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data_loader.py
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import json
import torch
from torch.utils.data import Dataset
from torch.nn.utils.rnn import pad_sequence
import numpy as np
import prettytable as pt
from gensim.models import KeyedVectors
from transformers import AutoTokenizer
import os
import utils
import requests
from pprint import pprint
os.environ["TOKENIZERS_PARALLELISM"] = "false"
dis2idx = np.zeros((1000), dtype='int64')
dis2idx[1] = 1
dis2idx[2:] = 2
dis2idx[4:] = 3
dis2idx[8:] = 4
dis2idx[16:] = 5
dis2idx[32:] = 6
dis2idx[64:] = 7
dis2idx[128:] = 8
dis2idx[256:] = 9
class Vocabulary(object):
PAD = '<pad>'
UNK = '<unk>'
SUC = '<suc>'
def __init__(self):
self.label2id = {self.PAD: 0, self.SUC: 1}
self.id2label = {0: self.PAD, 1: self.SUC}
def add_label(self, label):
label = label.lower()
if label not in self.label2id:
self.label2id[label] = len(self.label2id)
self.id2label[self.label2id[label]] = label
assert label == self.id2label[self.label2id[label]]
def __len__(self):
return len(self.token2id)
def label_to_id(self, label):
label = label.lower()
return self.label2id[label]
def collate_fn(data):
bert_inputs, grid_labels, grid_mask2d, pieces2word, dist_inputs, sent_length, entity_text = map(list, zip(*data))
max_tok = np.max(sent_length)
sent_length = torch.LongTensor(sent_length)
max_pie = np.max([x.shape[0] for x in bert_inputs])
bert_inputs = pad_sequence(bert_inputs, True)
batch_size = bert_inputs.size(0)
def fill(data, new_data):
for j, x in enumerate(data):
new_data[j, :x.shape[0], :x.shape[1]] = x
return new_data
dis_mat = torch.zeros((batch_size, max_tok, max_tok), dtype=torch.long)
dist_inputs = fill(dist_inputs, dis_mat)
labels_mat = torch.zeros((batch_size, max_tok, max_tok), dtype=torch.long)
grid_labels = fill(grid_labels, labels_mat)
mask2d_mat = torch.zeros((batch_size, max_tok, max_tok), dtype=torch.bool)
grid_mask2d = fill(grid_mask2d, mask2d_mat)
sub_mat = torch.zeros((batch_size, max_tok, max_pie), dtype=torch.bool)
pieces2word = fill(pieces2word, sub_mat)
return bert_inputs, grid_labels, grid_mask2d, pieces2word, dist_inputs, sent_length, entity_text
class RelationDataset(Dataset):
def __init__(self, bert_inputs, grid_labels, grid_mask2d, pieces2word, dist_inputs, sent_length, entity_text):
self.bert_inputs = bert_inputs
self.grid_labels = grid_labels
self.grid_mask2d = grid_mask2d
self.pieces2word = pieces2word
self.dist_inputs = dist_inputs
self.sent_length = sent_length
self.entity_text = entity_text
def __getitem__(self, item):
return torch.LongTensor(self.bert_inputs[item]), \
torch.LongTensor(self.grid_labels[item]), \
torch.LongTensor(self.grid_mask2d[item]), \
torch.LongTensor(self.pieces2word[item]), \
torch.LongTensor(self.dist_inputs[item]), \
self.sent_length[item], \
self.entity_text[item]
def __len__(self):
return len(self.bert_inputs)
def process_bert(data, tokenizer, vocab):
# {'<pad>': 0, '<suc>': 1, 'name': 2, 'cont': 3, 'race': 4, 'title': 5, 'edu': 6, 'org': 7, 'pro': 8, 'loc': 9}
bert_inputs = []
grid_labels = []
grid_mask2d = []
dist_inputs = []
entity_text = []
pieces2word = []
sent_length = []
for index, instance in enumerate(data):
# 以{'sentence': ['高', '勇', '：', '男', '，', '中', '国', '国', '籍', '，', '无', '境', '外', '居', '留', '权', '，'],
# 'ner': [{'index': [0, 1], 'type': 'NAME'},
# {'index': [5, 6, 7, 8], 'type': 'CONT'}],
# 'word': [[0, 1], [2], [3], [4], [5, 6], [7, 8], [9], [10], [11, 12], [13, 14, 15], [16]]}
# 为例
if len(instance['sentence']) == 0:
continue
