125 lines
3.7 KiB
Python
125 lines
3.7 KiB
Python
#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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# !/usr/bin/env python
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# -*- coding: UTF-8 -*-
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import sys
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sys.path.append(R'../0_Chinese-Text-Classification-Pytorch/')
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import icraft
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import icraft.xir as ir
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import icraft.xrt as rt
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import icraft.host_backend as hb
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import icraft.buyibackend as bb
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import yaml
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import os
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import numpy as np
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import collections
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import torch
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import torch.nn as nn
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import pickle as pkl
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import numpy as np
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from importlib import import_module
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def new_forward(self, out):
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# out = self.embedding(x[0])
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out = out.unsqueeze(1)
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out = torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)
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out = self.dropout(out)
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out = self.fc(out)
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return out
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# 参数设置
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TRACE_PATH = R'../2_compile/fmodel/TextCNN_traced.pt'
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key = {
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0: 'finance',
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1: 'realty',
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2: 'stocks',
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3: 'education',
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4: 'science',
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5: 'society',
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6: 'politics',
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7: 'sports',
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8: 'game',
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9: 'entertainment'
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}
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class Predict:
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def __init__(self, model_name='TextCNN', dataset='../0_Chinese-Text-Classification-Pytorch/THUCNews/', embedding='embedding_SougouNews.npz', use_word=False):
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if use_word:
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self.tokenizer = lambda x: x.split(' ') # 以空格隔开，word-level
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else:
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self.tokenizer = lambda x: [y for y in x] # char-level
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self.x = import_module('models.' + model_name)
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self.config = self.x.Config(dataset, embedding)
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self.vocab = pkl.load(open(self.config.vocab_path, 'rb'))
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self.pad_size = self.config.pad_size
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self.model = self.x.Model(self.config).to('cpu')
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self.model.load_state_dict(torch.load(self.config.save_path, map_location='cpu'))
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def build_predict_text(self, texts):
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words_lines = []
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seq_lens = []
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for text in texts:
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words_line = []
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token = self.tokenizer(text)
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seq_len = len(token)
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if self.pad_size:
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if len(token) < self.pad_size:
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token.extend(['<PAD>'] * (self.pad_size - len(token)))
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else:
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token = token[:self.pad_size]
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seq_len = self.pad_size
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# word to id
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for word in token:
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words_line.append(self.vocab.get(word, self.vocab.get('<UNK>')))
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words_lines.append(words_line)
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seq_lens.append(seq_len)
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return torch.LongTensor(words_lines), torch.LongTensor(seq_lens)
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def predict(self, query):
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query = [query]
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# 返回预测的索引
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data = self.build_predict_text(query)
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with torch.no_grad():
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# 替换 model 的 forward 方法
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self.model.forward = new_forward.__get__(self.model)
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self.model.eval()
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embedding_out = self.model.embedding(data[0])
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outputs = self.model(embedding_out)
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print(outputs)
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# 保存trace模型
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trace_model = torch.jit.trace(self.model, embedding_out)
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torch.jit.save(trace_model,TRACE_PATH)
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print('TorchScript export success, saved in %s' % TRACE_PATH)
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num = torch.argmax(outputs)
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return key[int(num)]
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if __name__ == "__main__":
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pred = Predict('TextCNN')
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# 预测一条
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# query = "学费太贵怎么办？"
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# query = "金融怎么样"
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# query = "今天股票涨了吗？"
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query = "昨天游戏通关了吗？"
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# query = "明天打球去啊"
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print(pred.predict(query))
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# # 预测一个列表
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# querys = ["学费太贵怎么办？", "金融怎么样"]
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# print(pred.predict_list(querys)) |