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run_sc.py
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# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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, division, print_function
""" Finetuning the library models for sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa)."""
'''
由于数据集中，没有关于NONE的数据，所以在最后的结果中，对于NONE的分类一定是很差的，可以考虑数据增强(为每个样本创建1-2个错误三元组)，来增加NONE数据的数量
'''
import nltk
from nltk.tokenize import word_tokenize
import argparse
import glob
import logging
import os
import random
from collections import defaultdict
import re
import shutil
import wandb
import numpy as np
from copy import copy, deepcopy
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from tensorboardX import SummaryWriter
from tqdm import tqdm, trange
import time
import linecache
from collections import Counter, defaultdict
from transformers import (WEIGHTS_NAME, BertConfig,
BertTokenizer,
RobertaConfig,
RobertaTokenizer,
get_linear_schedule_with_warmup,
AdamW,
BertForACEBothOneDropoutSub,
AlbertForACEBothSub,
AlbertConfig,
AlbertTokenizer,
AlbertForACEBothOneDropoutSub,
BertForACEBothOneDropoutSubNoNer,
)
import spacy
from transformers import AutoTokenizer
from torch.utils.data import TensorDataset, Dataset
import json
import pickle
import numpy as np
import unicodedata
import itertools
import timeit
from allennlp.modules.token_embedders.embedding import Embedding
from tqdm import tqdm
logger = logging.getLogger(__name__)
train_data = None
dev_data = None
test_data = None
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, AlbertConfig)), ())
MODEL_CLASSES = {
'bertsub': (BertConfig, BertForACEBothOneDropoutSub, BertTokenizer),
'bertnonersub': (BertConfig, BertForACEBothOneDropoutSubNoNer, BertTokenizer),
'albertsub': (AlbertConfig, AlbertForACEBothOneDropoutSub, AlbertTokenizer),
}
task_ner_labels = {
'ace04': ['FAC', 'WEA', 'LOC', 'VEH', 'GPE', 'ORG', 'PER'],
'ace05': ['FAC', 'WEA', 'LOC', 'VEH', 'GPE', 'ORG', 'PER'],
'data': ['opinion','target'],
'scierc_1_sim': ['opinion','target'],
}
task_rel_labels = {
'ace04': ['PER-SOC', 'OTHER-AFF', 'ART', 'GPE-AFF', 'EMP-ORG', 'PHYS'],
'ace05': ['PER-SOC', 'ART', 'ORG-AFF', 'GEN-AFF', 'PHYS', 'PART-WHOLE'],
'data': ['POSITIVE','NEGATIVE','NEUTRAL'],
'scierc_1_sim': ['POSITIVE','NEGATIVE','NEUTRAL'],
}
BERT_TOKEN_MAPPING = {
"-LRB-": "(",
"-RRB-": ")",
"-LCB-": "{",
"-RCB-": "}",
"-LSB-": "[",
"-RSB-": "]",
"``": '"',
"''": '"',
"`": "'",
'«': '"',
'»': '"',
'‘': "'",
'’': "'",
'“': '"',
'”': '"',
'„': '"',
'‹': "'",
'›': "'",
"\u2013": "--", # en dash
"\u2014": "--", # em dash
}
REVERSE_TOKEN_MAPPING = dict([(value, key) for key, value in BERT_TOKEN_MAPPING.items()])
class ACEDataset(Dataset):
def __init__(self, tokenizer,dataset_list,args=None, evaluate=False, do_test=False, max_pair_length=None,dataset=None):
if not evaluate:
file_path = os.path.join(args.data_dir, args.train_file)
else:
if do_test:
if args.test_file.find('models')==-1:
file_path = os.path.join(args.data_dir, args.test_file)
else:
file_path = args.test_file
else:
if args.dev_file.find('models')==-1:
file_path = os.path.join(args.data_dir, args.dev_file)
else:
file_path = args.dev_file
# assert os.path.isfile(file_path)
self.file_path = file_path
self.tokenizer = tokenizer
self.max_seq_length = args.max_seq_length
self.max_pair_length = max_pair_length
self.max_entity_length = self.max_pair_length*2
self.evaluate = evaluate
