def instantiate_model(model_name, vocab_size, embeddings):
multi_layer_args = yaml.load(open('./configs/multi_layer.yml'), Loader=yaml.FullLoader)
if model_name == "rcnn":
model_args = yaml.load(open('./configs/rcnn.yml'), Loader=yaml.FullLoader)
model = RCNN(vocab_size, embeddings, **{**model_args, **multi_layer_args})
elif model_name == "textcnn":
model_args = yaml.load(open('./configs/textcnn.yml'), Loader=yaml.FullLoader)
model = TextCNN(vocab_size, embeddings, **{**model_args, **multi_layer_args})
elif model_name == "textrnn":
model_args = yaml.load(open('./configs/textrnn.yml'), Loader=yaml.FullLoader)
model = TextRNN(vocab_size, embeddings, **{**model_args, **multi_layer_args})
elif model_name == "attention_rnn":
model_args = yaml.load(open('./configs/attention_rnn.yml'), Loader=yaml.FullLoader)
model = AttentionRNN(vocab_size, embeddings, **{**model_args, **multi_layer_args})
elif model_name == "transformer":
model_args = yaml.load(open('./configs/transformer.yml'), Loader=yaml.FullLoader)
model = Transformer(vocab_size, embeddings, **{**model_args, **multi_layer_args})
else:
model_args = yaml.load(open('./configs/fasttext.yml'), Loader=yaml.FullLoader)
model = FastText(vocab_size, embeddings, **{**model_args, **multi_layer_args})
logger = get_logger(__name__)
logger.info("A model of {} is instantiated.".format(model.__class__.__name__))
return model
def model_selector(config, model_id, use_element):
model = None
if use_element:
print("use element")
model = ModelWithElement(config, model_id)
elif model_id == 0:
model = FastText(config)
elif model_id == 1:
model = TextCNN(config)
elif model_id == 2:
model = TextRCNN(config)
elif model_id == 3:
model = TextRNN(config)
elif model_id == 4:
model = HAN(config)
elif model_id == 5:
model = CNNWithDoc2Vec(config)
elif model_id == 6:
model = RCNNWithDoc2Vec(config)
elif model_id == 7:
model = CNNwithInception(config)
else:
print("Input ERROR!")
exit(2)
return model
def load_multi_model(model_path, model_id, config):
if model_id == 0:
model = FastText(config)
elif model_id == 1:
model = TextCNN(config)
elif model_id == 2:
model = TextRCNN(config)
elif model_id == 3:
model = TextRNN(config)
elif model_id == 4:
model = HAN(config)
# print(model)
# time_stamp = '1510844987'
# final_model_path = config.model_path+"."+time_stamp+".multi."+config.model_names[model_id]
print("load model data:", model_path)
model.load_state_dict(torch.load(model_path))
if config.has_cuda:
model = model.cuda()
return model
def _model_selector(config, model_id):
model = None
if model_id == 0:
model = FastText(config)
elif model_id == 1:
model = TextCNN(config)
elif model_id == 2:
model = TextRCNN(config)
elif model_id == 4:
model = HAN(config)
elif model_id == 5:
model = CNNWithDoc2Vec(config)
elif model_id == 6:
model = RCNNWithDoc2Vec(config)
else:
print("Input ERROR!")
exit(2)
return model
def get_models(
vocab_size, # 词典大小
n_class=10, # 类别个数
seq_len=38, # 句子长度
device=None): # 设备
""" 获取所有需要训练的模型 """
if device is None:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
fast_text = FastText(vocab_size=vocab_size, n_class=n_class)
text_cnn = TextCNN(vocab_size=vocab_size, n_class=n_class)
text_rnn = TextRNN(vocab_size=vocab_size, n_class=n_class)
text_rcnn = TextRCNN(vocab_size=vocab_size, n_class=n_class)
transformer = Transformer(vocab_size=vocab_size,
seq_len=seq_len,
n_class=n_class,
device=device)
return [fast_text, text_cnn, text_rnn, text_rcnn, transformer]
sh = logging.StreamHandler()
sh.setFormatter(fmt)
sh.setLevel(logging.DEBUG)
logger.addHandler(sh)
# 设置文件日志
fh = logging.FileHandler(path, encoding='utf-8')
fh.setFormatter(fmt)
fh.setLevel(logging.DEBUG)
logger.addHandler(fh)
return logger
if __name__ == '__main__':
from dataprocess import get_vocab, load_dataset, DataIter
from models.fasttext import FastText
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
c2i = get_vocab()
model = FastText(vocab_size=len(c2i), n_class=10)
# data, labels = load_dataset('train')
dev_samples = load_dataset('dev')
trian_iter = DataIter(dev_samples)
test_iter = DataIter(dev_samples)
train(model, trian_iter, test_iter, device=device)
pass