+import modeling
+import optimization
+import tokenization
+import six
+import tensorflow as tf
+import os
+
+
+
+
+### Global variables
+
+# GPU number, default: -1, means not used
+CUDA_VISIBLE_DEVICES="2"
+
+# Questions to be trained/predicted
+questions = ['Communication Service Name','Max Number of UEs','Data Rate Downlink','Latency','Data Rate Uplink','Resource Sharing Level','Mobility','Area']
+
+# Configuration file
+FLAGS_bert_config_file = '/home/run/chinese_L-12_H-768_A-12/bert_config.json'
+FLAGS_vocab_file = '/home/run/chinese_L-12_H-768_A-12/vocab.txt'
+FLAGS_init_checkpoint_squad = '/home/run/chinese_L-12_H-768_A-12/bert_model.ckpt'
+
+max_seq_length = 512
+
+
+tokenizer_ch = tokenization.FullTokenizer(vocab_file=FLAGS_vocab_file, do_lower_case=True)
+
+
diff --git a/scripts/load_model.sh b/scripts/load_model.sh
new file mode 100644
index 0000000..1961f6a
--- /dev/null
+++ b/scripts/load_model.sh
@@ -0,0 +1,6 @@
+path=$1
+use_gpu=$2
+export CUDA_VISIBLE_DEVICES=$use_gpu
+netstat -nap | grep 8502 | awk 'NR==1{printf $7}' | sed 's/\([0-9]*\).*/\1/g' | xargs kill -9
+sleep 5
+nohup tensorflow_model_server --port=8500 --rest_api_port=8502 --model_name=predict --model_base_path=$path > server.log 2>&1 & \ No newline at end of file