def decode(args: Dict[str, str]):
""" Performs decoding on a test set, and save the best-scoring decoding results.
If the target gold-standard sentences are given, the function also computes
corpus-level BLEU score.
@param args (Dict): args from cmd line
"""
print("load test source sentences from [{}]".format(
args['TEST_SOURCE_FILE']),
file=sys.stderr)
test_data_src = read_corpus(args['TEST_SOURCE_FILE'], source='src')
if args['TEST_TARGET_FILE']:
print("load test target sentences from [{}]".format(
args['TEST_TARGET_FILE']),
file=sys.stderr)
test_data_tgt = read_corpus(args['TEST_TARGET_FILE'], source='tgt')
else:
test_data_tgt = None
print("load model from {}".format(args['MODEL_PATH']), file=sys.stderr)
model = NMT.load(args['MODEL_PATH'])
if args['--cuda']:
model = model.to(torch.device("cuda:0"))
beam_size = int(args['--beam-size'])
max_decoding_time_step = int(args['--max-decoding-time-step'])
output_file = args['OUTPUT_FILE']
decode_with_params(model, test_data_src, test_data_tgt, beam_size,
max_decoding_time_step, output_file)
def decode(args: Dict[str, str]):
""" Performs decoding on a test set, and save the best-scoring decoding results.
If the target gold-standard sentences are given, the function also computes
corpus-level BLEU score.
@param args (Dict): args from cmd line
"""
print("load test source sentences from [{}]".format(args['TEST_SOURCE_FILE']), file=sys.stderr)
test_data_src = read_corpus(args['TEST_SOURCE_FILE'], source='src')
if args['TEST_TARGET_FILE']:
print("load test target sentences from [{}]".format(args['TEST_TARGET_FILE']), file=sys.stderr)
test_data_tgt = read_corpus(args['TEST_TARGET_FILE'], source='tgt')
print("load model from {}".format(args['MODEL_PATH']), file=sys.stderr)
model = NMT.load(args['MODEL_PATH'])
if args['--cuda']:
model = model.to(torch.device("cuda:0"))
hypotheses = beam_search(model, test_data_src,
beam_size=int(args['--beam-size']),
max_decoding_time_step=int(args['--max-decoding-time-step']))
if args['TEST_TARGET_FILE']:
top_hypotheses = [hyps[0] for hyps in hypotheses]
bleu_score = compute_corpus_level_bleu_score(test_data_tgt, top_hypotheses)
print('Corpus BLEU: {}'.format(bleu_score * 100), file=sys.stderr)
with open(args['OUTPUT_FILE'], 'w') as f:
for src_sent, hyps in zip(test_data_src, hypotheses):
top_hyp = hyps[0]
hyp_sent = ' '.join(top_hyp.value)
f.write(hyp_sent + '\n')
def test(self):
print('*' * 20, 'start test', '*' * 20)
self.model = NMT.load(self.hparams.model_save_path, self.device)
sources, references, hypotheses = self.beam_search()
bleu_score = compute_corpus_level_bleu_score(references, hypotheses)
print('Corpus BLEU: {}'.format(bleu_score * 100))
with open(self.hparams.test_res_path, 'w') as f:
for src_sent, hypo in zip(sources, hypotheses):
src_sent = ' '.join(src_sent)
hypo_sent = ' '.join(hypo.value)
f.write(src_sent + '\n' + hypo_sent + '\n\n')
print('save test result to {}'.format(self.hparams.test_res_path))
print('*' * 20, 'end test', '*' * 20)
