CMeKG_tools/train_cws.py at main · king-yyf/CMeKG_tools · GitHub

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# coding: utf-8

from tqdm import tqdm
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
from torch.utils.data import TensorDataset
from torch.utils.data import DataLoader

from utils import load_vocab, load_data, recover_label, get_f1, save_model, load_model
from cws_constant import *
from model_cws import BERT_LSTM_CRF
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"

if torch.cuda.is_available():
    device = torch.device("cuda", 0)
    use_cuda = True
else:
    device = torch.device("cpu")
    use_cuda = False

vocab = load_vocab(vocab_file)
####读取训练集
print('max_length',max_length)

train_data = load_data(train_file, max_length=max_length, label_dic=l2i_dic, vocab=vocab)


train_ids = torch.LongTensor([temp.input_id for temp in train_data])
train_masks = torch.LongTensor([temp.input_mask for temp in train_data])
train_tags = torch.LongTensor([temp.label_id for temp in train_data])
train_lenghts = torch.LongTensor([temp.lenght for temp in train_data])


train_dataset = TensorDataset(train_ids, train_masks, train_tags,train_lenghts)
train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)


#######读取测试集
dev_data = load_data(dev_file, max_length=max_length, label_dic=l2i_dic, vocab=vocab)

dev_ids = torch.LongTensor([temp.input_id for temp in dev_data])
dev_masks = torch.LongTensor([temp.input_mask for temp in dev_data])
dev_tags = torch.LongTensor([temp.label_id for temp in dev_data])
dev_lenghts = torch.LongTensor([temp.lenght for temp in dev_data])


dev_dataset = TensorDataset(dev_ids, dev_masks, dev_tags,dev_lenghts)
dev_loader = DataLoader(dev_dataset, shuffle=True, batch_size=batch_size)


# 读取test数据集

test_data = load_data(test_file, max_length=max_length, label_dic=l2i_dic, vocab=vocab)

test_ids = torch.LongTensor([temp.input_id for temp in test_data])
test_masks = torch.LongTensor([temp.input_mask for temp in test_data])
test_tags = torch.LongTensor([temp.label_id for temp in test_data])
test_lenghts = torch.LongTensor([temp.lenght for temp in test_data])


test_dataset = TensorDataset(test_ids, test_masks, test_tags, test_lenghts)
test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)


######测试函数
def evaluate(medel, dev_loader):
    medel.eval()
    pred = []
    gold = []
    pred_test = []

    print('evaluate: ')
    with torch.no_grad():
        for i, dev_batch in enumerate(dev_loader):
            sentence, masks, tags , lengths = dev_batch
            sentence, masks, tags, lengths = Variable(sentence), Variable(masks), Variable(tags), Variable(lengths)
            if use_cuda:
                sentence = sentence.cuda()
                masks = masks.cuda()
                tags = tags.cuda()

            predict_tags = medel(sentence, masks)
            loss = model.neg_log_likelihood_loss(sentence, masks, tags)

            pred.extend([t for t in predict_tags.tolist()])
            gold.extend([t for t in tags.tolist()])

        pred_label, gold_label = recover_label(pred, gold, l2i_dic, i2l_dic)

        print('dev loss {}'.format(loss.item()))
        pred_label_1 = [t[1:] for t in pred_label]
        gold_label_1 = [t[1:] for t in gold_label]
        p, r, f = get_f1(gold_label_1, pred_label_1)
        print('p: {}，r: {}, f: {}'.format(p, r, f))
        # model.train()
        return p, r, f


# test 函数
def evaluate_test(medel, test_loader):
    medel.eval()
    pred = []
    gold = []
    print('test: ')
    with torch.no_grad():
        for i, dev_batch in enumerate(test_loader):
            sentence, masks, tags, lengths = dev_batch
            sentence, masks, tags , lengths = Variable(sentence), Variable(masks), Variable(tags),Variable(lengths)
            if use_cuda:
                sentence = sentence.cuda()
                masks = masks.cuda()
                tags = tags.cuda()
            predict_tags = medel(sentence, masks)

            pred.extend([t for t in predict_tags.tolist()])
            gold.extend([t for t in tags.tolist()])

        pred_label, gold_label = recover_label(pred, gold, l2i_dic, i2l_dic)
        pred_label_2 = [t[1:] for t in pred_label]
        gold_label_2 = [t[1:] for t in gold_label]
        p, r, f = get_f1(gold_label_2,pred_label_2)
        print('p: {}，r: {}, f: {}'.format(p, r, f))
    return p, r, f


########加载模型


model = BERT_LSTM_CRF(medical_bert, tagset_size, 768, 200, 2,
                      dropout_ratio=0.5, dropout1=0.5, use_cuda = use_cuda)

if use_cuda:
    model.cuda()

optimizer = getattr(optim, 'Adam')
optimizer = optimizer(model.parameters(), lr=0.00001, weight_decay=0.00005)

best_f = -100
# pre-training test
p_b, r_b, f_b = evaluate(model, dev_loader)
for epoch in range(epochs):
    print('-----------------------------------------------')
    print('epoch: {}，train'.format(epoch))

    for i, train_batch in enumerate(tqdm(train_loader, ncols=60)):
        sentence, masks, tags, lengths = train_batch

        sentence, masks, tags, lengths = Variable(sentence), Variable(masks), Variable(tags), Variable(lengths)

        if use_cuda:
            sentence = sentence.cuda()
            masks = masks.cuda()
            tags = tags.cuda()
        model.train()
        optimizer.zero_grad()
        loss = model.neg_log_likelihood_loss(sentence, masks, tags)
        loss.backward()
        optimizer.step()

    print('epoch: {}，train loss: {}'.format(epoch, loss.item()))
    p, r, f = evaluate(model, dev_loader)


    if f > best_f:
        print('epoch'+str(epoch)+'-model saving!!!!!!')
        best_f = f
        _, _, test_f1 = evaluate_test(model, test_loader)
        print("testset's f1: ", test_f1)
        model_name = save_model_dir + 'devf1-' + str(float('%.3f' % best_f)) + "-testf1-"+str(float('%.3f' %test_f1)) +".pkl"
        torch.save(model.state_dict(), model_name)