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train_bert.py
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train_bert.py
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# -*- coding: utf-8 -*-
import logging
import argparse
import math
import os
import sys
import random
import numpy
from sklearn import metrics
from time import strftime, localtime
from pytorch_pretrained_bert import BertModel
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, random_split
from data_utils_bert import build_tokenizer, build_embedding_matrix, Tokenizer4Bert, ABSADataset
from models import AAGCN_BERT, AAGCN_BERT14
logger = logging.getLogger()
logger.setLevel(logging.INFO)
logger.addHandler(logging.StreamHandler(sys.stdout))
class Instructor:
def __init__(self, opt):
self.opt = opt
if 'bert' in opt.model_name:
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:
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=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).to(opt.device)
self.trainset = ABSADataset(opt.dataset_file['train'], tokenizer)
self.testset = ABSADataset(opt.dataset_file['test'], tokenizer)
assert 0 <= opt.valset_ratio < 1
if opt.valset_ratio > 0:
valset_len = int(len(self.trainset) * opt.valset_ratio)
self.trainset, self.valset = random_split(self.trainset, (len(self.trainset)-valset_len, valset_len))
else:
self.valset = self.testset
if opt.device.type == 'cuda':
logger.info('cuda memory allocated: {}'.format(torch.cuda.memory_allocated(device=opt.device.index)))
self._print_args()
def _print_args(self):
n_trainable_params, n_nontrainable_params = 0, 0
for p in self.model.parameters():
n_params = torch.prod(torch.tensor(p.shape))
if p.requires_grad:
n_trainable_params += n_params
else:
n_nontrainable_params += n_params
logger.info('> n_trainable_params: {0}, n_nontrainable_params: {1}'.format(n_trainable_params, n_nontrainable_params))
logger.info('> training arguments:')
for arg in vars(self.opt):
logger.info('>>> {0}: {1}'.format(arg, getattr(self.opt, arg)))
def _reset_params(self):
for child in self.model.children():
if type(child) != BertModel:
for p in child.parameters():
if p.requires_grad:
if len(p.shape) > 1:
self.opt.initializer(p)
else:
stdv = 1. / math.sqrt(p.shape[0])
torch.nn.init.uniform_(p, a=-stdv, b=stdv)
def _train(self, criterion, optimizer, train_data_loader, val_data_loader):
max_val_acc = 0
max_val_f1 = 0
max_val_epoch = 0
global_step = 0
path = None
for i_epoch in range(self.opt.num_epoch):
logger.info('>' * 100)
logger.info('epoch: {}'.format(i_epoch))
n_correct, n_total, loss_total = 0, 0, 0
self.model.train()
for i_batch, batch in enumerate(train_data_loader):
global_step += 1
optimizer.zero_grad()
inputs = [batch[col].to(self.opt.device) for col in self.opt.inputs_cols]
outputs = self.model(inputs)
targets = batch['polarity'].to(self.opt.device)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
n_correct += (torch.argmax(outputs, -1) == targets).sum().item()
n_total += len(outputs)
loss_total += loss.item() * len(outputs)
if global_step % self.opt.log_step == 0:
train_acc = n_correct / n_total
train_loss = loss_total / n_total
logger.info('loss: {:.4f}, acc: {:.4f}'.format(train_loss, train_acc))
val_acc, val_f1 = self._evaluate_acc_f1(val_data_loader)
logger.info('> val_acc: {:.4f}, val_f1: {:.4f}'.format(val_acc, val_f1))
if val_acc > max_val_acc:
max_val_acc = val_acc
max_val_epoch = i_epoch
if not os.path.exists('state_dict'):
os.mkdir('state_dict')
path = 'state_dict/{0}_{1}_val_acc_{2}'.format(self.opt.model_name, self.opt.dataset, round(val_acc, 4))
torch.save(self.model.state_dict(), path)
logger.info('>> saved: {}'.format(path))
if val_f1 > max_val_f1:
max_val_f1 = val_f1
if i_epoch - max_val_epoch >= self.opt.patience:
print('>> early stop.')