self.use_typemarker = args.use_typemarker
self.local_rank = args.local_rank
self.args = args
self.model_type = args.model_type
self.no_sym = args.no_sym
self.cont_none = 0
self.cont_pos = 0
self.cont_neu = 0
self.cont_neg = 0
if args.data_dir.find('ace05')!=-1:
self.ner_label_list = ['NIL', 'FAC', 'WEA', 'LOC', 'VEH', 'GPE', 'ORG', 'PER']
if args.no_sym:
label_list = ['PER-SOC', 'ART', 'ORG-AFF', 'GEN-AFF', 'PHYS', 'PART-WHOLE']
self.sym_labels = ['NIL']
self.label_list = self.sym_labels + label_list
else:
label_list = ['ART', 'ORG-AFF', 'GEN-AFF', 'PHYS', 'PART-WHOLE']
self.sym_labels = ['NIL', 'PER-SOC']
self.label_list = self.sym_labels + label_list
elif args.data_dir.find('ace04')!=-1:
self.ner_label_list = ['NIL', 'FAC', 'WEA', 'LOC', 'VEH', 'GPE', 'ORG', 'PER']
if args.no_sym:
label_list = ['PER-SOC', 'OTHER-AFF', 'ART', 'GPE-AFF', 'EMP-ORG', 'PHYS']
self.sym_labels = ['NIL']
self.label_list = self.sym_labels + label_list
else:
label_list = ['OTHER-AFF', 'ART', 'GPE-AFF', 'EMP-ORG', 'PHYS']
self.sym_labels = ['NIL', 'PER-SOC']
self.label_list = self.sym_labels + label_list
elif args.data_dir.find('data')!=-1:
self.ner_label_list = ['opinion','target']
if args.no_sym:
label_list = ['POSITIVE','NEGATIVE','NEUTRAL']
self.sym_labels = ['NIL']
self.label_list = self.sym_labels + label_list
else:
label_list = ['POSITIVE','NEGATIVE','NEUTRAL']
self.sym_labels = ['NIL']
self.label_list = self.sym_labels + label_list
elif args.data_dir.find('scierc')!=-1:
self.ner_label_list = ['opinion','target']
if args.no_sym:
label_list = ['POSITIVE','NEGATIVE','NEUTRAL']
self.sym_labels = ['NIL']
self.label_list = self.sym_labels + label_list
else:
label_list = ['POSITIVE','NEGATIVE','NEUTRAL']
self.sym_labels = ['NIL']
self.label_list = self.sym_labels + label_list
else:
assert (False)
self._span_width_embedding = Embedding(
num_embeddings=args.num_width_embeddings, embedding_dim=args.span_width_embedding_dim
)
self.global_predicted_ners = {}
self.initialize(dataset_list,dataset,do_test)
def initialize(self,dataset_list,dataset,do_test):
tokenizer = self.tokenizer
vocab_size = tokenizer.vocab_size
max_num_subwords = self.max_seq_length - 4 # for two marker
label_map = {label: i for i, label in enumerate(self.label_list)}
ner_label_map = {label: i for i, label in enumerate(self.ner_label_list)}
def tokenize_word(text):
if (
isinstance(tokenizer, RobertaTokenizer)
and (text[0] != "'")
and (len(text) != 1 or not self.is_punctuation(text))
):
return tokenizer.tokenize(text, add_prefix_space=True)
return tokenizer.tokenize(text)
# with open(self.file_path, "r", encoding='utf-8') as f:
# DATA = json.load(f)
self.ner_tot_recall = 0
self.tot_recall = 0
self.data = []
self.ner_golden_labels = set([])
self.golden_labels = set([])
self.golden_labels_withner = set([])
self.aope_golden = set([])
maxR = 0
maxL = 0
# with open(self.file_path, 'r', encoding='utf-8') as f:
# DATAS = json.load(f)
all_unrolled = []
id_vis = []
all_temp_tokens = []
for l_idx, line in enumerate(dataset_list):
data = line
# if self.args.output_dir.find('test')!=-1:
# if len(self.data) > 70:
# break
sen_word = data['token']['sentences']
sentences = [data['token']['sentences']]
if 'predicted_ner' in data: # e2e predict
try:
ners = data['predicted_ner']
except:
ners = data['predicted_ner']
else:
ners = data['token']['ner']
std_ners = data['token']['ner']
relations = [data['token']['relations']]
whole_sent = " ".join(data['token']['sentences'])
for sentence_relation in relations:
