help='Use id of gpu, -1 if cpu.')
args, extra_args = argparser.parse_known_args()
config = Configurable(args.config_file, extra_args)
parser_config = ParserConfigurable(args.parser_config_file)
torch.set_num_threads(args.thread)
dep_vocab = pickle.load(open(parser_config.load_vocab_path, 'rb'))
parser_model = ParserModel(dep_vocab, parser_config)
dump_model = torch.load(parser_config.load_model_path,
map_location=lambda storage, loc: storage)
dep_vec = dump_model["extword_embed.weight"].detach().cpu().numpy()
del dump_model["extword_embed.weight"]
parser_model.load_state_dict(dump_model)
parser_extembed = ExtWord(dep_vocab, parser_config, dep_vec)
torch.save(parser_model.state_dict(), config.save_model_path + ".synbasic")
torch.save(parser_extembed.state_dict(),
config.save_model_path + ".synvec")
pickle.dump(dep_vocab, open(config.save_vocab_path + ".syn", 'wb'))
vocab = creatVocab(config.train_file, config.min_occur_count)
vec = vocab.load_initialize_embs(config.pretrained_embeddings_file)
pickle.dump(vocab, open(config.save_vocab_path, 'wb'))
config.use_cuda = False
gpu_id = -1
if gpu and args.gpu != -1:
config.use_cuda = True
torch.cuda.set_device(args.gpu)
print('GPU ID:' + str(args.gpu))
gpu_id = args.gpu
