test.py

import numpy as np
import torch
from utils import generate_data, check_data, word_ids_to_sentence
import torchtext
from torchtext import data
import torch.nn as nn
import torch.nn.functional as F
from train_eval import train, test

from torch.autograd import Variable as V
from train_eval import test_sentence,load_model

from model import RNNModel
from transformer import TransformerModel

class Config(object):
    def __init__(self):
        self.model_name="lm_model"
        self.data_ori="/mnt/data3/wuchunsheng/data_all/data_mine/lm_data/"
        #self.data_ori="E:/data/word_nlp/cnews_data/"
        self.train_path="train_0.csv"
        self.valid_path="train_0.csv"
        self.test_path="test_100.csv"
        self.sen_max_length=150
        #self.embedding_path = "need_bertembedding"
        self.embedding_path = "bert_embedding"
        self.embedding_dim=768
        self.vocab_maxsize=4000
        self.vocab_minfreq=10
        self.save_path="lm_ckpt"

        self.batch_size = 64
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

        self.hidden_size=200
        self.nlayers=1
        self.dropout=0.5
        self.epoch=20

        self.train_len=0
        self.test_len = 0
        self.valid_len = 0
        self.mode="test"

        ## transformer的参数
        self.dropout=0.5
        self.max_len=5000
        self.nhead=2


# data_path="E:/study_series/2020_3/re_write_classify/data/"
# data_path="/mnt/data3/wuchunsheng/code/nlper/NLP_task/text_classification/my_classification_cnews/2020_3_30/text_classify/data/"

config = Config()
train_iter, valid_iter, test_iter, TEXT = generate_data(config)

#model = RNNModel(config, TEXT).to(config.device)
model=TransformerModel(config, TEXT).to(config.device)

model =load_model(config, model)

#sen="目"*50
sen="体育快讯"
#sen="".join(['c', 'o', 'n', 't', 'e', 'x', 't', ',', 'l', 'a', 'b', 'e', 'l'])
#res=test_sentence(config, model ,TEXT, sen)
#print(sen)
#print(res)
#res=test(config,model,TEXT,  test_iter)
#print(res)
print("=========================")
sen="篮球"
#sen="体育"
sen_ori=sen
while(len(sen)<20):
    print("输入文本: ",sen)
    sen_pred=" ".join(test_sentence(config,model, TEXT,sen))
    sen+=sen_pred[1:]
    sen=sen.replace(" ","")
    print("文本生成: ", sen)
print("*"*20)
print("输入: ", sen_ori)
print("生成: ", sen)