# tokens:[['高'], ['勇'], ['：'], ['男'], ['，'], ['中'], ['国'], ['国'], ['籍'], ['，'], ['无'], ['境'], ['外'], ['居'], ['留'], ['权'], ['，']]
tokens = [tokenizer.tokenize(word) for word in instance['sentence']]
pieces = [piece for pieces in tokens for piece in pieces]
_bert_inputs = tokenizer.convert_tokens_to_ids(pieces)
# 将字符转换为bert需要的token
_bert_inputs = np.array([tokenizer.cls_token_id] + _bert_inputs + [tokenizer.sep_token_id])
length = len(instance['sentence'])
_grid_labels = np.zeros((length, length), dtype=np.int)
_pieces2word = np.zeros((length, len(_bert_inputs)), dtype=np.bool)
_dist_inputs = np.zeros((length, length), dtype=np.int)
_grid_mask2d = np.ones((length, length), dtype=np.bool)
if tokenizer is not None:
start = 0
# tokens:[['高'], ['勇'], ['：'], ['男'], ['，'], ['中'], ['国'], ['国'], ['籍'], ['，'], ['无'], ['境'], ['外'], ['居'], ['留'], ['权'], ['，']]
for i, pieces in enumerate(tokens):
if len(pieces) == 0:
continue
# 这里的start表示的是第i个token的起始位置
pieces = list(range(start, start + len(pieces)))
_pieces2word[i, pieces[0] + 1:pieces[-1] + 2] = 1
start += len(pieces)
for k in range(length):
_dist_inputs[k, :] += k
_dist_inputs[:, k] -= k
for i in range(length):
for j in range(length):
if _dist_inputs[i, j] < 0:
_dist_inputs[i, j] = dis2idx[-_dist_inputs[i, j]] + 9
else:
_dist_inputs[i, j] = dis2idx[_dist_inputs[i, j]]
_dist_inputs[_dist_inputs == 0] = 19
# _dist_inputs:
"""
[[19 10 11 11 12 12 12 12 13 13 13 13 13 13 13 13 14]
[ 1 19 10 11 11 12 12 12 12 13 13 13 13 13 13 13 13]
[ 2 1 19 10 11 11 12 12 12 12 13 13 13 13 13 13 13]
[ 2 2 1 19 10 11 11 12 12 12 12 13 13 13 13 13 13]
[ 3 2 2 1 19 10 11 11 12 12 12 12 13 13 13 13 13]
[ 3 3 2 2 1 19 10 11 11 12 12 12 12 13 13 13 13]
[ 3 3 3 2 2 1 19 10 11 11 12 12 12 12 13 13 13]
[ 3 3 3 3 2 2 1 19 10 11 11 12 12 12 12 13 13]
[ 4 3 3 3 3 2 2 1 19 10 11 11 12 12 12 12 13]
[ 4 4 3 3 3 3 2 2 1 19 10 11 11 12 12 12 12]
[ 4 4 4 3 3 3 3 2 2 1 19 10 11 11 12 12 12]
[ 4 4 4 4 3 3 3 3 2 2 1 19 10 11 11 12 12]
[ 4 4 4 4 4 3 3 3 3 2 2 1 19 10 11 11 12]
[ 4 4 4 4 4 4 3 3 3 3 2 2 1 19 10 11 11]
[ 4 4 4 4 4 4 4 3 3 3 3 2 2 1 19 10 11]
[ 4 4 4 4 4 4 4 4 3 3 3 3 2 2 1 19 10]
[ 5 4 4 4 4 4 4 4 4 3 3 3 3 2 2 1 19]]
"""
for entity in instance["ner"]:
index = entity["index"]
for i in range(len(index)):
if i + 1 >= len(index):
break
_grid_labels[index[i], index[i + 1]] = 1
_grid_labels[index[-1], index[0]] = vocab.label_to_id(entity["type"])
# _grid_labels：
"""
[[0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0]
[0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]
"""
# _entity_text：{'0-1-#-2', '5-6-7-8-#-3'}
_entity_text = set([utils.convert_index_to_text(e["index"], vocab.label_to_id(e["type"]))
for e in instance["ner"]])
sent_length.append(length)
bert_inputs.append(_bert_inputs)
grid_labels.append(_grid_labels)
grid_mask2d.append(_grid_mask2d)
dist_inputs.append(_dist_inputs)
pieces2word.append(_pieces2word)
entity_text.append(_entity_text)
return bert_inputs, grid_labels, grid_mask2d, pieces2word, dist_inputs, sent_length, entity_text
def fill_vocab(vocab, dataset):
entity_num = 0
for instance in dataset:
for entity in instance["ner"]:
vocab.add_label(entity["type"])
entity_num += len(instance["ner"])
return entity_num
def load_data_bert(config):
with open('./data/{}/train.json'.format(config.dataset), 'r', encoding='utf-8') as f:
train_data = json.load(f)
with open('./data/{}/dev.json'.format(config.dataset), 'r', encoding='utf-8') as f:
dev_data = json.load(f)
with open('./data/{}/test.json'.format(config.dataset), 'r', encoding='utf-8') as f:
test_data = json.load(f)