for x in sentence_relation:
if x[4] in self.sym_labels[1:]:
print('has something sym')
self.tot_recall += 2
else:
self.tot_recall += 1
sentence_boundaries = [0]
words = []
L = 0
for i in range(len(sentences)):
L += len(sentences[i])
sentence_boundaries.append(L)
words += sentences[i]
tokens = [tokenize_word(w) for w in words]
subwords = [w for li in tokens for w in li]
maxL = max(maxL, len(subwords))
subword2token = list(itertools.chain(*[[i] * len(li) for i, li in enumerate(tokens)]))
token2subword = [0] + list(itertools.accumulate(len(li) for li in tokens))
subword_start_positions = frozenset(token2subword)
subword_sentence_boundaries = [sum(len(li) for li in tokens[:p]) for p in sentence_boundaries]
for n in range(len(subword_sentence_boundaries) - 1):
sentence_ners = ners[n]
sentence_relations = relations[n]
std_ner = std_ners[n]
std_entity_labels = {}
self.ner_tot_recall += len(std_ner)
for start, end, label in std_ner:
std_entity_labels[(start, end)] = label
self.ner_golden_labels.add( ((l_idx, n), (start, end), label) )
self.global_predicted_ners[(l_idx, n)] = list(sentence_ners)
doc_sent_start, doc_sent_end = subword_sentence_boundaries[n : n + 2]
left_length = doc_sent_start
right_length = len(subwords) - doc_sent_end
sentence_length = doc_sent_end - doc_sent_start
half_context_length = int((max_num_subwords - sentence_length) / 2)
if sentence_length < max_num_subwords:
if left_length < right_length:
left_context_length = min(left_length, half_context_length)
right_context_length = min(right_length, max_num_subwords - left_context_length - sentence_length)
else:
right_context_length = min(right_length, half_context_length)
left_context_length = min(left_length, max_num_subwords - right_context_length - sentence_length)
doc_offset = doc_sent_start - left_context_length
target_tokens = subwords[doc_offset : doc_sent_end + right_context_length]
target_tokens = [tokenizer.cls_token] + target_tokens[ : self.max_seq_length - 4] + [tokenizer.sep_token]
assert(len(target_tokens) <= self.max_seq_length - 2)
# if l_idx==199:
# print('================================')
pos2label = {}
for x in sentence_relations:
pos2label[(x[0],x[1],x[2],x[3])] = label_map[x[4]]
# if ((l_idx, n), (x[0],x[1]), (x[2],x[3]), x[4]) in self.golden_labels:
# print(l_idx,n,x[0],x[1],x[2],x[3],x[4],'已经在里面了')
self.aope_golden.add(((l_idx, n), (x[0],x[1]), (x[2],x[3])))
self.golden_labels.add(((l_idx, n), (x[0],x[1]), (x[2],x[3]), x[4]))#
self.golden_labels_withner.add(((l_idx, n), (x[0],x[1], std_entity_labels[(x[0], x[1])]), (x[2],x[3], std_entity_labels[(x[2], x[3])]), x[4]))
if x[4] in self.sym_labels[1:]:
self.golden_labels.add(((l_idx, n), (x[2],x[3]), (x[0],x[1]), x[4]))
self.golden_labels_withner.add(((l_idx, n), (x[2],x[3], std_entity_labels[(x[2], x[3])]), (x[0],x[1], std_entity_labels[(x[0], x[1])]), x[4]))
entities = list(sentence_ners)
for x in sentence_relations:
w = (x[2],x[3],x[0],x[1])# 对称关系 但是 在 ASTE任务中是否要考虑， target 和 opinion 之间的对称关系
if w not in pos2label:
if x[4] in self.sym_labels[1:]:
pos2label[w] = label_map[x[4]] # bug
else:
pos2label[w] = label_map[x[4]] + len(label_map) - len(self.sym_labels)
# if not self.evaluate:
# entities.append((10000, 10000, 'NIL')) # only for NER
for sub in entities:
# if sub[2]!='target':
# continue
cur_ins = []
if sub[0] < 10000:
sub_s = token2subword[sub[0]] - doc_offset + 1
try:
sub_e = token2subword[sub[1]+1] - doc_offset
except:
print(sub)
print(token2subword)
