M3FEND/grid_search.py at main · ICTMCG/M3FEND · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
import torch
import tqdm
import pickle
import logging
import os
import time
import json
from copy import deepcopy

from utils.utils import Averager
from utils.dataloader import bert_data
from models.textcnn import Trainer as TextCNNTrainer
from models.bigru import Trainer as BiGRUTrainer
from models.bert import Trainer as BertTrainer
from models.eann import Trainer as EANNTrainer
from models.eddfn import Trainer as EDDFNTrainer
from models.mmoe import Trainer as MMoETrainer
from models.mose import Trainer as MoSETrainer
from models.mdfend import Trainer as MDFENDTrainer
from models.m3fend import Trainer as M3FENDTrainer
from models.dualemotion import Trainer as DualEmotionTrainer
from models.stylelstm import Trainer as StyleLstmTrainer


def frange(x, y, jump):
  while x < y:
      x = round(x, 8)
      yield x
      x += jump

class Run():
    def __init__(self,
                 config
                 ):
        self.configinfo = config

        self.use_cuda = config['use_cuda']
        self.model_name = config['model_name']
        self.batchsize = config['batchsize']
        self.emb_dim = config['emb_dim']
        self.weight_decay = config['weight_decay']
        self.lr = config['lr']
        self.epoch = config['epoch']
        self.max_len = config['max_len']
        self.num_workers = config['num_workers']
        self.early_stop = config['early_stop']
        self.root_path = config['root_path']
        self.mlp_dims = config['model']['mlp']['dims']
        self.dropout = config['model']['mlp']['dropout']
        self.seed = config['seed']
        self.save_log_dir = config['save_log_dir']
        self.save_param_dir = config['save_param_dir']
        self.param_log_dir = config['param_log_dir']

        self.semantic_num = config['semantic_num']
        self.emotion_num = config['emotion_num']
        self.style_num = config['style_num']
        self.lnn_dim = config['lnn_dim']
        self.domain_num = config['domain_num']
        self.category_dict = config['category_dict']
        self.dataset = config['dataset']


        self.train_path = self.root_path + 'train.pkl'
        self.val_path = self.root_path + 'val.pkl'
        self.test_path = self.root_path + 'test.pkl'


    def get_dataloader(self):
        loader = bert_data(max_len = self.max_len, batch_size = self.batchsize,
                        category_dict = self.category_dict, num_workers=self.num_workers, dataset = self.dataset)
        train_loader = loader.load_data(self.train_path, True)
        val_loader = loader.load_data(self.val_path, False)
        test_loader = loader.load_data(self.test_path, False)
        return train_loader, val_loader, test_loader

    def getFileLogger(self, log_file):
        logger = logging.getLogger()
        logger.setLevel(level = logging.INFO)
        handler = logging.FileHandler(log_file)
        handler.setLevel(logging.INFO)
        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
        handler.setFormatter(formatter)
        logger.addHandler(handler)
        return logger

    def config2dict(self):
        config_dict = {}
        for k, v in self.configinfo.items():
            config_dict[k] = v
        return config_dict

    def main(self):
        param_log_dir = self.param_log_dir
        if not os.path.exists(param_log_dir):
            os.makedirs(param_log_dir)
        param_log_file = os.path.join(param_log_dir, self.model_name +'_'+ 'oneloss_param.txt')
        logger = self.getFileLogger(param_log_file)

