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run_hgn.py
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run_hgn.py
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from __future__ import absolute_import, division, print_function
import argparse
import csv
import json
import logging
import os
import random
import sys
import time
#import truecase
import re
import numpy as np
import torch
import torch.nn.functional as F
import torch.nn.functional as F
# from pytorch_transformers import (WEIGHTS_NAME, AdamW, BertConfig,
# BertForTokenClassification, BertTokenizer,
# WarmupLinearSchedule)
from transformers import WEIGHTS_NAME, AdamW, AutoConfig, AutoTokenizer, get_linear_schedule_with_warmup
from transformers import (PreTrainedModel, AutoModel,AutoConfig)
from torch import nn
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from seqeval.metrics import classification_report
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO)
logger = logging.getLogger(__name__)
from HGN import HGNER
class InputExample(object):
"""A single training/test example for simple sequence classification."""
def __init__(self, guid, text_a, text_b=None, label=None, domain_label=None):
"""Constructs a InputExample.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
self.guid = guid
self.text_a = text_a
self.text_b = text_b
self.label = label
self.domain_label = domain_label
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self, input_ids, input_mask, segment_ids, label_id, valid_ids=None, label_mask=None, domain_label=None, seq_len=None):
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.label_id = label_id
self.valid_ids = valid_ids
self.label_mask = label_mask
self.domain_label = domain_label
self.seq_len = seq_len
def readfile(filename, type_=None):
'''
read file
'''
f = open(filename)
data = []
sentence = []
label= []
for line in f:
if len(line)==0 or line.startswith('-DOCSTART') or line[0]=="\n":
if len(sentence) > 0:
assert len(sentence) == len(label)
data.append((sentence,label))
sentence = []
label = []
continue
#TODO
if type_!='predict':
splits = line.split()
#print(splits)
sentence.append(splits[0])
label.append(splits[-1])
#domain_l = eval(splits[2])
else:
splits = line.strip().split()
sentence.append(splits[0])
label.append('O')
if len(sentence) >0:
data.append((sentence,label))
assert len(sentence) == len(label)
sentence = []
label = []
return data
def readfile_label(train_file, test_file, dev_file):
'''
read file
'''
label_dict = {}
f_train = open(train_file)
f_test = open(test_file)
f_dev = open(dev_file)
for line in f_train:
temp = line.strip()
if temp!='':
splits = line.strip().split()
if splits[-1] != 'O':
label_dict[splits[-1]] = 0
for line in f_test:
temp = line.strip()
if temp!='':
splits = line.strip().split()
if splits[-1] != 'O':
label_dict[splits[-1]] = 0
for line in f_dev:
temp = line.strip()
if temp!='':
splits = line.strip().split()
if splits[-1] != 'O':
label_dict[splits[-1]] = 0
return label_dict
class DataProcessor(object):
"""Base class for data converters for sequence classification data sets."""
def get_train_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the train set."""
raise NotImplementedError()
def get_dev_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the dev set."""
raise NotImplementedError()
def get_predict_examples(self, data_dir):
raise NotImplementedError()
def get_labels(self, label_list):
"""Gets the list of labels for this data set."""
raise NotImplementedError()
@classmethod
def _read_tsv(cls, input_file, quotechar=None, type_=None):
"""Reads a tab separated value file."""