tokenizer = AutoTokenizer.from_pretrained(config.bert_name, cache_dir="./cache/")
vocab = Vocabulary()
train_ent_num = fill_vocab(vocab, train_data)
dev_ent_num = fill_vocab(vocab, dev_data)
test_ent_num = fill_vocab(vocab, test_data)
table = pt.PrettyTable([config.dataset, 'sentences', 'entities'])
table.add_row(['train', len(train_data), train_ent_num])
table.add_row(['dev', len(dev_data), dev_ent_num])
table.add_row(['test', len(test_data), test_ent_num])
config.logger.info("\n{}".format(table))
config.label_num = len(vocab.label2id)
config.vocab = vocab
train_dataset = RelationDataset(*process_bert(train_data, tokenizer, vocab))
dev_dataset = RelationDataset(*process_bert(dev_data, tokenizer, vocab))
test_dataset = RelationDataset(*process_bert(test_data, tokenizer, vocab))
return train_dataset, dev_dataset, test_dataset
def process_bert_predict(texts, tokenizer, vocab):
bert_inputs = []
grid_labels = []
grid_mask2d = []
dist_inputs = []
entity_text = []
pieces2word = []
sent_length = []
for index, text in enumerate(texts):
# 这里直接是以字为单位
tokens = [tokenizer.tokenize(word) for word in text]
pieces = [piece for pieces in tokens for piece in pieces]
_bert_inputs = tokenizer.convert_tokens_to_ids(pieces)
# 将字符转换为bert需要的token
_bert_inputs = np.array([tokenizer.cls_token_id] + _bert_inputs + [tokenizer.sep_token_id])
length = len(text)
_grid_labels = np.zeros((length, length), dtype=np.int)
_pieces2word = np.zeros((length, len(_bert_inputs)), dtype=np.bool)
_dist_inputs = np.zeros((length, length), dtype=np.int)
_grid_mask2d = np.ones((length, length), dtype=np.bool)
if tokenizer is not None:
start = 0
# tokens:[['高'], ['勇'], ['：'], ['男'], ['，'], ['中'], ['国'], ['国'], ['籍'], ['，'], ['无'], ['境'], ['外'], ['居'], ['留'], ['权'], ['，']]
for i, pieces in enumerate(tokens):
if len(pieces) == 0:
continue
# 这里的start表示的是第i个token的起始位置
pieces = list(range(start, start + len(pieces)))
_pieces2word[i, pieces[0] + 1:pieces[-1] + 2] = 1
start += len(pieces)
for k in range(length):
_dist_inputs[k, :] += k
_dist_inputs[:, k] -= k
for i in range(length):
for j in range(length):
if _dist_inputs[i, j] < 0:
_dist_inputs[i, j] = dis2idx[-_dist_inputs[i, j]] + 9
else:
_dist_inputs[i, j] = dis2idx[_dist_inputs[i, j]]
_dist_inputs[_dist_inputs == 0] = 19
sent_length.append(length)
bert_inputs.append(_bert_inputs)
grid_mask2d.append(_grid_mask2d)
dist_inputs.append(_dist_inputs)
pieces2word.append(_pieces2word)
return bert_inputs, grid_mask2d, pieces2word, dist_inputs, sent_length, texts
def collate_fn_predict(data):
bert_inputs, grid_mask2d, pieces2word, dist_inputs, sent_length, texts = map(list, zip(*data))
max_tok = np.max(sent_length)
sent_length = torch.LongTensor(sent_length)
max_pie = np.max([x.shape[0] for x in bert_inputs])
bert_inputs = pad_sequence(bert_inputs, True)
batch_size = bert_inputs.size(0)
def fill(data, new_data):
for j, x in enumerate(data):
new_data[j, :x.shape[0], :x.shape[1]] = x
return new_data
dis_mat = torch.zeros((batch_size, max_tok, max_tok), dtype=torch.long)
dist_inputs = fill(dist_inputs, dis_mat)
mask2d_mat = torch.zeros((batch_size, max_tok, max_tok), dtype=torch.bool)
grid_mask2d = fill(grid_mask2d, mask2d_mat)
sub_mat = torch.zeros((batch_size, max_tok, max_pie), dtype=torch.bool)
pieces2word = fill(pieces2word, sub_mat)
return bert_inputs, grid_mask2d, pieces2word, dist_inputs, sent_length, texts
class RelationDatasetPredict(Dataset):
def __init__(self, bert_inputs, grid_mask2d, pieces2word, dist_inputs, sent_length, texts):
self.bert_inputs = bert_inputs
self.grid_mask2d = grid_mask2d
