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predict.py
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import pickle
import os
import torch
from net import deal_eval, dataset
from pytorch_pretrained_bert import BertTokenizer, BertModel
device = 'cuda:0'
dataset.device = device
num_words = 10000
max_len = 400
# 读取实体词典，用于推断，根据"实体-id"检索
with open('./data_deal/%d/alias_data.pkl' % num_words, 'rb') as f:
alias_data = pickle.load(f)
# 读取测试集预处理
with open('./data_deal/%d/test_data.pkl' % num_words, 'rb') as f:
develop_data = pickle.load(f)
file_name = {
10000: {
'bert': {
'lstm_2_768_3_len_400_lf_2_l_2': [
18, # 0.743
],
'lstm_3_768_3_len_400_lf_2_l_2': [
13, # 0.742
],
'lstm_3_1024_3_len_400_lf_2_l_2': [
14, # 0.744
],
'lstm_4_768_3_len_400_lf_2_l_2': [
12, # 0.743
],
'lstm_4_1024_3_len_400_lf_2_l_2': [
13, # 0.743
],
'lstm_2_768_2_len_400_lf_2_l_2': [
17, # 0.744
],
'lstm_2_1024_2_len_400_lf_2_l_2': [
12, # 0.742
],
'lstm_3_768_2_len_400_lf_2_l_2': [
20, # 0.741
],
'lstm_3_1024_2_len_400_lf_2_l_2': [
22, # 0.741
],
'lstm_4_768_2_len_400_lf_2_l_2': [
13, # 0.742
],
'lstm_4_1024_2_len_400_lf_2_l_2': [
15, # 0.742
],
},
'wwm': {
'lstm_2_768_3_len_400_lf_2_l_2': [
17, # 0.744
],
'lstm_2_1024_3_len_400_lf_2_l_2': [
9, # 0.745
],
'lstm_3_768_3_len_400_lf_2_l_2': [
18, # 0.744
],
'lstm_3_1024_3_len_400_lf_2_l_2': [
14, # 0.742
],
'lstm_4_768_3_len_400_lf_2_l_2': [
13, # 0.743
],
'lstm_4_1024_3_len_400_lf_2_l_2': [
14, # 0.743
],
'lstm_2_768_2_len_400_lf_2_l_2': [
20, # 0.744
],
'lstm_2_1024_2_len_400_lf_2_l_2': [
17, # 0.743
],
'lstm_3_768_2_len_400_lf_2_l_2': [
21, # 0.745
],
'lstm_3_1024_2_len_400_lf_2_l_2': [
18, # 0.743
],
'lstm_4_768_2_len_400_lf_2_l_2': [
19, # 0.742
],
'lstm_4_1024_2_len_400_lf_2_l_2': [
21, # 0.743
],
},
'ernie': {
'lstm_3_768_3_len_400_lf_2_l_2': [
24, # 0.743
],
'lstm_4_768_3_len_400_lf_2_l_2': [
9, # 0.739
],
'lstm_4_768_2_len_400_lf_2_l_2': [
19, # 0.742
],
'lstm_3_768_2_len_400_lf_2_l_2': [
7, # 0.740
],
'lstm_3_1024_2_len_400_lf_2_l_2': [
# 11, # 0.742 xin x
12, # 0.742 jiu
],
'lstm_4_1024_2_len_400_lf_2_l_2': [
21, # 0.742 xin x
],
},
}
}
for num_words, value1 in file_name.items():
for embedding_name, value2 in value1.items():
bert_path = './data/pretrain/' + embedding_name + '/'
dataset.tokenizer = BertTokenizer.from_pretrained(bert_path + 'vocab.txt')
dataset.BERT = BertModel.from_pretrained(bert_path).to(device)
dataset.BERT.eval()
dataset.max_len = max_len
for model_name, model_idxs in value2.items():
if not os.path.exists('./results_test/%d/%s/%s/' % (num_words, embedding_name, model_name)):
os.mkdir('./results_test/%d/%s/%s/' % (num_words, embedding_name, model_name))
for model_idx in model_idxs:
model = torch.load('./results/%d/%s/%s/%03d.pth' % (
num_words, embedding_name, model_name, model_idx), map_location=device)
model.device = device
model.to(device)
model.eval()
entity_list_all = []
for idx, data in enumerate(develop_data):
model.zero_grad()
text_seq = deal_eval([data])
text_seq = text_seq.to(device)
text = data['text']
with torch.no_grad():
entity_predict = model(text_seq,
text,
alias_data)
entity_list_all.append(entity_predict)
with open('./results_test/%d/%s/%s/test_%03d.pkl' % (
num_words, embedding_name, model_name, model_idx),
'wb') as f:
pickle.dump(entity_list_all, f)