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infer_example.py
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infer_example.py
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# -*- coding: utf-8 -*-
# file: infer_example.py
# author: songyouwei <[email protected]>
# fixed: yangheng <[email protected]>
# Copyright (C) 2019. All Rights Reserved.
import torch
import torch.nn.functional as F
import argparse
import numpy as np
from data_utils import build_tokenizer, build_embedding_matrix, Tokenizer4Bert, pad_and_truncate
from models import LSTM, IAN, MemNet, RAM, TD_LSTM, TC_LSTM, Cabasc, ATAE_LSTM, TNet_LF, AOA, MGAN, ASGCN, LCF_BERT
from models.aen import CrossEntropyLoss_LSR, AEN_BERT
from models.bert_spc import BERT_SPC
from dependency_graph import dependency_adj_matrix
from transformers import BertModel
class Inferer:
"""A simple inference example"""
def __init__(self, opt):
self.opt = opt
if 'bert' in opt.model_name:
self.tokenizer = Tokenizer4Bert(opt.max_seq_len, opt.pretrained_bert_name)
bert = BertModel.from_pretrained(opt.pretrained_bert_name)
self.model = opt.model_class(bert, opt).to(opt.device)
else:
self.tokenizer = build_tokenizer(
fnames=[opt.dataset_file['train'], opt.dataset_file['test']],
max_seq_len=opt.max_seq_len,
dat_fname='{0}_tokenizer.dat'.format(opt.dataset))
embedding_matrix = build_embedding_matrix(
word2idx=self.tokenizer.word2idx,
embed_dim=opt.embed_dim,
dat_fname='{0}_{1}_embedding_matrix.dat'.format(str(opt.embed_dim), opt.dataset))
self.model = opt.model_class(embedding_matrix, opt)
print('loading model {0} ...'.format(opt.model_name))
self.model.load_state_dict(torch.load(opt.state_dict_path))
self.model = self.model.to(opt.device)
# switch model to evaluation mode
self.model.eval()
torch.autograd.set_grad_enabled(False)
def evaluate(self, text, aspect):
aspect = aspect.lower().strip()
text_left, _, text_right = [s.strip() for s in text.lower().partition(aspect)]
text_indices = self.tokenizer.text_to_sequence(text_left + " " + aspect + " " + text_right)
context_indices = self.tokenizer.text_to_sequence(text_left + " " + text_right)
left_indices = self.tokenizer.text_to_sequence(text_left)
left_with_aspect_indices = self.tokenizer.text_to_sequence(text_left + " " + aspect)
right_indices = self.tokenizer.text_to_sequence(text_right, reverse=True)
right_with_aspect_indices = self.tokenizer.text_to_sequence(aspect + " " + text_right, reverse=True)
aspect_indices = self.tokenizer.text_to_sequence(aspect)
left_len = np.sum(left_indices != 0)
aspect_len = np.sum(aspect_indices != 0)
aspect_boundary = np.asarray([left_len, left_len + aspect_len - 1], dtype=np.int64)
text_len = np.sum(text_indices != 0)
concat_bert_indices = self.tokenizer.text_to_sequence('[CLS] ' + text_left + " " + aspect + " " + text_right + ' [SEP] ' + aspect + " [SEP]")
concat_segments_indices = [0] * (text_len + 2) + [1] * (aspect_len + 1)
concat_segments_indices = pad_and_truncate(concat_segments_indices, self.tokenizer.max_seq_len)
text_bert_indices = self.tokenizer.text_to_sequence("[CLS] " + text_left + " " + aspect + " " + text_right + " [SEP]")
aspect_bert_indices = self.tokenizer.text_to_sequence("[CLS] " + aspect + " [SEP]")
