#checkpoint = torch.load(loadFilename, map_location=torch.device('cpu')) encoder_sd = checkpoint['en'] decoder_sd = checkpoint['de'] encoder_optimizer_sd = checkpoint['en_opt'] decoder_optimizer_sd = checkpoint['de_opt'] input_embedding_sd = checkpoint['input_embedding'] output_embedding_sd = checkpoint['output_embedding'] inputVoc.__dict__ = checkpoint['input_voc_dict'] outputVoc.__dict__ = checkpoint['output_voc_dict'] input_embedding.load_state_dict(input_embedding_sd) output_embedding.load_state_dict(output_embedding_sd) encoder.load_state_dict(encoder_sd) decoder.load_state_dict(decoder_sd) ''' encoder.eval() decoder.eval() # Initialize search module # searcher = Model.GreedySearchDecoder(encoder, decoder) searcher = Model.BeamSearchDecoder(encoder, decoder) result_list = EvaluateUtil.evaluate_data(searcher, inputVoc, outputVoc, test_pairs) for i in result_list: for j in i: print(j) print()