sub_label = ner_label_map[sub[2]]
if self.use_typemarker:
l_m = '<subj_start=%s>'%sub[2]
r_m = '<subj_end=%s>'%sub[2]
else:
l_m = '[unused0]'
r_m = '[unused1]'
#temp_tokens = words[:sub[0]] + [l_m] + words[sub[0]:sub[1] + 1] + [r_m] + words[sub[1] + 1:]
# all_temp_tokens.append(temp_tokens)
if not self.args.nosolid:
temp_tokens = words[:sub[0]] + [l_m] + words[sub[0]:sub[1] + 1] + [r_m] + words[sub[1] + 1:]
sub_tokens = target_tokens[:sub_s] + [l_m] + target_tokens[sub_s:sub_e+1] + [r_m] + target_tokens[sub_e+1: ]
sub_e += 2
else:
temp_tokens = words[:sub[0]]+ words[sub[0]:sub[1] + 1]+ words[sub[1] + 1:]
sub_tokens = target_tokens
else:
sub_s = len(target_tokens)
sub_e = len(target_tokens)+1
sub_tokens = target_tokens + ['[unused0]', '[unused1]']
sub_label = -1
if sub_e >= self.max_seq_length-1:
continue
# assert(sub_e < self.max_seq_length)
for start, end, obj_label in sentence_ners:
if self.model_type.endswith('nersub'):
if start==sub[0] and end==sub[1]:
continue
doc_entity_start = token2subword[start]
doc_entity_end = token2subword[end+1]
left = doc_entity_start - doc_offset + 1
right = doc_entity_end - doc_offset
obj = (start, end)
if not self.args.nosolid:
if obj[0] >= sub[0]:
left += 1
if obj[0] > sub[1]:
left += 1
if obj[1] >= sub[0]:
right += 1
if obj[1] > sub[1]:
right += 1
label = pos2label.get((sub[0], sub[1], obj[0], obj[1]), 0)#判断 sub 和 obj 是否有关系
if right >= self.max_seq_length-1:
continue
if label == 0:
self.cont_none += 1
elif label == 1 or label == 4:
self.cont_pos += 1
elif label == 2 or label == 5:
self.cont_neg += 1
else:
self.cont_neu += 1
cur_ins.append(((left, right, ner_label_map[obj_label]), label, obj))# 这里的 都是 左闭右开 （（谓语位置，谓语label）,主谓语关系，谓语位置（没加入[s][/s]））
# print('sub:',sub_tokens[sub_s:sub_e+1],"obj:",sub_tokens[left:right+1])
maxR = max(maxR, len(cur_ins))
dL = self.max_pair_length
if self.args.shuffle:
np.random.shuffle(cur_ins)
for i in range(0, len(cur_ins), dL):
examples = cur_ins[i : i + dL]
item = {
'index': (l_idx, n),
'sentence': sub_tokens,
'temp_sentence': temp_tokens,
'examples': examples,
'sub': (sub, (sub_s, sub_e), sub_label), #(sub[0], sub[1], sub_label),
'whole_sen':whole_sent,
'sen_word':sen_word,
'origion_adj': data['graph'],
}
self.data.append(item)
# if dataset == 'train':
#
# with open('sentence_list_train.json', 'w') as json_file:
# # 使用json.dump将列表写入文件
# json.dump(all_temp_tokens, json_file)
# elif dataset == 'dev' and do_test == False:
# with open('sentence_list_dev.json', 'w') as json_file:
# # 使用json.dump将列表写入文件
# json.dump(all_temp_tokens, json_file)
# else:
# with open('sentence_list_test.json', 'w') as json_file:
# # 使用json.dump将列表写入文件
# json.dump(all_temp_tokens, json_file)
print("none:",self.cont_none)
print("pos:",self.cont_pos)
print("neu:",self.cont_neu)
print("neg:",self.cont_neg)
logger.info('maxR: %s', maxR)
logger.info('maxL: %s', maxL)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
entry = self.data[idx]
ori_adj = entry['origion_adj']
#===========================
tokens_t = []
tok2ori_map_t = []
cont = 0
for ori_i, w in enumerate(entry['temp_sentence']):
if w == '[unused0]' or w == '<subj_start=opinion>' or w == '<subj_start=target>':
tokens_t.append(w)
tok2ori_map_t.append(-1)#实体标记和其他没有关系
continue
elif w == '[unused1]' or w == '<subj_end=opinion>' or w == '<subj_end=target>':
tokens_t.append(w)
tok2ori_map_t.append(-1)
continue
for t in self.args.tokenizer.tokenize(w):
tokens_t.append(t)
tok2ori_map_t.append(cont)
cont += 1
