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