return readfile(input_file, type_)
class NerProcessor(DataProcessor):
"""Processor for the CoNLL-2003 data set."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(data_dir,type_='train'), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(data_dir, type_='dev'), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(data_dir, type_='test'), "test")
def get_predict_examples(self, data_dir):
return self._create_examples(
self._read_tsv(data_dir, type_='predict'), "predict")
def get_labels(self, label_list):
#return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "[CLS]", "[SEP]"]
f_label_list=[]
f_label_list.append("O")
for i in label_list:
f_label_list.append("B-"+i)
f_label_list.append("I-"+i)
f_label_list.append("E-"+i)
f_label_list.append("S-"+i)
f_label_list.append("[CLS]")
f_label_list.append("[SEP]")
return f_label_list
'''
final_label_list = []
final_label_list.append("O")
for keys, values in label_list.items():
final_label_list.append('B-'+keys)
final_label_list.append('I-'+keys)
f_label_list.append("E-"+i)
f_label_list.append("S-"+i)
final_label_list.append("[CLS]")
final_label_list.append("[SEP]")
return final_label_list
'''
def _create_examples(self,lines,set_type):
examples = []
for i,(sentence,label) in enumerate(lines):
guid = "%s-%s" % (set_type, i)
text_a = '\t\t'.join(sentence)
text_b = None
label = label
#domain_label = domain_label
examples.append(InputExample(guid=guid,text_a=text_a,text_b=text_b,label=label))
return examples
def truecase_sentence(tokens):
word_lst = [(w, idx) for idx, w in enumerate(tokens) if all(c.isalpha() for c in w)]
lst = [w for w, _ in word_lst if re.match(r'\b[A-Z\.\-]+\b', w)]
if len(lst) and len(lst) == len(word_lst):
parts = truecase.get_true_case(' '.join(lst)).split()
# the trucaser have its own tokenization ...
# skip if the number of word dosen't match
if len(parts) != len(word_lst): return tokens
for (w, idx), nw in zip(word_lst, parts):
tokens[idx] = nw
return tokens
def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):
"""Loads a data file into a list of `InputBatch`s."""
label_map = {label : i for i, label in enumerate(label_list,1)}
features = []
ori_sents = []
#seq_lens = []
for (ex_index,example) in enumerate(examples):
textlist = example.text_a.split('\t\t')
#textlist = truecase_sentence(textlist)
ori_sents.append(textlist)
labellist = example.label
#domain_label = []
#domain_label.append(example.domain_label)
tokens = []
labels = []
valid = []
label_mask = []
#
seq_len = []
seq_len.append(len(textlist))
for i, word in enumerate(textlist):
token = tokenizer.tokenize(word)
tokens.extend(token)
label_1 = labellist[i]
for m in range(len(token)):
if m == 0:
labels.append(label_1)
valid.append(1)
label_mask.append(1)
else:
valid.append(0)
if len(tokens) >= max_seq_length - 1:
tokens = tokens[0:(max_seq_length - 2)]
labels = labels[0:(max_seq_length - 2)]
valid = valid[0:(max_seq_length - 2)]
label_mask = label_mask[0:(max_seq_length - 2)]
#assert len(tokens) == len(labels)
ntokens = []
segment_ids = []
label_ids = []
ntokens.append("[CLS]")
segment_ids.append(0)
valid.insert(0,1)
label_mask.insert(0,1)
label_ids.append(label_map["[CLS]"])
for i, token in enumerate(tokens):
ntokens.append(token)
segment_ids.append(0)
if len(labels) > i:
try:
label_ids.append(label_map[labels[i]])
except:
print(tokens)
print(labels)
#print(len(tokens))
#print(len(labels))
time.sleep(100)
ntokens.append("[SEP]")
segment_ids.append(0)
valid.append(1)
label_mask.append(1)
label_ids.append(label_map["[SEP]"])
input_ids = tokenizer.convert_tokens_to_ids(ntokens)
input_mask = [1] * len(input_ids)
label_mask = [1] * len(label_ids)
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
label_ids.append(0)
valid.append(1)
label_mask.append(0)
while len(label_ids) < max_seq_length:
label_ids.append(0)
label_mask.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(label_ids) == max_seq_length
assert len(valid) == max_seq_length
assert len(label_mask) == max_seq_length
#if ex_index < 5:
# logger.info("*** Example ***")
# logger.info("guid: %s" % (example.guid))
# logger.info("tokens: %s" % " ".join(
# [str(x) for x in tokens]))
# logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
# logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
# logger.info(
# "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
# logger.info("label: %s (id = %d)" % (example.label, label_ids))
features.append(
InputFeatures(input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_ids,
valid_ids=valid,
label_mask=label_mask,
seq_len=seq_len))
return features, ori_sents
def setup_seed(seed):
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
random.seed(seed)
np.random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmard = False
torch.random.manual_seed(seed)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--train_data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--dev_data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--test_data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--gpu_id",
default=None,
nargs='+',
type=str,
required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--use_crf",
action='store_true',
help="Whether use crf")
parser.add_argument("--bert_model", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument("--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.")