truncate_tok_len = len(tokens_t)
tok_adj = np.zeros(
(truncate_tok_len, truncate_tok_len), dtype='float32')
for i in range(truncate_tok_len):
for j in range(truncate_tok_len):
try:
if tok2ori_map_t[i]!=-1 and tok2ori_map_t[j]!=-1:
tok_adj[i][j] = ori_adj[tok2ori_map_t[i]][tok2ori_map_t[j]]
except:
tok_adj[i][j] = ori_adj[tok2ori_map_t[i]][tok2ori_map_t[j]]
context_matrix = np.zeros(
(self.max_seq_length + self.max_pair_length * 2, self.max_seq_length + self.max_pair_length * 2)).astype('float64')
context_matrix[1:truncate_tok_len + 1, 1:truncate_tok_len + 1] = tok_adj
# context_matrix[truncate_tok_len + 1,truncate_tok_len + 1] = 1
np.fill_diagonal(context_matrix, 1)
#===========================
sub, sub_position, sub_label = entry['sub']
input_ids = self.tokenizer.convert_tokens_to_ids(entry['sentence'])
L = len(input_ids)
input_ids += [self.tokenizer.pad_token_id] * (self.max_seq_length - len(input_ids))
attention_mask = torch.zeros((self.max_entity_length+self.max_seq_length, self.max_entity_length+self.max_seq_length), dtype=torch.int64)
attention_mask[:L, :L] = 1
if self.model_type.startswith('albert'):
input_ids = input_ids + [30002] * (len(entry['examples'])) + [self.tokenizer.pad_token_id] * (self.max_pair_length - len(entry['examples']))
input_ids = input_ids + [30003] * (len(entry['examples'])) + [self.tokenizer.pad_token_id] * (self.max_pair_length - len(entry['examples'])) # for debug
else:
input_ids = input_ids + [3] * (len(entry['examples'])) + [self.tokenizer.pad_token_id] * (self.max_pair_length - len(entry['examples']))# [3] 表示obj 开头的部分
input_ids = input_ids + [4] * (len(entry['examples'])) + [self.tokenizer.pad_token_id] * (self.max_pair_length - len(entry['examples'])) # for debug [4]表示 obj 结尾的部分
labels = []
ner_labels = []
mention_pos = []
mention_2 = []
position_ids = list(range(self.max_seq_length)) + [0] * self.max_entity_length
num_pair = self.max_pair_length
for x_idx, obj in enumerate(entry['examples']):#遍历所有的 sub, obj对
m2 = obj[0]
label = obj[1]
mention_pos.append((m2[0], m2[1]))
mention_2.append(obj[2])
w1 = x_idx
w2 = w1 + num_pair
w1 += self.max_seq_length
w2 += self.max_seq_length
position_ids[w1] = m2[0]#obj 的开头位置
position_ids[w2] = m2[1]#obj end 的位置
for xx in [w1, w2]:
for yy in [w1, w2]:
attention_mask[xx, yy] = 1 #marker 可以互相看到
attention_mask[xx, :L] = 1 # marker 可以看到句子
labels.append(label)
ner_labels.append(m2[2])
l_m = '<subj_start=opinion>'
r_m = '<subj_end=opinion>'
if self.use_typemarker and not self.args.nosolid:
if label == 0:
l_m = '<subj_start=opinion>'
r_m = '<subj_end=opinion>'
elif label == 1:
l_m = '<subj_start=target>'
r_m = '<subj_end=target>'
l_m = self.tokenizer.encode(l_m)
r_m = self.tokenizer.encode(r_m)
input_ids[w1] = l_m[1]
input_ids[w2] = r_m[1]
pair_L = len(entry['examples'])
if self.args.att_left:
attention_mask[self.max_seq_length : self.max_seq_length+pair_L, self.max_seq_length : self.max_seq_length+pair_L] = 1
if self.args.att_right:
attention_mask[self.max_seq_length+num_pair : self.max_seq_length+num_pair+pair_L, self.max_seq_length+num_pair : self.max_seq_length+num_pair+pair_L] = 1
mention_pos += [(0, 0)] * (num_pair - len(mention_pos))
labels += [-1] * (num_pair - len(labels))
ner_labels += [-1] * (num_pair - len(ner_labels))
t = entry['sen_word']
t = t + ['pad'] * (self.max_seq_length - len(t))
