def main():
arg_parser = argparse.ArgumentParser(
description="Neural Machine Translation Testing")
arg_parser.add_argument("--model_file", required=True, help="Model File")
arg_parser.add_argument("--valid_data",
required=True,
nargs="+",
help="Validation_data")
args = arg_parser.parse_args()
args = vars(args)
print(args)
model = Seq2Seq.load(args["model_file"])
print(model)
model.device = "cpu"
tr_dev_dataset_fn, en_dev_dataset_fn = args["valid_data"]
tr_valid_data = read_text(tr_dev_dataset_fn)
en_valid_data = read_text(en_dev_dataset_fn)
valid_data = list(zip(tr_valid_data, en_valid_data))
src_valid, tgt_valid = add_start_end_tokens(valid_data)
hypotheses = beam_search(model,
src_valid,
beam_size=3,
max_decoding_time_step=70)
top_hypotheses = [hyps[0] for hyps in hypotheses]
bleu_score = compute_corpus_level_bleu_score(tgt_valid, top_hypotheses)
print('Corpus BLEU: {}'.format(bleu_score * 100))
def translate():
if request.method == "GET":
return render_template("index.html")
elif request.method == "POST":
args = request.form
print(args)
text_input = args["textarea"]
print("Input: ", text_input)
tokenized_sent = tokenizer.tokenize(text_input)
print("Tokenized input: ", tokenized_sent)
with open(VOCAB_FILE, "rb") as f:
vocabs = pickle.load(f)
model = Seq2Seq.load(MODEL_PATH)
model.device = "cpu"
hypothesis = beam_search(model, [tokenized_sent],
beam_size=3,
max_decoding_time_step=70)[0]
print("Hypothesis")
print(hypothesis)
for i in range(3):
new_target = [['<sos>'] + hypothesis[i].value + ['<eos>']]
a_ts = generate_attention_map(model, vocabs, [tokenized_sent],
new_target)
save_attention(tokenized_sent,
hypothesis[i].value, [
a[0].detach().cpu().numpy()
for a in a_ts[:len(hypothesis[i].value)]
],
save_path="static/list_{}.png".format(i))
result_hypothesis = []
for idx, hyp in enumerate(hypothesis):
result_hypothesis.append((idx, " ".join(hyp.value)))
return render_template("index.html",
hypothesis=result_hypothesis,
sentence=text_input)