break
return path
def _evaluate_acc_f1(self, data_loader):
n_correct, n_total = 0, 0
t_targets_all, t_outputs_all = None, None
self.model.eval()
with torch.no_grad():
for i_batch, t_batch in enumerate(data_loader):
t_inputs = [t_batch[col].to(self.opt.device) for col in self.opt.inputs_cols]
t_targets = t_batch['polarity'].to(self.opt.device)
t_outputs = self.model(t_inputs)
n_correct += (torch.argmax(t_outputs, -1) == t_targets).sum().item()
n_total += len(t_outputs)
if t_targets_all is None:
t_targets_all = t_targets
t_outputs_all = t_outputs
else:
t_targets_all = torch.cat((t_targets_all, t_targets), dim=0)
t_outputs_all = torch.cat((t_outputs_all, t_outputs), dim=0)
acc = n_correct / n_total
f1 = metrics.f1_score(t_targets_all.cpu(), torch.argmax(t_outputs_all, -1).cpu(), labels=[0, 1, 2], average='macro')
return acc, f1
def run(self):
criterion = nn.CrossEntropyLoss()
_params = filter(lambda p: p.requires_grad, self.model.parameters())
optimizer = self.opt.optimizer(_params, lr=self.opt.lr, weight_decay=self.opt.l2reg)
train_data_loader = DataLoader(dataset=self.trainset, batch_size=self.opt.batch_size, shuffle=True)
test_data_loader = DataLoader(dataset=self.testset, batch_size=self.opt.batch_size, shuffle=False)
val_data_loader = DataLoader(dataset=self.valset, batch_size=self.opt.batch_size, shuffle=False)
self._reset_params()
best_model_path = self._train(criterion, optimizer, train_data_loader, val_data_loader)
self.model.load_state_dict(torch.load(best_model_path))
test_acc, test_f1 = self._evaluate_acc_f1(test_data_loader)
logger.info('>> test_acc: {:.4f}, test_f1: {:.4f}'.format(test_acc, test_f1))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model_name', default='bert_spc', type=str)
parser.add_argument('--dataset', default='laptop', type=str, help='twitter, restaurant, laptop')
parser.add_argument('--optimizer', default='adam', type=str)
parser.add_argument('--initializer', default='xavier_uniform_', type=str)
parser.add_argument('--lr', default=2e-5, type=float, help='try 5e-5, 2e-5 for BERT, 1e-3 for others')
parser.add_argument('--dropout', default=0.1, type=float)
parser.add_argument('--l2reg', default=0.01, type=float)
parser.add_argument('--num_epoch', default=20, type=int, help='try larger number for non-BERT models')
parser.add_argument('--batch_size', default=16, type=int, help='try 16, 32, 64 for BERT models')
parser.add_argument('--log_step', default=10, type=int)
parser.add_argument('--embed_dim', default=300, type=int)
parser.add_argument('--hidden_dim', default=300, type=int)
parser.add_argument('--bert_dim', default=768, type=int)
parser.add_argument('--pretrained_bert_name', default='bert-base-uncased', type=str)
parser.add_argument('--max_seq_len', default=85, type=int)
parser.add_argument('--polarities_dim', default=3, type=int)
parser.add_argument('--hops', default=3, type=int)
parser.add_argument('--patience', default=5, type=int)
parser.add_argument('--device', default=None, type=str, help='e.g. cuda:0')
parser.add_argument('--seed', default=1234, type=int, help='set seed for reproducibility')
parser.add_argument('--valset_ratio', default=0, type=float, help='set ratio between 0 and 1 for validation support')
opt = parser.parse_args()