# te1 = np.zeros((self.max_seq_length + num_pair * 2, self.max_seq_length + num_pair * 2))#在没加[cls][sep] 以及[unused0] 和wordpiece下的 句子的邻接矩阵
# te1[:len(entry['origion_adj'][0]), :len(entry['origion_adj'][0])] = entry['origion_adj']
# te1 = te1.astype(float)
# np.fill_diagonal(te1, 1)
# te2 = np.zeros((self.max_seq_length + num_pair * 2, self.max_seq_length + num_pair * 2))#都有
# te2[:len(adj2[0]), :len(adj2[0])] = adj2
# te2 = te2.astype(float)
# np.fill_diagonal(te2, 1)
#sub_width_embedding
sub_width = [sub[1] - sub[0]]
sub_width = torch.tensor(sub_width)
sub_width = torch.unsqueeze(sub_width,0)
sub_max_value = torch.max(sub_width).item()
if sub_max_value >= self.args.num_width_embeddings:
sub_span_width_embedding_over = Embedding(num_embeddings=sub_max_value + 1,
embedding_dim=self.args.span_width_embedding_dim)
sub_width_embeddings = sub_span_width_embedding_over(sub_width)
else:
sub_width_embeddings = self._span_width_embedding(sub_width)
sub_width_embeddings = torch.squeeze(sub_width_embeddings, 0)
#obj_width_embedding
padded_x = torch.zeros((1,num_pair, 20))
obj_width = [i[2][1] - i[2][0] for i in entry['examples']]
obj_width = torch.tensor(obj_width)
obj_width = torch.unsqueeze(obj_width,0)
obj_max_value = torch.max(obj_width).item()
if obj_max_value >= self.args.num_width_embeddings:
_span_width_embedding_over = Embedding(num_embeddings=obj_max_value+1, embedding_dim=self.args.span_width_embedding_dim)
obj_width_embeddings = _span_width_embedding_over(obj_width)
else:
obj_width_embeddings = self._span_width_embedding(obj_width)
padded_x[:, :obj_width.size()[1], :] = obj_width_embeddings
obj_width_embeddings = padded_x
obj_width_embeddings = torch.squeeze(obj_width_embeddings,0)
item = [torch.tensor(input_ids),
attention_mask,
torch.tensor(position_ids),
torch.tensor(sub_position),
torch.tensor(mention_pos),
torch.tensor(labels, dtype=torch.int64),
torch.tensor(ner_labels, dtype=torch.int64),
torch.tensor(sub_label, dtype=torch.int64),
torch.tensor(context_matrix),
sub_width_embeddings.detach(),
obj_width_embeddings.detach(),
entry['whole_sen'],
t,
]
if self.evaluate:
item.append(entry['index'])
item.append(sub)
item.append(mention_2)
return item
@staticmethod
def collate_fn(batch):
fields = [x for x in zip(*batch)]
num_metadata_fields = 3
try:
stacked_fields = [torch.stack(field) for field in fields[:-num_metadata_fields-2]] # don't stack metadata fields
#stacked_fields = [torch.stack(field) for field in fields[:-num_metadata_fields]]
stacked_fields.extend(fields[-num_metadata_fields-2:]) # add them as lists not torch tensors
except:
print('error')
return stacked_fields
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def _rotate_checkpoints(args, checkpoint_prefix, use_mtime=False):
if not args.save_total_limit:
return
if args.save_total_limit <= 0:
return
# Check if we should delete older checkpoint(s)
glob_checkpoints = glob.glob(os.path.join(args.output_dir, '{}-*'.format(checkpoint_prefix)))
if len(glob_checkpoints) <= args.save_total_limit:
return
ordering_and_checkpoint_path = []
for path in glob_checkpoints:
if use_mtime:
ordering_and_checkpoint_path.append((os.path.getmtime(path), path))
else:
regex_match = re.match('.*{}-([0-9]+)'.format(checkpoint_prefix), path)
if regex_match and regex_match.groups():
ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))
checkpoints_sorted = sorted(ordering_and_checkpoint_path)
checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit)
checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
for checkpoint in checkpoints_to_be_deleted:
logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint))
