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ernieDataReader.py
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import absolute_import
import os
import numpy as np
import types
import gzip
import logging
import re
import six
import collections
import tokenization
import paddle
import paddle.fluid as fluid
from tokenization import FullTokenizer
from batching import prepare_batch_data,pad_batch_data
class ErnieDataReader(object):
def __init__(self,
vocab_path,
data,
batch_size=4096,
max_seq_len=512,
random_seed=1):
self.vocab = self.load_vocab(vocab_path)
# vocab = load_vocab(vocab_path)
self.vocab_path = vocab_path
self.batch_size = batch_size
self.random_seed = random_seed
self.max_seq_len = max_seq_len
self.pad_id = self.vocab["[PAD]"]
self.mask_id = self.vocab["[MASK]"]
self.pos = 0
self.data = data
def parse_line(self, line, max_seq_len=512):
""" parse one line to token_ids, sentence_ids, pos_ids, label
"""
line = line.strip().split("，")
assert len(line) == 3, \
"One sample must have %d fields!" % 3
text_left, text_right, masklabel = line
tokenizer = FullTokenizer(self.vocab_path)
# tokenizer = FullTokenizer(vocab_path)
text_left = tokenizer.tokenize(text_left)
masklabel = tokenizer.tokenize(masklabel)
masklabel_ = len(masklabel)*["[MASK]"]
text_right = tokenizer.tokenize(text_right)
all_tokens = text_left + masklabel_ + text_right
token_ids = tokenizer.convert_tokens_to_ids(all_tokens)
sent_ids = [0]*len(all_tokens)
pos_ids = [i for i in range(len(all_tokens))]
input_mask = [1.0]*len(all_tokens)
# 这儿还差一个mask_pos
mask_pos = []
for idx,mask in enumerate(token_ids):
if mask ==self.mask_id:
mask_pos.append(idx)
# 添加一个mask_label
mask_label = list(tokenizer.convert_tokens_to_ids(masklabel))
assert len(token_ids) == len(sent_ids) == len(pos_ids)==len(input_mask) , "[Must be true]len(token_ids) == len(sent_ids) == len(pos_ids) == len(seg_labels)"
if len(token_ids) > max_seq_len:
return None
return [token_ids, sent_ids, pos_ids, input_mask,mask_pos,mask_label]
def parse_batch(self,batch):
token_ids_batch, sent_ids_batch, pos_ids_batch, input_mask_batch,mask_pos_batch,mask_label_batch= [],[],[],[],[],[]
for line in batch:
token_ids, sent_ids, pos_ids, input_mask,mask_pos,mask_label = self.parse_line(line)
token_ids_batch.append(token_ids)
sent_ids_batch.append(sent_ids)
pos_ids_batch.append(pos_ids)
input_mask_batch.append(input_mask)
mask_pos_batch.append(mask_pos)
mask_label_batch.append(mask_label)
return token_ids_batch, sent_ids_batch, pos_ids_batch, input_mask_batch,mask_pos_batch,mask_label_batch
def next_predict_batch(self,batch_size):
if self.pos>=len(self.data):
self.pos=0
return None
else:
batch = self.data[self.pos: self.pos + batch_size]
self.pos += batch_size
token_ids_batch, sent_ids_batch, pos_ids_batch, input_mask_batch,mask_pos_batch,mask_label_batch = self.parse_batch(batch)
# 用padding处理一下
token_ids_batch ,input_mask_batch= pad_batch_data(token_ids_batch,pad_idx=self.pad_id,return_input_mask=True)
sent_ids_batch = pad_batch_data(sent_ids_batch,pad_idx=self.pad_id)
pos_ids_batch = pad_batch_data(pos_ids_batch, pad_idx=self.pad_id)
# input_mask_batch = pad_batch_data(input_mask_batch, pad_idx=self.pad_id)
mask_pos_batch = np.array(mask_pos_batch).astype("int64").reshape([-1, 1])
mask_label_batch = np.array(mask_label_batch).astype("int64").reshape([-1, 1])
# mask_pos_batch并没有进行padding,而在ernie中似乎进行了padding
return token_ids_batch, sent_ids_batch, pos_ids_batch, input_mask_batch,mask_pos_batch,mask_label_batch
def convert_to_unicode(self, text):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text.decode("utf-8", "ignore")
elif isinstance(text, unicode):
return text
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def load_vocab(self, vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
fin = open(vocab_file,encoding='utf-8')
for num, line in enumerate(fin):
items = self.convert_to_unicode(line.strip()).split("\t")
if len(items) > 2:
break
token = items[0]
index = items[1] if len(items) == 2 else num
token = token.strip()
vocab[token] = int(index)
return vocab
class process(object):
def convert_to_unicode(self, text):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text.decode("utf-8", "ignore")
elif isinstance(text, unicode):
return text
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def read_file(self,file):
assert file.endswith('.txt'), "[ERROR] %s is not a txt file" % file
sentensens = []
with open(file, encoding='utf-8') as f:
for line in f:
sentensens.append(line)
return sentensens
if __name__ == "__main__":
pass
# 最后产生的结果是return_list = [
# src_id, pos_id, sent_id, self_input_mask, mask_label, mask_pos, labels
# ]