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TaskForSQuADQuestionAnswering.py
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TaskForSQuADQuestionAnswering.py
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import collections
import sys
sys.path.append('../')
from model.BasicBert.BertConfig import BertConfig
from model.DownstreamTasks import BertForQuestionAnswering
from utils.data_helpers import LoadSQuADQuestionAnsweringDataset
from utils.log_helper import logger_init
from transformers import BertTokenizer
from transformers import get_scheduler
import logging
import torch
import os
import time
class ModelConfig:
def __init__(self):
self.project_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
self.dataset_dir = os.path.join(self.project_dir, 'data', 'SQuAD')
self.pretrained_model_dir = os.path.join(self.project_dir, "bert_base_uncased_english")
self.vocab_path = os.path.join(self.pretrained_model_dir, 'vocab.txt')
self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
self.train_file_path = os.path.join(self.dataset_dir, 'train-v1.1.json')
self.test_file_path = os.path.join(self.dataset_dir, 'dev-v1.1.json')
self.model_save_dir = os.path.join(self.project_dir, 'cache')
self.logs_save_dir = os.path.join(self.project_dir, 'logs')
self.model_save_path = os.path.join(self.model_save_dir, 'model.pt')
self.n_best_size = 10 # 对预测出的答案近后处理时,选取的候选答案数量
self.max_answer_len = 30 # 在对候选进行筛选时,对答案最大长度的限制
self.is_sample_shuffle = True # 是否对训练集进行打乱
self.use_torch_multi_head = False # 是否使用PyTorch中的multihead实现
self.batch_size = 12
self.max_sen_len = 384 # 最大句子长度,即 [cls] + question ids + [sep] + context ids + [sep] 的长度
self.max_query_len = 64 # 表示问题的最大长度,超过长度截取
self.learning_rate = 3.5e-5
self.doc_stride = 128 # 滑动窗口一次滑动的长度
self.epochs = 2
self.model_val_per_epoch = 1
logger_init(log_file_name='qa', log_level=logging.DEBUG,
log_dir=self.logs_save_dir)
if not os.path.exists(self.model_save_dir):
os.makedirs(self.model_save_dir)
# 把原始bert中的配置参数也导入进来
bert_config_path = os.path.join(self.pretrained_model_dir, "config.json")
bert_config = BertConfig.from_json_file(bert_config_path)
for key, value in bert_config.__dict__.items():
self.__dict__[key] = value
# 将当前配置打印到日志文件中
logging.info(" ### 将当前配置打印到日志文件中 ")
for key, value in self.__dict__.items():
logging.info(f"### {key} = {value}")
def train(config):
model = BertForQuestionAnswering(config,
config.pretrained_model_dir)
if os.path.exists(config.model_save_path):
loaded_paras = torch.load(config.model_save_path)
model.load_state_dict(loaded_paras)
logging.info("## 成功载入已有模型,进行追加训练......")
model = model.to(config.device)
model.train()
bert_tokenize = BertTokenizer.from_pretrained(config.pretrained_model_dir).tokenize
data_loader = LoadSQuADQuestionAnsweringDataset(vocab_path=config.vocab_path,
tokenizer=bert_tokenize,
batch_size=config.batch_size,
max_sen_len=config.max_sen_len,
max_query_length=config.max_query_len,
max_position_embeddings=config.max_position_embeddings,
pad_index=config.pad_token_id,
is_sample_shuffle=config.is_sample_shuffle,
doc_stride=config.doc_stride)
train_iter, test_iter, val_iter = \
data_loader.load_train_val_test_data(train_file_path=config.train_file_path,
test_file_path=config.test_file_path,
only_test=False)
optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
lr_scheduler = get_scheduler(name='linear',
optimizer=optimizer,
num_warmup_steps=int(len(train_iter) * 0),
num_training_steps=int(config.epochs * len(train_iter)))
max_acc = 0
for epoch in range(config.epochs):
losses = 0
start_time = time.time()
for idx, (batch_input, batch_seg, batch_label, _, _, _, _) in enumerate(train_iter):
batch_input = batch_input.to(config.device) # [src_len, batch_size]
batch_seg = batch_seg.to(config.device)
batch_label = batch_label.to(config.device)
padding_mask = (batch_input == data_loader.PAD_IDX).transpose(0, 1)
loss, start_logits, end_logits = model(input_ids=batch_input,
attention_mask=padding_mask,
token_type_ids=batch_seg,
position_ids=None,
start_positions=batch_label[:, 0],
end_positions=batch_label[:, 1])
optimizer.zero_grad()
loss.backward()
optimizer.step()
lr_scheduler.step()
losses += loss.item()
acc_start = (start_logits.argmax(1) == batch_label[:, 0]).float().mean()
acc_end = (end_logits.argmax(1) == batch_label[:, 1]).float().mean()
