#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()