## Other parameters
parser.add_argument("--cache_dir",
default="",
type=str,
help="Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument("--label_list",
default=["O"],
type=str,
nargs='+',
help="Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument("--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval or not.")
parser.add_argument("--do_predict",
action='store_true',
help="Whether to run eval or not.")
parser.add_argument("--eval_on",
default="dev",
help="Whether to run eval on the dev set or test set.")
parser.add_argument("--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion",
default=0.1,
type=float,
help="Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--weight_decay", default=0.01, type=float,
help="Weight deay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-8, type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=1.0, type=float,
help="Max gradient norm.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument('--gradient_accumulation_steps',
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument('--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--fp16_opt_level', type=str, default='O1',
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument('--loss_scale',
type=float, default=0,
help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
parser.add_argument("--hidden_dropout_prob",
default=0.1,
type=float,
help="hidden_dropout_prob")
parser.add_argument("--window_size",
default=-1,
type=int,
help="window_size")
parser.add_argument("--d_model",
default=1024,
type=int,
help="pre-trained model size")
#####
parser.add_argument("--use_bilstm",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--use_single_window",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--use_multiple_window",
action='store_true',
help="Set this flag if you are using an multiple.")
parser.add_argument("--use_global_lstm",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--use_n_gram",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument('--windows_list', type=str, default='', help="window list")
parser.add_argument('--connect_type', type=str, default='add', help="window list")
args = parser.parse_args()
# if not args.do_train and not args.do_eval:
# raise ValueError("At least one of `do_train` or `do_eval` must be True.")
if args.do_train:
if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
handler = logging.FileHandler(args.output_dir+'/log.txt', encoding='UTF-8')
logger.addHandler(handler)
gpu_ids = ''
for ids in args.gpu_id:
gpu_ids = gpu_ids + str(ids) +','
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_ids
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
processors = {"ner":NerProcessor}
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
#random.seed(args.seed)
#np.random.seed(args.seed)
#torch.manual_seed(args.seed)
#torch.cuda.manual_seed(args.seed)
#torch.cuda.manual_seed_all(args.seed)
setup_seed(args.seed)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
tokenizer = AutoTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
train_examples = None
num_train_optimization_steps = 0
if args.do_train:
train_examples = processor.get_train_examples(args.train_data_dir)
#num_train_optimization_steps = int(
# len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
label_dict = readfile_label(args.train_data_dir, args.test_data_dir, args.dev_data_dir)
#label_list = processor.get_labels(args.label_list)
label_list = []
label_list.append('O')
for keys,_ in label_dict.items():
label_list.append(keys)
label_list.append("[CLS]")
label_list.append("[SEP]")
num_labels = len(label_list) + 1
logger.info(args)
# Prepare model
model = HGNER(args,
hidden_dropout_prob=args.hidden_dropout_prob,
num_labels=num_labels,
windows_list = [int(k) for k in args.windows_list.split('qq')] if args.windows_list else args.window_size,
)
n_params = sum([p.nelement() for p in model.parameters()])
print('n_params',n_params)
#my_lstm = LSTMLayer(config.hidden_size)
if args.local_rank == 0:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
#for name, param in model.named_parameters():