shutil.rmtree(checkpoint)
def read_sentence_depparsed(file_path):
with open(file_path, 'r') as f:
data = json.load(f)
return data
def get_dataset(dataset_name,args):
train = list(read_sentence_depparsed(dataset_name + '/train.json'))
dev = list(read_sentence_depparsed(dataset_name + '/dev.json'))
test_file_path = args.test_file
test = list(read_sentence_depparsed(test_file_path))
print('测试集位置：',test_file_path)
return train, dev, test
def train(args, model, tokenizer,dataset_list,dev_list):
""" Train the model """
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter("logs/"+args.data_dir[max(args.data_dir.rfind('/'),0):]+"_re_logs/"+args.output_dir[args.output_dir.rfind('/'):])
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_dataset = ACEDataset(tokenizer=tokenizer,dataset_list=dataset_list,args=args, max_pair_length=args.max_pair_length,dataset='train')
train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, num_workers=4*int(args.output_dir.find('test')==-1))
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
if args.warmup_steps==-1:
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=int(0.1*t_total), num_training_steps=t_total
)
else:
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
# ori_model = model
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1))
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
tr_loss, logging_loss = 0.0, 0.0
tr_ner_loss, logging_ner_loss = 0.0, 0.0
tr_re_loss, logging_re_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
set_seed(args) # Added here for reproductibility (even between python 2 and 3)
best_f1 = -1
for _ in train_iterator:
if args.shuffle and _ > 0:
train_dataset.initialize()
epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
for step, batch in enumerate(epoch_iterator):
model.train()
#sents = batch[-1]
batch = tuple(t.to(args.device) for t in batch[:-2])
#batch = tuple(t.to(args.device) for t in batch[:-1])
inputs = {'input_ids': batch[0],
'attention_mask': batch[1],
'position_ids': batch[2],
'labels': batch[5],
'ner_labels': batch[6],
'adj': batch[8],
'sub_width_embedding': batch[9],
'obj_width_embedding': batch[10],
}
inputs['sub_positions'] = batch[3]
if args.model_type.find('span')!=-1:
inputs['mention_pos'] = batch[4]
if args.model_type.endswith('bertonedropoutnersub'):
inputs['sub_ner_labels'] = batch[7]
outputs = model(**inputs)
loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc)
re_loss = outputs[1]
ner_loss = outputs[2]
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
re_loss = re_loss / args.gradient_accumulation_steps
ner_loss = ner_loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.item()
if re_loss > 0:
tr_re_loss += re_loss.item()
if ner_loss > 0:
tr_ner_loss += ner_loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.max_grad_norm > 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
# if args.model_type.endswith('rel') :
# ori_model.bert.encoder.layer[args.add_coref_layer].attention.self.relative_attention_bias.weight.data[0].zero_() # 可以手动乘个mask
if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Log metrics
tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
logging_loss = tr_loss
tb_writer.add_scalar('RE_loss', (tr_re_loss - logging_re_loss)/args.logging_steps, global_step)
logging_re_loss = tr_re_loss
tb_writer.add_scalar('NER_loss', (tr_ner_loss - logging_ner_loss)/args.logging_steps, global_step)
logging_ner_loss = tr_ner_loss
