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import os
import torch
import numpy as np
import json
import re
from torch.autograd import Variable
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since if one sequence is very short then each token
# that's truncated likely contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
def tok2int_sent(sentence, tokenizer, max_seq_length):
"""Loads a data file into a list of `InputBatch`s."""
sent_a, sent_b = sentence
tokens_a = tokenizer.tokenize(sent_a)
tokens_b = None
if sent_b:
tokens_b = tokenizer.tokenize(sent_b)
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[:(max_seq_length - 2)]
tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
segment_ids = [0] * len(tokens)
if tokens_b:
tokens = tokens + tokens_b + ["[SEP]"]
segment_ids += [1] * (len(tokens_b) + 1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
input_mask = [1] * len(input_ids)
padding = [0] * (max_seq_length - len(input_ids))
input_ids += padding
input_mask += padding
segment_ids += padding
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
return input_ids, input_mask, segment_ids
def tok2int_list(src_list, tokenizer, max_seq_length, max_seq_size=-1):
inp_padding = list()
msk_padding = list()
seg_padding = list()
for step, sent in enumerate(src_list):
input_ids, input_mask, input_seg = tok2int_sent(sent, tokenizer, max_seq_length)
inp_padding.append(input_ids)
msk_padding.append(input_mask)
seg_padding.append(input_seg)
# if max_seq_size != -1:
# inp_padding = inp_padding[:max_seq_size]
# msk_padding = msk_padding[:max_seq_size]
# seg_padding = seg_padding[:max_seq_size]
# inp_padding += ([[0] * max_seq_length] * (max_seq_size - len(inp_padding)))
# msk_padding += ([[0] * max_seq_length] * (max_seq_size - len(msk_padding)))
# seg_padding += ([[0] * max_seq_length] * (max_seq_size - len(seg_padding)))
return inp_padding, msk_padding, seg_padding
class DataLoader(object):
''' For data iteration '''
def __init__(self, data_path, tokenizer, args, test=False, cuda=True, batch_size=64):
self.cuda = cuda
self.batch_size = batch_size
self.tokenizer = tokenizer
self.max_len = args.max_len
self.evi_num = args.evi_num
self.threshold = args.threshold
self.data_path = data_path
self.test = test
examples = self.read_file(data_path)
self.examples = examples
self.total_num = len(examples)
if self.test:
self.total_num = 100000
self.total_step = np.ceil(self.total_num * 1.0 / batch_size)
self.shuffle()
else:
self.total_step = self.total_num / batch_size
self.shuffle()
self.step = 0
def process_sent(self, sentence):
sentence = re.sub(" \-LSB\-.*?\-RSB\-", "", sentence)
sentence = re.sub("\-LRB\- \-RRB\- ", "", sentence)
sentence = re.sub(" -LRB-", " ( ", sentence)
sentence = re.sub("-RRB-", " )", sentence)
sentence = re.sub("--", "-", sentence)
sentence = re.sub("``", '"', sentence)
sentence = re.sub("''", '"', sentence)
return sentence
def process_wiki_title(self, title):
title = re.sub("_", " ", title)
title = re.sub(" -LRB-", " ( ", title)
title = re.sub("-RRB-", " )", title)
title = re.sub("-COLON-", ":", title)
return title
def read_file(self, data_path):
examples = list()
with open(data_path) as fin:
for step, line in enumerate(fin):
sublines = line.strip().split("\t")
examples.append(
[self.process_sent(sublines[0]), self.process_sent(sublines[2]), self.process_sent(sublines[4])])
return examples
def shuffle(self):
np.random.shuffle(self.examples)
def __iter__(self):
return self
def __next__(self):
return self.next()
def __len__(self):
return self._n_batch
def next(self):
''' Get the next batch '''
if self.step < self.total_step:
examples = self.examples[self.step * self.batch_size: (self.step + 1) * self.batch_size]