model_classes = {
'aagcn_bert': AAGCN_BERT,
'aagcn_bert14': AAGCN_BERT14,
#'afgcn_bert': AFGCN_BERT,
}
dataset_files = {
'rest15_sen': {
'train': './dataset/senticnet/15_rest_train.raw.tokenized',
'test': './dataset/senticnet/15_rest_test.raw.tokenized'
},
'lap15_sen': {
'train': './dataset/senticnet/15_lap_train.raw.tokenized',
'test': './dataset/senticnet/15_lap_test.raw.tokenized'
},
'rest15_con': {
'train': './dataset/conceptnet/15_rest_train.raw.tokenized',
'test': './dataset/conceptnet/15_rest_test.raw.tokenized'
},
'lap15_con': {
'train': './dataset/conceptnet/15_lap_train.raw.tokenized',
'test': './dataset/conceptnet/15_lap_test.raw.tokenized'
},
'rest16_sen': {
'train': './dataset/senticnet/16_rest_train.raw.tokenized',
'test': './dataset/senticnet/16_rest_test.raw.tokenized'
},
'lap16_sen': {
'train': './dataset/senticnet/16_lap_train.raw.tokenized',
'test': './dataset/senticnet/16_lap_test.raw.tokenized'
},
'rest16_con': {
'train': './dataset/conceptnet/16_rest_train.raw.tokenized',
'test': './dataset/conceptnet/16_rest_test.raw.tokenized'
},
'lap16_con': {
'train': './dataset/conceptnet/16_lap_train.raw.tokenized',
'test': './dataset/conceptnet/16_lap_test.raw.tokenized'
},
'14_con': {
'train': './dataset/conceptnet/14_train.raw.tokenized',
'test': './dataset/conceptnet/14_test.raw.tokenized'
},
'14_sen': {
'train': './dataset/senticnet/14_train.raw.tokenized',
'test': './dataset/senticnet/14_test.raw.tokenized'
}
}
input_colses = {
'aagcn_bert': ['concat_bert_indices', 'concat_segments_indices', 'entity_graph', 'attribute_graph'],
'aagcn_bert14': ['concat_bert_indices', 'concat_segments_indices', 'entity_graph', 'attribute_graph'],
#'afgcn_bert': ['concat_bert_indices', 'concat_segments_indices', 'entity_graph'],
}
initializers = {
'xavier_uniform_': torch.nn.init.xavier_uniform_,
'xavier_normal_': torch.nn.init.xavier_normal_,
'orthogonal_': torch.nn.init.orthogonal_,
}
optimizers = {
'adadelta': torch.optim.Adadelta,
'adagrad': torch.optim.Adagrad,
'adam': torch.optim.Adam,
'adamax': torch.optim.Adamax,
'asgd': torch.optim.ASGD,
'rmsprop': torch.optim.RMSprop,
'sgd': torch.optim.SGD,
}
opt.model_class = model_classes[opt.model_name]
opt.dataset_file = dataset_files[opt.dataset]
opt.inputs_cols = input_colses[opt.model_name]
opt.initializer = initializers[opt.initializer]
opt.optimizer = optimizers[opt.optimizer]
opt.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') \
if opt.device is None else torch.device(opt.device)
log_file = '{}-{}-{}.log'.format(opt.model_name, opt.dataset, strftime("%y%m%d-%H%M", localtime()))
logger.addHandler(logging.FileHandler(log_file))
# for seed in range(1000):
# print('\n=======================> seed:', seed, '\n')
# random.seed(seed)
# numpy.random.seed(seed)
# torch.manual_seed(seed)
# torch.cuda.manual_seed(seed)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
# os.environ['PYTHONHASHSEED'] = str(opt.seed)
# ins = Instructor(opt)
# ins.run()
# print('#'*30)
seed = opt.seed
print('\n=======================> seed:', seed, '\n')
random.seed(seed)
numpy.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
os.environ['PYTHONHASHSEED'] = str(opt.seed)
ins = Instructor(opt)
ins.run()
print('#'*30)
if __name__ == '__main__':
main()