if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # valid for bert/spanbert
update = True
# Save model checkpoint
if args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(args,dev_list, model, tokenizer)
f1 = results['f1_with_ner']
tb_writer.add_scalar('f1_with_ner', f1, global_step)
if f1 > best_f1:
best_f1 = f1
print ('Best F1', best_f1)
else:
update = False
if update:
checkpoint_prefix = 'checkpoint'
output_dir = os.path.join(args.output_dir, '{}-{}'.format(checkpoint_prefix, global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, 'training_args.bin'))
logger.info("Saving model checkpoint to %s", output_dir)
_rotate_checkpoints(args, checkpoint_prefix)
if args.max_steps > 0 and global_step > args.max_steps:
epoch_iterator.close()
break
if args.max_steps > 0 and global_step > args.max_steps:
train_iterator.close()
break
if args.local_rank in [-1, 0]:
tb_writer.close()
return global_step, tr_loss / global_step, best_f1
def to_list(tensor):
return tensor.detach().cpu().tolist()
def evaluate(args,dataset_list,model, tokenizer, prefix="", do_test=False):
eval_output_dir = args.output_dir
eval_dataset = ACEDataset(tokenizer=tokenizer,dataset_list=dataset_list, args=args, evaluate=True, do_test=do_test, max_pair_length=args.max_pair_length,dataset='dev')
aope_golden = set(eval_dataset.aope_golden)
golden_labels = set(eval_dataset.golden_labels)# 正确的 三元组关系
golden_labels_withner = set(eval_dataset.golden_labels_withner)# 正确的带有NER标签的 三元组关系
label_list = list(eval_dataset.label_list)
sym_labels = list(eval_dataset.sym_labels)
tot_recall = eval_dataset.tot_recall#有一个重复了
if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
os.makedirs(eval_output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
scores = defaultdict(dict)
# ner_pred = not args.model_type.endswith('noner')
example_subs = set([])
num_label = len(label_list)
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Batch size = %d", args.eval_batch_size)
model.eval()
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=ACEDataset.collate_fn, num_workers=4*int(args.output_dir.find('test')==-1))
# Eval!
logger.info(" Num examples = %d", len(eval_dataset))
start_time = timeit.default_timer()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
indexs = batch[-3]
subs = batch[-2]
batch_m2s = batch[-1]
ner_labels = batch[6]
SEN = batch[-5]#整个句子
sen_word = batch[-4]#单个单词
batch = tuple(t.to(args.device) for t in batch[:11])
with torch.no_grad():
inputs = {'input_ids': batch[0],
'attention_mask': batch[1],
'position_ids': batch[2],
'adj': batch[8],
# 'labels': batch[4],
# 'ner_labels': batch[5],
'sub_width_embedding':batch[9],
'obj_width_embedding':batch[10],
}
inputs['sub_positions'] = batch[3]
if args.model_type.find('span')!=-1:
inputs['mention_pos'] = batch[4]
outputs = model(**inputs)
logits = outputs[0]
if args.eval_logsoftmax: # perform a bit better
logits = torch.nn.functional.log_softmax(logits, dim=-1)
elif args.eval_softmax:
logits = torch.nn.functional.softmax(logits, dim=-1)
if args.use_ner_results or args.model_type.endswith('nonersub'):
ner_preds = ner_labels# 判断是否使用 ner 结果 ，即是否使用真正的NER
else:
ner_preds = torch.argmax(outputs[1], dim=-1)#否则 用预测的结果
logits = logits.cpu().numpy()
ner_preds = ner_preds.cpu().numpy()# 这里长度 指的是每个标签 每个值对应该标签的ner_label
for i in range(len(indexs)):#第i 个样例（1batch中有多个样例）
index = indexs[i]
sub = subs[i]
m2s = batch_m2s[i]
example_subs.add(((index[0], index[1]), (sub[0], sub[1])))
for j in range(len(m2s)):#该样例中的一个 term
obj = m2s[j]