# if name.startswith('embeddings'):
# param.requires_grad = False
#param_optimizer = list(model.named_parameters())
#param_optimizer_new = []
#for i in param_optimizer:
# if i[0].startswith('bert.embeddings'):
# continue
# param_optimizer_new.append(i)
model.to(device)
#param_optimizer = param_optimizer_new
param_optimizer = list(model.named_parameters())
no_decay = ['bias','LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
warmup_steps = int(args.warmup_proportion * num_train_optimization_steps)
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
# scheduler = WarmupLinearSchedule(optimizer, warmup_steps=warmup_steps, t_total=num_train_optimization_steps)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=num_train_optimization_steps)
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)
# multi-gpu training (should be after apex fp16 initialization)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
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)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
label_map = {i : label for i, label in enumerate(label_list,1)}
logger.info("*** Label map ***")
logger.info(label_map)
logger.info("*******************************************")
best_epoch = -1
best_p = -1
best_r = -1
best_f = -1
best_test_f = -1
best_eval_f = -1
if args.do_train:
#load train data
train_features,_ = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
all_valid_ids = torch.tensor([f.valid_ids for f in train_features], dtype=torch.long)
all_lmask_ids = torch.tensor([f.label_mask for f in train_features], dtype=torch.long)
#all_domain_l = torch.tensor([f.domain_label for f in train_features], dtype=torch.long)
all_seq_lens = torch.tensor([f.seq_len for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids,all_valid_ids,all_lmask_ids, all_seq_lens)
#load valid data
eval_examples = processor.get_dev_examples(args.dev_data_dir)
eval_features,_ = convert_examples_to_features(eval_examples, label_list, args.max_seq_length, tokenizer)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
all_valid_ids = torch.tensor([f.valid_ids for f in eval_features], dtype=torch.long)
all_lmask_ids = torch.tensor([f.label_mask for f in eval_features], dtype=torch.long)
#all_domain_l = torch.tensor([f.domain_label for f in eval_features], dtype=torch.long)
all_seq_lens = torch.tensor([f.seq_len for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids,all_valid_ids,all_lmask_ids, all_seq_lens)
#load test data
test_examples = processor.get_test_examples(args.test_data_dir)
test_features,_ = convert_examples_to_features(test_examples, label_list, args.max_seq_length, tokenizer)
all_input_ids_dev = torch.tensor([f.input_ids for f in test_features], dtype=torch.long)
all_input_mask_dev = torch.tensor([f.input_mask for f in test_features], dtype=torch.long)
all_segment_ids_dev = torch.tensor([f.segment_ids for f in test_features], dtype=torch.long)
all_label_ids_dev = torch.tensor([f.label_id for f in test_features], dtype=torch.long)
all_valid_ids_dev = torch.tensor([f.valid_ids for f in test_features], dtype=torch.long)
all_lmask_ids_dev = torch.tensor([f.label_mask for f in test_features], dtype=torch.long)
#all_domain_l = torch.tensor([f.domain_label for f in test_features], dtype=torch.long)
all_seq_lens_dev = torch.tensor([f.seq_len for f in test_features], dtype=torch.long)
test_data = TensorDataset(all_input_ids_dev, all_input_mask_dev, all_segment_ids_dev, all_label_ids_dev, all_valid_ids_dev, all_lmask_ids_dev, all_seq_lens_dev)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
test_sampler = SequentialSampler(test_data)
test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=args.eval_batch_size)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
test_f1 = []
dev_f1 = []
for epoch_ in trange(int(args.num_train_epochs), desc="Epoch"):
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
# begin_time = time.time()
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids, valid_ids,l_mask, seq_len = batch
loss = model(input_ids, segment_ids, input_mask, label_ids,valid_ids,l_mask)#, seq_len=seq_len)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
