model = RNN(train_config) elif train_config["model"] == "TXL": model = TXL(train_config) elif train_config["model"] == "RGCN": model = RGCN(train_config) elif train_config["model"] == "RGIN": model = RGIN(train_config) else: raise NotImplementedError("Currently, the %s model is not supported" % (train_config["model"])) model.load_state_dict( torch.load(os.path.join(finetune_config["load_model_dir"], "epoch%d.pt" % (best_epochs["dev"])), map_location=torch.device("cpu"))) model.increase_net(finetune_config) if not all([ train_config[key] == finetune_config[key] for key in [ "max_npv", "max_npe", "max_npvl", "max_npel", "max_ngv", "max_nge", "max_ngvl", "max_ngel", "share_emb", "share_arch" ] ]): model.increase_input_size(finetune_config) if not all([ train_config[key] == finetune_config[key] for key in [ "predict_net", "predict_net_hidden_dim", "predict_net_num_heads", "predict_net_mem_len", "predict_net_mem_init", "predict_net_recurrent_steps" ] ]): new_predict_net = model.create_predict_net(