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medical_cws.py
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medical_cws.py
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# coding:utf-8
import codecs
import torch
from torch.autograd import Variable
from torch.utils.data import TensorDataset
from torch.utils.data import DataLoader
from utils import load_vocab
from cws_constant import *
from model_cws import BERT_LSTM_CRF
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
class medical_seg(object):
def __init__(self):
self.NEWPATH = '/Users/yangyf/workplace/model/medical_cws/pytorch_model.pkl'
if torch.cuda.is_available():
self.device = torch.device("cuda", 0)
self.use_cuda = True
else:
self.device = torch.device("cpu")
self.use_cuda = False
self.vocab = load_vocab('/Users/yangyf/workplace/model/medical_cws/vocab.txt')
self.vocab_reverse = {v: k for k, v in self.vocab.items()}
self.model = BERT_LSTM_CRF('/Users/yangyf/workplace/model/medical_cws', tagset_size, 768, 200, 2,
dropout_ratio=0.5, dropout1=0.5, use_cuda=use_cuda)
if use_cuda:
self.model.cuda()
def from_input(self, input_str):
# 单行的输入
raw_text = []
textid = []
textmask = []
textlength = []
text = ['[CLS]'] + [x for x in input_str] + ['[SEP]']
raw_text.append(text)
cur_len = len(text)
# raw_textid = [self.vocab[x] for x in text] + [0] * (max_length - cur_len)
raw_textid = [self.vocab[x] for x in text if self.vocab.__contains__(x)] + [0] * (max_length - cur_len)
textid.append(raw_textid)
raw_textmask = [1] * cur_len + [0] * (max_length - cur_len)
textmask.append(raw_textmask)
textlength.append([cur_len])
textid = torch.LongTensor(textid)
textmask = torch.LongTensor(textmask)
textlength = torch.LongTensor(textlength)
return raw_text, textid, textmask, textlength
def from_txt(self, input_path):
# 多行输入
raw_text = []
textid = []
textmask = []
textlength = []
with open(input_path, 'r', encoding='utf-8') as f:
for line in f.readlines():
if len(line) > 148:
line = line[:148]
temptext = ['[CLS]'] + [x for x in line[:-1]] + ['[SEP]']
cur_len = len(temptext)
raw_text.append(temptext)
tempid = [self.vocab[x] for x in temptext[:cur_len]] + [0] * (max_length - cur_len)
textid.append(tempid)
textmask.append([1] * cur_len + [0] * (max_length - cur_len))
textlength.append([cur_len])
textid = torch.LongTensor(textid)
textmask = torch.LongTensor(textmask)
textlength = torch.LongTensor(textlength)
return raw_text, textid, textmask, textlength
def recover_to_text(self, pred, raw_text):
# 输入[标签list]和[原文list],batch为1
pred = [i2l_dic[t.item()] for t in pred[0]]
pred = pred[:len(raw_text)]
pred = pred[1:-1]
raw_text = raw_text[1:-1]
raw = ""
res = ""
for tag, char in zip(pred, raw_text):
res += char
if tag in ["S", 'E']:
res += ' '
raw += char
return raw, res
def predict_sentence(self, sentence):
if sentence == '':
print("输入为空！请重新输入")
return
if len(sentence) > 148:
print("输入句子过长，请输入小于148的长度字符！")
sentence = sentence[:148]
raw_text, test_ids, test_masks, test_lengths = self.from_input(sentence)
test_dataset = TensorDataset(test_ids, test_masks, test_lengths)
test_loader = DataLoader(test_dataset, shuffle=False, batch_size=1)
# self.model.load_state_dict(torch.load(self.NEWPATH, map_location={'cuda:0': str(self.device)}))
self.model.load_state_dict(torch.load(self.NEWPATH,map_location=self.device))
self.model.eval()
for i, dev_batch in enumerate(test_loader):
sentence, masks, lengths = dev_batch
batch_raw_text = raw_text[i]
sentence, masks, lengths = Variable(sentence), Variable(masks), Variable(lengths)
if use_cuda:
sentence = sentence.cuda()
masks = masks.cuda()
predict_tags = self.model(sentence, masks)
predict_tags.tolist()
raw, res = self.recover_to_text(predict_tags, batch_raw_text)
#print("输入：", raw)
#print("结果：", res)
return res
def predict_file(self, input_file, output_file):
# raw_text, test_ids, test_masks, test_lengths = self.from_txt("./data/raw_text.txt")
raw_text, test_ids, test_masks, test_lengths = self.from_txt(input_file)
test_dataset = TensorDataset(test_ids, test_masks, test_lengths)
test_loader = DataLoader(test_dataset, shuffle=False, batch_size=1)
self.model.load_state_dict(torch.load(self.NEWPATH, map_location={'cuda:0': str(self.device)}))
self.model.eval()
op_file = codecs.open(output_file, 'w', 'utf-8')
for i, dev_batch in enumerate(test_loader):
sentence, masks, lengths = dev_batch
batch_raw_text = raw_text[i]
sentence, masks, lengths = Variable(sentence), Variable(masks), Variable(lengths)
if use_cuda:
sentence = sentence.cuda()
masks = masks.cuda()
predict_tags = self.model(sentence, masks)
predict_tags.tolist()
raw, res = self.recover_to_text(predict_tags, batch_raw_text)
op_file.write(res + '\n')
op_file.close()
print('处理完成！')
print("results have been stored in {}".format(output_file))
if __name__ == "__main__":
meg = medical_seg()
# meg.predict_file('./data/raw_text.txt', './data/out_raw.txt')
res = meg.predict_sentence("肾上腺由皮质和髓质两个功能不同的内分泌器官组成，皮质分泌肾上腺皮质激素，髓质分泌儿茶酚胺激素。")
print(res)