# end_time = time.time()
# print('one step时间',end_time-begin_time)
# eval in each epoch.
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
y_true = []
y_pred = []
label_map = {i : label for i, label in enumerate(label_list,1)}
for input_ids, input_mask, segment_ids, label_ids,valid_ids,l_mask, seq_len in eval_dataloader:
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
valid_ids = valid_ids.to(device)
label_ids = label_ids.to(device)
l_mask = l_mask.to(device)
seq_len = seq_len.to(device)
#domain_l = domain_l.to(device)
with torch.no_grad():
logits = model(input_ids, segment_ids, input_mask,valid_ids=valid_ids,attention_mask_label=l_mask)#, seq_len=seq_len)
if not args.use_crf:
logits = torch.argmax(F.log_softmax(logits, dim=2), dim=2)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
input_mask = input_mask.to('cpu').numpy()
for i, label in enumerate(label_ids):
temp_1 = []
temp_2 = []
for j,m in enumerate(label):
if j == 0:
continue
elif label_ids[i][j] == len(label_map):
y_true.append(temp_1)
y_pred.append(temp_2)
break
else:
temp_1.append(label_map[label_ids[i][j]])
try:
temp_2.append(label_map[logits[i][j]])
except:
temp_2.append('O')
#temp_2.append(label_map[logits[i][j]])
report = classification_report(y_true, y_pred,digits=4)
logger.info("\n******evaluate on the dev data*******")
logger.info("\n%s", report)
temp = report.split('\n')[-3]
f_eval = eval(temp.split()[-2])
dev_f1.append(f_eval)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
#if os.path.exists(output_eval_file):
with open(output_eval_file, "a") as writer:
#logger.info("***** Eval results *****")
#logger.info("=======token level========")
#logger.info("\n%s", report)
#logger.info("=======token level========")
writer.write('*******************epoch*******'+str(epoch_)+'\n')
writer.write(report+'\n')
y_true = []
y_pred = []
label_map = {i : label for i, label in enumerate(label_list,1)}
for input_ids, input_mask, segment_ids, label_ids,valid_ids,l_mask, seq_len in test_dataloader:
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
valid_ids = valid_ids.to(device)
label_ids = label_ids.to(device)
l_mask = l_mask.to(device)
seq_len = seq_len
#domain_l = domain_l.to(device)
with torch.no_grad():
logits = model(input_ids, segment_ids, input_mask,valid_ids=valid_ids,attention_mask_label=l_mask)
shape = logits.shape
if len(shape) < 3:
logits = logits.unsqueeze(dim=0)
try:
if not args.use_crf:
logits = torch.argmax(F.log_softmax(logits,dim=2),dim=2)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
input_mask = input_mask.to('cpu').numpy()
except:
import pdb
pdb.set_trace()
for i, label in enumerate(label_ids):
temp_1 = []
temp_2 = []
for j,m in enumerate(label):
if j == 0:
continue
elif label_ids[i][j] == len(label_map):
y_true.append(temp_1)
y_pred.append(temp_2)
#print(temp_2)
#time.sleep(5)
break
else:
temp_1.append(label_map[label_ids[i][j]])
try:
temp_2.append(label_map[logits[i][j]])
except:
temp_2.append('O')
#temp_2.append(label_map[logits[i][j]])
report = classification_report(y_true, y_pred,digits=4)
logger.info("\n******evaluate on the test data*******")
logger.info("\n%s", report)
temp = report.split('\n')[-3]
f_test = eval(temp.split()[-2])
test_f1.append(f_test)
output_eval_file_t = os.path.join(args.output_dir, "test_results.txt")
#if os.path.exists(output_eval_file):
with open(output_eval_file_t, "a") as writer2:
#logger.info("***** Eval results *****")
#logger.info("=======token level========")
#logger.info("\n%s", report)
#logger.info("=======token level========")
writer2.write('*******************epoch*******'+str(epoch_)+'\n')
writer2.write(report+'\n')
# Load a trained model and config that you have fine-tuned
output_f1_test = os.path.join(args.output_dir, "f1_score_epoch.txt")
with open(output_f1_test, "w") as writer1:
for i, j in zip(test_f1, dev_f1):
writer1.write(str(i) + '\t' + str(j) + '\n')
writer1.write('\n')
writer1.write(str(best_test_f))
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
model = Ner.from_pretrained(args.output_dir)
tokenizer = AutoTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
model.to(device)
#if args.eval_on == "dev":
# eval_examples = processor.get_dev_examples(args.data_dir)
#elif args.eval_on == "test":
eval_examples = processor.get_test_examples(args.test_data_dir)
#else:
# raise ValueError("eval on dev or test set only")
eval_features,_ = convert_examples_to_features(eval_examples, label_list, args.max_seq_length, tokenizer)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)