pos_inputs = list()
neg_inputs = list()
for example in examples:
pos_inputs.append([example[0], example[1]])
neg_inputs.append([example[0], example[2]])
inp_pos, msk_pos, seg_pos = tok2int_list(pos_inputs, self.tokenizer, self.max_len)
inp_neg, msk_neg, seg_neg = tok2int_list(neg_inputs, self.tokenizer, self.max_len)
inp_tensor_pos = Variable(
torch.LongTensor(inp_pos))
msk_tensor_pos = Variable(
torch.LongTensor(msk_pos))
seg_tensor_pos = Variable(
torch.LongTensor(seg_pos))
inp_tensor_neg = Variable(
torch.LongTensor(inp_neg))
msk_tensor_neg = Variable(
torch.LongTensor(msk_neg))
seg_tensor_neg = Variable(
torch.LongTensor(seg_neg))
if self.cuda:
inp_tensor_pos = inp_tensor_pos.cuda()
msk_tensor_pos = msk_tensor_pos.cuda()
seg_tensor_pos = seg_tensor_pos.cuda()
inp_tensor_neg = inp_tensor_neg.cuda()
msk_tensor_neg = msk_tensor_neg.cuda()
seg_tensor_neg = seg_tensor_neg.cuda()
self.step += 1
return inp_tensor_pos, msk_tensor_pos, seg_tensor_pos, inp_tensor_neg, msk_tensor_neg, seg_tensor_neg
else:
self.step = 0
if not self.test:
# examples = self.read_file(self.data_path)
# self.examples = examples
self.shuffle()
raise StopIteration()
class DataLoaderTest(object):
''' For data iteration '''
def __init__(self, data_path, tokenizer, args, cuda=True, batch_size=64):
self.cuda = cuda
self.batch_size = batch_size
self.tokenizer = tokenizer
self.max_len = args.max_len
self.evi_num = args.evi_num
self.threshold = args.threshold
self.data_path = data_path
inputs, ids, evi_list = self.read_all(data_path)
self.inputs = inputs
self.ids = ids
self.evi_list = evi_list
self.total_num = len(inputs)
self.total_step = np.ceil(self.total_num * 1.0 / batch_size)
self.step = 0
def process_sent(self, sentence):
sentence = re.sub(" \-LSB\-.*?\-RSB\-", "", sentence)
sentence = re.sub("\-LRB\- \-RRB\- ", "", sentence)
sentence = re.sub(" -LRB-", " ( ", sentence)
sentence = re.sub("-RRB-", " )", sentence)
sentence = re.sub("--", "-", sentence)
sentence = re.sub("``", '"', sentence)
sentence = re.sub("''", '"', sentence)
return sentence
def process_wiki_title(self, title):
title = re.sub("_", " ", title)
title = re.sub(" -LRB-", " ( ", title)
title = re.sub("-RRB-", " )", title)
title = re.sub("-COLON-", ":", title)
return title
def read_all(self, data):
if not isinstance(data, list):
with open(data) as f:
data_ = [json.loads(line) for line in f]
else:
data_ = data
inputs = list()
ids = list()
evi_list = list()
for instance in data_:
claim = instance['claim']
id = instance['id']
for evidence in instance['evidence']:
ids.append(id)
inputs.append([self.process_sent(claim), self.process_sent(evidence[2])])
evi_list.append(evidence)
return inputs, ids, evi_list
def shuffle(self):
np.random.shuffle(self.examples)
def __iter__(self):
return self
def __next__(self):
return self.next()
def __len__(self):
return self._n_batch
def next(self):
''' Get the next batch '''
if self.step < self.total_step:
inputs = self.inputs[self.step * self.batch_size: (self.step + 1) * self.batch_size]
ids = self.ids[self.step * self.batch_size: (self.step + 1) * self.batch_size]
evi_list = self.evi_list[self.step * self.batch_size: (self.step + 1) * self.batch_size]
inp, msk, seg = tok2int_list(inputs, self.tokenizer, self.max_len, -1)
inp_tensor_input = Variable(
torch.LongTensor(inp))
msk_tensor_input = Variable(
torch.LongTensor(msk))
seg_tensor_input = Variable(
torch.LongTensor(seg))
if self.cuda:
inp_tensor_input = inp_tensor_input.cuda()
msk_tensor_input = msk_tensor_input.cuda()
seg_tensor_input = seg_tensor_input.cuda()
self.step += 1
return inp_tensor_input, msk_tensor_input, seg_tensor_input, ids, evi_list
else:
self.step = 0
raise StopIteration()
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