        train_loader, val_loader, test_loader = self.get_dataloader()
        if self.model_name == 'textcnn':
            trainer = TextCNNTrainer(emb_dim = self.emb_dim, mlp_dims = self.mlp_dims,
                use_cuda = self.use_cuda, lr = self.lr, train_loader = train_loader, dropout = self.dropout, weight_decay = self.weight_decay, val_loader = val_loader, test_loader = test_loader, category_dict = self.category_dict, early_stop = self.early_stop, epoches = self.epoch, dataset = self.dataset,
                save_param_dir = os.path.join(self.save_param_dir, self.model_name))
        elif self.model_name == 'eann':
            trainer = EANNTrainer(emb_dim = self.emb_dim, mlp_dims = self.mlp_dims,  dataset = self.dataset,
                use_cuda = self.use_cuda, lr = self.lr, train_loader = train_loader, dropout = self.dropout, weight_decay = self.weight_decay, val_loader = val_loader, test_loader = test_loader, category_dict = self.category_dict, early_stop = self.early_stop, epoches = self.epoch, domain_num = self.domain_num,
                save_param_dir = os.path.join(self.save_param_dir, self.model_name))
        elif self.model_name == 'eddfn':
            trainer = EDDFNTrainer(emb_dim = self.emb_dim, mlp_dims = self.mlp_dims, dataset = self.dataset,
                use_cuda = self.use_cuda, lr = self.lr, train_loader = train_loader, dropout = self.dropout, weight_decay = self.weight_decay, val_loader = val_loader, test_loader = test_loader, category_dict = self.category_dict, early_stop = self.early_stop, epoches = self.epoch, domain_num = self.domain_num,
                save_param_dir = os.path.join(self.save_param_dir, self.model_name))
        elif self.model_name == 'bigru':
            trainer = BiGRUTrainer(emb_dim = self.emb_dim, mlp_dims = self.mlp_dims, dataset = self.dataset,
                use_cuda = self.use_cuda, lr = self.lr, train_loader = train_loader, dropout = self.dropout, weight_decay = self.weight_decay, val_loader = val_loader, test_loader = test_loader, category_dict = self.category_dict, num_layers = 1, early_stop = self.early_stop, epoches = self.epoch,
                save_param_dir = os.path.join(self.save_param_dir, self.model_name))
        elif self.model_name == 'mmoe':
            trainer = MMoETrainer(emb_dim = self.emb_dim, mlp_dims = self.mlp_dims, dataset = self.dataset,
                use_cuda = self.use_cuda, lr = self.lr, train_loader = train_loader, dropout = self.dropout, weight_decay = self.weight_decay, val_loader = val_loader, test_loader = test_loader, category_dict = self.category_dict, num_layers = 1, early_stop = self.early_stop, epoches = self.epoch,
                save_param_dir = os.path.join(self.save_param_dir, self.model_name))
        elif self.model_name == 'mdfend':
            trainer = MDFENDTrainer(emb_dim = self.emb_dim, mlp_dims = self.mlp_dims, dataset = self.dataset,
                use_cuda = self.use_cuda, lr = self.lr, train_loader = train_loader, dropout = self.dropout, weight_decay = self.weight_decay, val_loader = val_loader, test_loader = test_loader, category_dict = self.category_dict, early_stop = self.early_stop, epoches = self.epoch,
                save_param_dir = os.path.join(self.save_param_dir, self.model_name))
        elif self.model_name == 'mose':
            trainer = MoSETrainer(emb_dim = self.emb_dim, mlp_dims = self.mlp_dims, dataset = self.dataset,
                use_cuda = self.use_cuda, lr = self.lr, train_loader = train_loader, dropout = self.dropout, weight_decay = self.weight_decay, val_loader = val_loader, test_loader = test_loader, category_dict = self.category_dict, num_layers = 1, early_stop = self.early_stop, epoches = self.epoch,
                save_param_dir = os.path.join(self.save_param_dir, self.model_name))
        elif self.model_name == 'bert':
            trainer = BertTrainer(emb_dim = self.emb_dim, mlp_dims = self.mlp_dims, dataset = self.dataset,
                use_cuda = self.use_cuda, lr = self.lr, train_loader = train_loader, dropout = self.dropout, weight_decay = self.weight_decay, val_loader = val_loader, test_loader = test_loader, category_dict = self.category_dict, early_stop = self.early_stop, epoches = self.epoch,
                save_param_dir = os.path.join(self.save_param_dir, self.model_name))
        elif self.model_name == 'm3fend':
            trainer = M3FENDTrainer(emb_dim = self.emb_dim, mlp_dims = self.mlp_dims, use_cuda = self.use_cuda, lr = self.lr, train_loader = train_loader, dropout = self.dropout, weight_decay = self.weight_decay, val_loader = val_loader, test_loader = test_loader, category_dict = self.category_dict, early_stop = self.early_stop, epoches = self.epoch, save_param_dir = os.path.join(self.save_param_dir, self.model_name), semantic_num = self.semantic_num, emotion_num = self.emotion_num, style_num = self.style_num, lnn_dim = self.lnn_dim,dataset = self.dataset)
        elif self.model_name == 'dualemotion':
            trainer = DualEmotionTrainer(emb_dim = self.emb_dim, mlp_dims = self.mlp_dims, dataset = self.dataset,
                use_cuda = self.use_cuda, lr = self.lr, train_loader = train_loader, dropout = self.dropout, weight_decay = self.weight_decay, val_loader = val_loader, test_loader = test_loader, category_dict = self.category_dict, early_stop = self.early_stop, epoches = self.epoch,
                save_param_dir = os.path.join(self.save_param_dir, self.model_name))
        elif self.model_name == 'stylelstm':
            trainer = StyleLstmTrainer(emb_dim = self.emb_dim, mlp_dims = self.mlp_dims, dataset = self.dataset,
                use_cuda = self.use_cuda, lr = self.lr, train_loader = train_loader, dropout = self.dropout, weight_decay = self.weight_decay, val_loader = val_loader, test_loader = test_loader, category_dict = self.category_dict, early_stop = self.early_stop, epoches = self.epoch,
                save_param_dir = os.path.join(self.save_param_dir, self.model_name))

        train_param = {
            'lr': [self.lr] * 10
        }
        print(train_param)

        param = train_param
        best_param = []

        json_path = './logs/json/' + self.model_name +'.json'
        json_result = []
        for p, vs in param.items():
            best_metric = {}
            best_metric['metric'] = 0
            best_v = vs[0]
            best_model_path = None
            for i, v in enumerate(vs):
                setattr(trainer, p, v)
                metrics, model_path = trainer.train(logger)
                json_result.append(metrics)
                if(metrics['metric'] > best_metric['metric']):
                    best_metric = metrics
                    best_v = v
                    best_model_path = model_path
            best_param.append({p: best_v})
            print("best model path:", best_model_path)
            print("best metric:", best_metric)
            logger.info("best model path:" + best_model_path)
            logger.info("best param " + p + ": " + str(best_v))
            logger.info("best metric:" + str(best_metric))
            logger.info('--------------------------------------\n')
        with open(json_path, 'w') as file:
            json.dump(json_result, file, indent=4, ensure_ascii=False)