self.pieces2word = pieces2word
self.dist_inputs = dist_inputs
self.sent_length = sent_length
self.texts = texts
def __getitem__(self, item):
return torch.LongTensor(self.bert_inputs[item]), \
torch.LongTensor(self.grid_mask2d[item]), \
torch.LongTensor(self.pieces2word[item]), \
torch.LongTensor(self.dist_inputs[item]), \
self.sent_length[item], \
self.texts[item]
def __len__(self):
return len(self.bert_inputs)
def load_data_bert_predict(texts, config):
if isinstance(texts, str):
texts = [texts]
# with open('./data/{}/train.json'.format(config.dataset), 'r', encoding='utf-8') as f:
# train_data = json.load(f)
# with open('./data/{}/dev.json'.format(config.dataset), 'r', encoding='utf-8') as f:
# dev_data = json.load(f)
# with open('./data/{}/test.json'.format(config.dataset), 'r', encoding='utf-8') as f:
# test_data = json.load(f)
tokenizer = AutoTokenizer.from_pretrained(config.bert_name, cache_dir="./cache/")
vocab = Vocabulary()
# train_ent_num = fill_vocab(vocab, train_data)
# dev_ent_num = fill_vocab(vocab, dev_data)
# test_ent_num = fill_vocab(vocab, test_data)
label2id = {'<pad>': 0, '<suc>': 1, 'name': 2, 'cont': 3, 'race': 4, 'title': 5, 'edu': 6, 'org': 7, 'pro': 8, 'loc': 9}
id2label = {v:k for k,v in label2id.items()}
vocab.label2id = label2id
vocab.id2label = id2label
print(dict(vocab.label2id))
print("=============================")
config.label_num = len(vocab.label2id)
config.vocab = vocab
print(config)
# process_bert_predict(texts, tokenizer, vocab)
predict_dataset = RelationDatasetPredict(*process_bert_predict(texts, tokenizer, vocab))
return predict_dataset
if __name__ == "__main__":
import config
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='./config/resume-zh.json')
parser.add_argument('--device', type=int, default=0)
parser.add_argument('--dist_emb_size', type=int)
parser.add_argument('--type_emb_size', type=int)
parser.add_argument('--lstm_hid_size', type=int)
parser.add_argument('--conv_hid_size', type=int)
parser.add_argument('--bert_hid_size', type=int)
parser.add_argument('--ffnn_hid_size', type=int)
parser.add_argument('--biaffine_size', type=int)
parser.add_argument('--dilation', type=str, help="e.g. 1,2,3")
parser.add_argument('--emb_dropout', type=float)
parser.add_argument('--conv_dropout', type=float)
parser.add_argument('--out_dropout', type=float)
parser.add_argument('--epochs', type=int)
parser.add_argument('--batch_size', type=int)
parser.add_argument('--clip_grad_norm', type=float)
parser.add_argument('--learning_rate', type=float)
parser.add_argument('--weight_decay', type=float)
parser.add_argument('--bert_name', type=str)
parser.add_argument('--bert_learning_rate', type=float)
parser.add_argument('--warm_factor', type=float)
parser.add_argument('--use_bert_last_4_layers', type=int, help="1: true, 0: false")
parser.add_argument('--seed', type=int)
args = parser.parse_args()
config = config.Config(args)
"""
with open('./data/{}/test.json'.format(config.dataset), 'r', encoding='utf-8') as f:
test_data = json.load(f)
print(test_data[0])
"""
texts = [
"高勇，男，中国国籍，无境外居留权。"
]
predict_dataset = load_data_bert_predict(texts, config)
from torch.utils.data import DataLoader
predict_loader = DataLoader(dataset=predict_dataset,
batch_size=config.batch_size,
collate_fn=collate_fn_predict,
shuffle=False,
num_workers=4,
drop_last=False)
for i, data_batch in enumerate(predict_loader):
bert_inputs, grid_mask2d, pieces2word, dist_inputs, sent_length = data_batch
print(bert_inputs)
print(grid_mask2d)
print(pieces2word)
print(dist_inputs)
print(sent_length)