from utils import build_dataset, build_iterator, get_time_dif
# 相当于 import models.model_name
x = import_module('models.' + model_name)
# 相当于 models.model_name.Config(dataset, embedding)
config = x.Config(dataset, embedding)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed_all(1)
torch.backends.cudnn.deterministic = True # 保证每次结果一样
# load data
start_time = time.time()
print("Loading data...")
is_train = False
vocab, train_data, dev_data, test_data = build_dataset(
config, args.word, is_train)
if is_train:
train_iter = build_iterator(train_data, config)
dev_iter = build_iterator(dev_data, config)
test_iter = build_iterator(test_data, config)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
# model
config.n_vocab = len(vocab)
model = x.Model(config).to(config.device)
if model_name != 'Transformer':
init_network(model)
try:
model.load_state_dict(torch.load(config.save_path))
print('load model')
from utils_fasttext import build_dataset, build_iterator, get_time_dif
embedding = 'random'
else:
from utils import build_dataset, build_iterator, get_time_dif
x = import_module('models.' + model_name)
config = x.Config(dataset, embedding)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed_all(1)
torch.backends.cudnn.deterministic = True # 保证每次结果一样
start_time = time.time()
print("Loading data...")
vocab, train_data, dev_data, test_data = build_dataset(config, word)
train_iter = build_iterator(train_data, config)
dev_iter = build_iterator(dev_data, config)
test_iter = build_iterator(test_data, config)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
# train
config.n_vocab = len(vocab)
# to 是为了去除device
model = x.Model(config).to(config.device)
if model_name != 'Transformer':
init_network(model)
print(model.parameters)
train(config, model, train_iter, dev_iter, test_iter)
model_name = args.model # 'TextRCNN' # TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att, DPCNN, Transformer
if model_name == 'FastText':
from utils_fasttext import build_dataset, build_iterator, get_time_dif
embedding = 'random'
else:
from utils import build_dataset, build_iterator, get_time_dif
x = import_module('models.' + model_name)
config = x.Config(dataset, embedding)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed_all(1)
torch.backends.cudnn.deterministic = True # 保证每次结果一样
start_time = time.time()
print("Loading data...")
w2v_vec, train_data, dev_data, test_data = build_dataset(config, args.word)
train_iter = build_iterator(train_data, config, w2v_vec)
dev_iter = build_iterator(dev_data, config, w2v_vec)
test_iter = build_iterator(test_data, config, w2v_vec)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
# train
config.n_vocab = len(w2v_vec.wv.vocab)
model = x.Model(config).to(config.device)
if model_name != 'Transformer':
init_network(model)
print(model.parameters)
train(config, model, train_iter, dev_iter, test_iter)
