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TaskForMultipleChoice.py
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import sys
sys.path.append('../')
from model import BertConfig
from model import BertForMultipleChoice
from utils import LoadMultipleChoiceDataset
from utils import logger_init
from transformers import BertTokenizer
import logging
import torch
import os
import time
import numpy as np
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', 'MultipleChoice')
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.csv')
self.val_file_path = os.path.join(self.dataset_dir, 'val.csv')
self.test_file_path = os.path.join(self.dataset_dir, 'test.csv')
self.model_save_dir = os.path.join(self.project_dir, 'cache')
self.logs_save_dir = os.path.join(self.project_dir, 'logs')
self.is_sample_shuffle = True
self.batch_size = 16
self.max_sen_len = None
self.num_labels = 4 # num_choice
self.learning_rate = 2e-5
self.epochs = 10
self.model_val_per_epoch = 2
logger_init(log_file_name='choice', log_level=logging.INFO,
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 = BertForMultipleChoice(config,
config.pretrained_model_dir)
model_save_path = os.path.join(config.model_save_dir, 'model.pt')
if os.path.exists(model_save_path):
loaded_paras = torch.load(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)
optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
model.train()
bert_tokenize = BertTokenizer.from_pretrained(model_config.pretrained_model_dir).tokenize
data_loader = LoadMultipleChoiceDataset(
vocab_path=config.vocab_path,
tokenizer=bert_tokenize,
batch_size=config.batch_size,
max_sen_len=config.max_sen_len,
max_position_embeddings=config.max_position_embeddings,
pad_index=config.pad_token_id,
is_sample_shuffle=config.is_sample_shuffle,
num_choice=config.num_labels)
train_iter, test_iter, val_iter = \
data_loader.load_train_val_test_data(config.train_file_path,
config.val_file_path,
config.test_file_path)
max_acc = 0
for epoch in range(config.epochs):
losses = 0
start_time = time.time()
for idx, (qa, seg, mask, label) in enumerate(train_iter):
qa = qa.to(config.device) # [src_len, batch_size]
label = label.to(config.device)
seg = seg.to(config.device)
mask = mask.to(config.device)
loss, logits = model(input_ids=qa,
attention_mask=mask,
token_type_ids=seg,
position_ids=None,
labels=label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses += loss.item()
acc = (logits.argmax(1) == label).float().mean()
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 = logits.argmax(1).cpu()
show_result(qa, y_pred, data_loader.vocab.itos, num_show=1)
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, inference=False)
logging.info(f"Accuracy on val {acc:.3f}")
if acc > max_acc:
max_acc = acc
torch.save(model.state_dict(), model_save_path)
def inference(config):
model = BertForMultipleChoice(config,
config.pretrained_model_dir)
model_save_path = os.path.join(config.model_save_dir, 'model.pt')
if os.path.exists(model_save_path):
loaded_paras = torch.load(model_save_path)
model.load_state_dict(loaded_paras)
logging.info("## 成功载入已有模型，进行预测......")
model = model.to(config.device)
data_loader = LoadMultipleChoiceDataset(vocab_path=config.vocab_path,
tokenizer=BertTokenizer.from_pretrained(
config.pretrained_model_dir).tokenize,
batch_size=config.batch_size,
max_sen_len=config.max_sen_len,
max_position_embeddings=config.max_position_embeddings,
pad_index=config.pad_token_id,
is_sample_shuffle=config.is_sample_shuffle)
test_iter = data_loader.load_train_val_test_data(test_file_path=config.test_file_path,
only_test=True)
y_pred = evaluate(test_iter, model, config.device, inference=True)
logging.info(f"预测标签为：{y_pred.tolist()}")
def evaluate(data_iter, model, device, inference=False):
model.eval()
with torch.no_grad():
acc_sum, n = 0.0, 0
y_pred = []
for qa, seg, mask, y in data_iter:
qa, seg, y, mask = qa.to(device), seg.to(device), y.to(device), mask.to(device)
logits = model(qa, attention_mask=mask, token_type_ids=seg)
y_pred.append(logits.argmax(1).cpu().numpy())
if not inference:
acc_sum += (logits.argmax(1) == y).float().sum().item()
n += len(y)
model.train()
if inference:
return np.hstack(y_pred)
return acc_sum / n, np.hstack(y_pred)
def show_result(qas, y_pred, itos=None, num_show=5):
count = 0
num_samples, num_choice, seq_len = qas.size()
qas = qas.reshape(-1)
strs = np.array([itos[t] for t in qas]).reshape(-1, seq_len)
for i in range(num_samples): # 遍历每个样本
s_idx = i * num_choice
e_idx = s_idx + num_choice
sample = strs[s_idx:e_idx]
if count == num_show:
return
count += 1
for j, item in enumerate(sample): # 每个样本的四个答案
q, a, _ = " ".join(item[1:]).replace(" .", ".").replace(" ##", "").split('[SEP]')
if y_pred[i] == j:
a += " ## True"
else:
a += " ## False"
logging.info(f"[{num_show}/{count}] ### {q + a}")
logging.info("\n")
if __name__ == '__main__':
model_config = ModelConfig()
train(model_config)
inference(model_config)