dependency_graph = dependency_adj_matrix(text)
data = {
'concat_bert_indices': concat_bert_indices,
'concat_segments_indices': concat_segments_indices,
'text_bert_indices': text_bert_indices,
'aspect_bert_indices': aspect_bert_indices,
'text_indices': text_indices,
'context_indices': context_indices,
'left_indices': left_indices,
'left_with_aspect_indices': left_with_aspect_indices,
'right_indices': right_indices,
'right_with_aspect_indices': right_with_aspect_indices,
'aspect_indices': aspect_indices,
'aspect_boundary': aspect_boundary,
'dependency_graph': dependency_graph,
}
t_inputs = [torch.tensor([data[col]], device=self.opt.device) for col in self.opt.inputs_cols]
t_outputs = self.model(t_inputs)
t_probs = F.softmax(t_outputs, dim=-1).cpu().numpy()
return t_probs
if __name__ == '__main__':
model_classes = {
'lstm': LSTM,
'td_lstm': TD_LSTM,
'tc_lstm': TC_LSTM,
'atae_lstm': ATAE_LSTM,
'ian': IAN,
'memnet': MemNet,
'ram': RAM,
'cabasc': Cabasc,
'tnet_lf': TNet_LF,
'aoa': AOA,
'mgan': MGAN,
'asgcn': ASGCN,
'bert_spc': BERT_SPC,
'aen_bert': AEN_BERT,
'lcf_bert': LCF_BERT,
}
dataset_files = {
'twitter': {
'train': './datasets/acl-14-short-data/train.raw',
'test': './datasets/acl-14-short-data/test.raw'
},
'restaurant': {
'train': './datasets/semeval14/Restaurants_Train.xml.seg',
'test': './datasets/semeval14/Restaurants_Test_Gold.xml.seg'
},
'laptop': {
'train': './datasets/semeval14/Laptops_Train.xml.seg',
'test': './datasets/semeval14/Laptops_Test_Gold.xml.seg'
}
}
input_colses = {
'lstm': ['text_indices'],
'td_lstm': ['left_with_aspect_indices', 'right_with_aspect_indices'],
'tc_lstm': ['left_with_aspect_indices', 'right_with_aspect_indices', 'aspect_indices'],
'atae_lstm': ['text_indices', 'aspect_indices'],
'ian': ['text_indices', 'aspect_indices'],
'memnet': ['context_indices', 'aspect_indices'],
'ram': ['text_indices', 'aspect_indices', 'left_indices'],
'cabasc': ['text_indices', 'aspect_indices', 'left_with_aspect_indices', 'right_with_aspect_indices'],
'tnet_lf': ['text_indices', 'aspect_indices', 'aspect_in_text'],
'aoa': ['text_indices', 'aspect_indices'],
'mgan': ['text_indices', 'aspect_indices', 'left_indices'],
'asgcn': ['text_indices', 'aspect_indices', 'left_indices', 'dependency_graph'],
'bert_spc': ['concat_bert_indices', 'concat_segments_indices'],
'aen_bert': ['text_bert_indices', 'aspect_bert_indices'],
'lcf_bert': ['concat_bert_indices', 'concat_segments_indices', 'text_bert_indices', 'aspect_bert_indices'],
}
class Option(object): pass
opt = Option()
opt.model_name = 'ian'
opt.model_class = model_classes[opt.model_name]
opt.dataset = 'restaurant'
opt.dataset_file = dataset_files[opt.dataset]
opt.inputs_cols = input_colses[opt.model_name]
# set your trained models here
opt.state_dict_path = 'state_dict/ian_restaurant_acc0.7911'
opt.embed_dim = 300
opt.hidden_dim = 300
opt.max_seq_len = 85
opt.bert_dim = 768
opt.pretrained_bert_name = 'bert-base-uncased'
opt.polarities_dim = 3
opt.hops = 3
opt.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
opt.local_context_focus = 'cdm'
opt.SRD = 3
inf = Inferer(opt)
t_probs = inf.evaluate('the service is terrible', 'service')
print(t_probs.argmax(axis=-1) - 1)