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train.py
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train.py
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# -*- encoding: utf-8 -*-
import random
import time
from typing import List, Union, Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from loguru import logger
from sklearn import metrics
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
from transformers import BertConfig, BertModel, BertTokenizer
# 基本参数
EPOCHS = 1
BATCH_SIZE = 128
LR = 5e-5
MAXLEN = 32
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 模型位置
SAVE_PATH = 'saved_model/pytorch_model.bin'
BERT = 'pretrained_model/chinese_bert_pytorch'
model_path = BERT
# 数据位置
CLASS_PATH = 'datasets/class.txt'
TARGET_NAMES = [x.strip() for x in open(CLASS_PATH).readlines()]
NUMBER_CLASS = len(TARGET_NAMES)
TRAIN_PATH = 'datasets/train.txt'
DEV_PATH = 'datasets/dev.txt'
TEST_PATH = 'datasets/test.txt'
def load_data(path):
"""加载数据"""
data = []
with open(path, 'r', encoding='utf8') as f:
for line in tqdm(f):
line = line.strip()
text, label = line.split('\t')
data.append((text, label))
return data
class TextDataset(Dataset):
"""自定义数据集"""
def __init__(self, data, tokenizer):
self.data = data
self.tokenizer = tokenizer
def __len__(self):
return len(self.data)
def text_2_id(self, text):
return self.tokenizer(text, max_length=MAXLEN, truncation=True,
padding='max_length', return_tensors='pt')
def __getitem__(self, index):
return self.text_2_id(self.data[index][0]), int(self.data[index][1])
class TextClassifyModel(nn.Module):
"""文本分类模型"""
def __init__(self, pretrained_model: str):
super(TextClassifyModel, self).__init__()
config = BertConfig.from_pretrained(pretrained_model)
self.bert = BertModel.from_pretrained(pretrained_model)
self.fc = nn.Linear(config.hidden_size, NUMBER_CLASS)
def forward(self, input_ids, attention_mask, token_type_ids):
out = self.bert(input_ids, attention_mask, token_type_ids)
pooler = out.pooler_output # [batch, 768]
out = self.fc(pooler)
return out
def compute_loss(pred, label):
"""自定义损失函数"""
return F.cross_entropy(pred, label)
def eval(model, dataloader, test_flag=False) -> Union[float, Tuple]:
"""评估函数"""
model.eval()
preds = torch.tensor([], dtype=int, device=DEVICE)
labels = np.array([], dtype=int)
with torch.no_grad():
for text, label in dataloader:
input_ids = text.get('input_ids').squeeze(1).to(DEVICE)
attention_mask = text.get('attention_mask').squeeze(1).to(DEVICE)
token_type_ids = text.get('token_type_ids').squeeze(1).to(DEVICE)
out = model(input_ids, attention_mask, token_type_ids)
pred = torch.max(out, 1)[1]
preds = torch.cat((preds, pred), dim=-1)
labels = np.append(labels, label)
preds = preds.cpu().numpy()
acc = metrics.accuracy_score(labels, preds) # 避免频繁的cpu与gpu的数据交换
if not test_flag:
return acc
report = metrics.classification_report(labels, preds, target_names=TARGET_NAMES, digits=4)
confusion = metrics.confusion_matrix(labels, preds)
return acc, report, confusion
def train(model, train_dl, dev_dl, optimizer) -> None:
"""训练函数"""
model.train()
global best
early_stop_batch = 0
for batch_idx, (text, label) in enumerate(tqdm(train_dl), start=1):
# [batch, 1, seq_len] -> [batch, seq_len]
input_ids = text.get('input_ids').squeeze(1).to(DEVICE)
attention_mask = text.get('attention_mask').squeeze(1).to(DEVICE)
token_type_ids = text.get('token_type_ids').squeeze(1).to(DEVICE)
label = label.to(DEVICE)
# 训练
out = model(input_ids, attention_mask, token_type_ids) # [batch, num_class]
loss = compute_loss(out, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 评估
if batch_idx % 100 == 0:
logger.info(f'loss: {loss.item():.4f}')
_, indices = torch.max(out, dim=1)
dev_acc = eval(model, dev_dl)
model.train()
if best < dev_acc:
best = dev_acc
early_stop_batch = 0
torch.save(model.state_dict(), SAVE_PATH)
logger.info(f"higher dev acc: {best:.4f} in batch: {batch_idx}, save model")
continue
early_stop_batch += 1
if early_stop_batch == 30:
logger.info(f"dev acc doesn't improve for {early_stop_batch} batch, early stop!")
return
if __name__ == '__main__':
tokenizer = BertTokenizer.from_pretrained(model_path)
# load data
train_data = load_data(TRAIN_PATH)
dev_data = load_data(DEV_PATH)
test_data = load_data(TEST_PATH)
train_dataloader = DataLoader(TextDataset(train_data, tokenizer), batch_size=BATCH_SIZE)
dev_dataloader = DataLoader(TextDataset(dev_data, tokenizer), batch_size=BATCH_SIZE)
test_dataloader = DataLoader(TextDataset(test_data, tokenizer), batch_size=BATCH_SIZE)
# load model
model = TextClassifyModel(pretrained_model=model_path)
model.to(DEVICE)
optimizer = torch.optim.AdamW(model.parameters(), lr=LR)
# train
best = 0
for epoch in range(EPOCHS):
logger.info(f'epoch: {epoch}')
train(model, train_dataloader, dev_dataloader, optimizer)
logger.info(f'train is finished, best model is saved at {SAVE_PATH}')
# eval
model.load_state_dict(torch.load(SAVE_PATH))
test_acc, test_report, test_confusion = eval(model, test_dataloader, test_flag=True)
print(f'acc: {test_acc:.4f}')
print(f'classification report:')
print(f'{test_report}')
print(f'confusion matrix:')
print(f'{test_confusion}')