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multi_main.py
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multi_main.py
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# coding=utf-8
import random
import numpy as np
import time
from tqdm import tqdm
import os
import torch
import torch.nn as nn
from pytorch_pretrained_bert.tokenization import BertTokenizer
from pytorch_pretrained_bert.modeling import BertConfig, WEIGHTS_NAME, CONFIG_NAME
from pytorch_pretrained_bert.optimization import BertAdam
from Utils.utils import get_device
from Utils.MultiSentences_utils import load_data
from Utils.train_evalute import train, evaluate
def main(config, model_times, myProcessor):
if not os.path.exists(config.output_dir + model_times):
os.makedirs(config.output_dir + model_times)
if not os.path.exists(config.cache_dir + model_times):
os.makedirs(config.cache_dir + model_times)
# Bert 模型输出文件
output_model_file = os.path.join(
config.output_dir, model_times, WEIGHTS_NAME)
output_config_file = os.path.join(
config.output_dir, model_times, CONFIG_NAME)
# 设备准备
gpu_ids = [int(device_id) for device_id in config.gpu_ids.split()]
device, n_gpu = get_device(gpu_ids[0])
if n_gpu > 1:
n_gpu = len(gpu_ids)
config.train_batch_size = config.train_batch_size // config.gradient_accumulation_steps
# 设定随机种子
random.seed(config.seed)
np.random.seed(config.seed)
torch.manual_seed(config.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(config.seed)
# 数据准备
processor = myProcessor() # 整个文件的代码只需要改此处即可
tokenizer = BertTokenizer.from_pretrained(
config.bert_vocab_file, do_lower_case=config.do_lower_case) # 分词器选择
label_list = processor.get_labels()
num_labels = len(label_list)
# Train and dev
if config.do_train:
train_dataloader, train_examples_len = load_data(
config.data_dir, tokenizer, processor, config.max_seq_length, config.train_batch_size, "train", config.max_sentence_num)
dev_dataloader, _ = load_data(
config.data_dir, tokenizer, processor, config.max_seq_length, config.dev_batch_size, "dev", config.max_sentence_num)
num_train_optimization_steps = int(
train_examples_len / config.train_batch_size / config.gradient_accumulation_steps) * config.num_train_epochs
# 模型准备
print("model name is {}".format(config.model_name))
if config.model_name == "BertHAN":
from BertHAN.BertHAN import BertHAN
model = BertHAN.from_pretrained(
config.bert_model_dir, cache_dir=config.cache_dir, num_labels=num_labels)
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model, device_ids=gpu_ids)
""" 优化器准备 """
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.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': 0.01},
{'params': [p for n, p in param_optimizer if any(
nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = BertAdam(optimizer_grouped_parameters,
lr=config.learning_rate,
warmup=config.warmup_proportion,
t_total=num_train_optimization_steps)
""" 损失函数准备 """
criterion = nn.CrossEntropyLoss()
criterion = criterion.to(device)
train(config.num_train_epochs, n_gpu, model, train_dataloader, dev_dataloader, optimizer,
criterion, config.gradient_accumulation_steps, device, label_list, output_model_file, output_config_file, config.log_dir, config.print_step, config.early_stop)
""" Test """
# test 数据
test_dataloader, _ = load_data(
config.data_dir, tokenizer, processor, config.max_seq_length, config.test_batch_size, "test", config.max_sentence_num)
# 加载模型
bert_config = BertConfig(output_config_file)
if config.model_name == "BertHAN":
from BertHAN.BertHAN import BertHAN
model = BertHAN(bert_config, num_labels=num_labels)
model.load_state_dict(torch.load(output_model_file))
model.to(device)
# 损失函数准备
criterion = nn.CrossEntropyLoss()
criterion = criterion.to(device)
# test the model
test_loss, test_acc, test_report, test_auc = evaluate(
model, test_dataloader, criterion, device, label_list)
print("-------------- Test -------------")
print(
f'\t Loss: {test_loss: .3f} | Acc: {test_acc*100: .3f} % | AUC:{test_auc}')
for label in label_list:
print('\t {}: Precision: {} | recall: {} | f1 score: {}'.format(
label, test_report[label]['precision'], test_report[label]['recall'], test_report[label]['f1-score']))
print_list = ['macro avg', 'weighted avg']
for label in print_list:
print('\t {}: Precision: {} | recall: {} | f1 score: {}'.format(
label, test_report[label]['precision'], test_report[label]['recall'], test_report[label]['f1-score']))