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.
"""
test_src_dir = os.path.join(args.test_dir, args.input_col.lower())
test_tgt_dir = os.path.join(args.test_dir, args.output_col.lower())
print(f"load test source sentences from [{test_src_dir}]", file=sys.stderr)
test_data_src = read_corpus(test_src_dir, source='src')
if test_tgt_dir:
print(f"load test target sentences from [{test_tgt_dir}]", file=sys.stderr)
test_data_tgt = read_corpus(test_tgt_dir, source='tgt')
model_path = os.path.join(args.model_dir, 'model.bin')
print(f"load model from {model_path}", file=sys.stderr)
model = NMT.load(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))
top_hypotheses = [hyps[0] for hyps in hypotheses]
bleu_score = compute_corpus_level_bleu_score(test_data_tgt, top_hypotheses)
print(f'Corpus BLEU: {bleu_score}', file=sys.stderr)
output_path = os.path.join(args.eval_dir, 'decode.txt')
with open(output_path, 'w') as f:
f.write(str(bleu_score))
def init():
global model
model_dir = Model.get_model_path('arxiv-nmt-pipeline')
model_path = os.path.join(model_dir, 'model.bin')
model = NMT.load(model_path)
model.eval()