acc = (acc_start + acc_end) / 2
if idx % 10 == 0:
logging.info(f"Epoch: {epoch}, Batch[{idx}/{len(train_iter)}], "
f"Train loss :{loss.item():.3f}, Train acc: {acc:.3f}")
if idx % 100 == 0:
y_pred = [start_logits.argmax(1), end_logits.argmax(1)]
y_true = [batch_label[:, 0], batch_label[:, 1]]
show_result(batch_input, data_loader.vocab.itos,
y_pred=y_pred, y_true=y_true)
end_time = time.time()
train_loss = losses / len(train_iter)
logging.info(f"Epoch: {epoch}, Train loss: "
f"{train_loss:.3f}, Epoch time = {(end_time - start_time):.3f}s")
if (epoch + 1) % config.model_val_per_epoch == 0:
acc = evaluate(val_iter, model,
config.device,
data_loader.PAD_IDX,
inference=False)
logging.info(f" ### Accuracy on val: {round(acc, 4)} max :{max_acc}")
if acc > max_acc:
max_acc = acc
torch.save(model.state_dict(), config.model_save_path)
def evaluate(data_iter, model, device, PAD_IDX, inference=False):
model.eval()
with torch.no_grad():
acc_sum, n = 0.0, 0
all_results = collections.defaultdict(list)
for batch_input, batch_seg, batch_label, batch_qid, _, batch_feature_id, _ in data_iter:
batch_input = batch_input.to(device) # [src_len, batch_size]
batch_seg = batch_seg.to(device)
batch_label = batch_label.to(device)
padding_mask = (batch_input == PAD_IDX).transpose(0, 1)
start_logits, end_logits = model(input_ids=batch_input,
attention_mask=padding_mask,
token_type_ids=batch_seg,
position_ids=None)
# 将同一个问题下的所有预测样本的结果保存到一个list中,这里只对batchsize=1时有用
all_results[batch_qid[0]].append([batch_feature_id[0],
start_logits.cpu().numpy().reshape(-1),
end_logits.cpu().numpy().reshape(-1)])
if not inference:
acc_sum_start = (start_logits.argmax(1) == batch_label[:, 0]).float().sum().item()
acc_sum_end = (end_logits.argmax(1) == batch_label[:, 1]).float().sum().item()
acc_sum += (acc_sum_start + acc_sum_end)
n += len(batch_label)
model.train()
if inference:
return all_results
return acc_sum / (2 * n)
def show_result(batch_input, itos, num_show=5, y_pred=None, y_true=None):
"""
本函数的作用是在训练模型的过程中展示相应的结果
:param batch_input:
:param itos:
:param num_show:
:param y_pred:
:param y_true:
:return:
"""
count = 0
batch_input = batch_input.transpose(0, 1) # 转换为[batch_size, seq_len]形状
for i in range(len(batch_input)): # 取一个batch所有的原始文本
if count == num_show:
break
input_tokens = [itos[s] for s in batch_input[i]] # 将question+context 的ids序列转为字符串
start_pos, end_pos = y_pred[0][i], y_pred[1][i]
answer_text = " ".join(input_tokens[start_pos:(end_pos + 1)]).replace(" ##", "")
input_text = " ".join(input_tokens).replace(" ##", "").split('[SEP]')
question_text, context_text = input_text[0], input_text[1]
logging.info(f"### Question: {question_text}")
logging.info(f" ## Predicted answer: {answer_text}")
start_pos, end_pos = y_true[0][i], y_true[1][i]
true_answer_text = " ".join(input_tokens[start_pos:(end_pos + 1)])
true_answer_text = true_answer_text.replace(" ##", "")
logging.info(f" ## True answer: {true_answer_text}")
logging.info(f" ## True answer idx: {start_pos.cpu(), end_pos.cpu()}")
count += 1
def inference(config):
bert_tokenize = BertTokenizer.from_pretrained(config.pretrained_model_dir).tokenize
data_loader = LoadSQuADQuestionAnsweringDataset(vocab_path=config.vocab_path,
tokenizer=bert_tokenize,
batch_size=1, # 只能是1
max_sen_len=config.max_sen_len,
doc_stride=config.doc_stride,
max_query_length=config.max_query_len,
max_answer_length=config.max_answer_len,
max_position_embeddings=config.max_position_embeddings,
pad_index=config.pad_token_id,
n_best_size=config.n_best_size)
test_iter, all_examples = data_loader.load_train_val_test_data(test_file_path=config.test_file_path,
only_test=True)
model = BertForQuestionAnswering(config,
config.pretrained_model_dir)
if os.path.exists(config.model_save_path):
loaded_paras = torch.load(config.model_save_path)
model.load_state_dict(loaded_paras)
logging.info("## 成功载入已有模型,开始进行推理......")
else:
raise ValueError(f"## 模型{config.model_save_path}不存在,请检查路径或者先训练模型......")
model = model.to(config.device)
all_result_logits = evaluate(test_iter, model, config.device,
data_loader.PAD_IDX, inference=True)
data_loader.write_prediction(test_iter, all_examples,
all_result_logits, config.dataset_dir)
if __name__ == '__main__':
model_config = ModelConfig()
train(config=model_config)
inference(model_config)