wehub-resource-sync
93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import io
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import numpy as np
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import paddle
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from args import parse_args
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from data import create_infer_loader
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from seq2seq_attn import Seq2SeqAttnInferModel
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from paddlenlp.data import Vocab
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from paddlenlp.metrics import BLEU
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def post_process_seq(seq, bos_idx, eos_idx, output_bos=False, output_eos=False):
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"""
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Post-process the decoded sequence.
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"""
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eos_pos = len(seq) - 1
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for i, idx in enumerate(seq):
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if idx == eos_idx:
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eos_pos = i
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break
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seq = [idx for idx in seq[: eos_pos + 1] if (output_bos or idx != bos_idx) and (output_eos or idx != eos_idx)]
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return seq
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def do_predict(args):
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paddle.set_device(args.device)
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test_loader, src_vocab_size, tgt_vocab_size, bos_id, eos_id = create_infer_loader(args)
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tgt_vocab = Vocab.load_vocabulary(**test_loader.dataset.vocab_info["vi"])
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model = paddle.Model(
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Seq2SeqAttnInferModel(
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src_vocab_size,
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tgt_vocab_size,
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args.hidden_size,
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args.hidden_size,
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args.num_layers,
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args.dropout,
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bos_id=bos_id,
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eos_id=eos_id,
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beam_size=args.beam_size,
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max_out_len=256,
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)
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)
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model.prepare()
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# Load the trained model
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assert args.init_from_ckpt, "Please set reload_model to load the infer model."
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model.load(args.init_from_ckpt)
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cand_list = []
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with io.open(args.infer_output_file, "w", encoding="utf-8") as f:
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for data in test_loader():
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with paddle.no_grad():
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finished_seq = model.predict_batch(inputs=data)[0]
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finished_seq = finished_seq[:, :, np.newaxis] if len(finished_seq.shape) == 2 else finished_seq
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finished_seq = np.transpose(finished_seq, [0, 2, 1])
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for ins in finished_seq:
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for beam_idx, beam in enumerate(ins):
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id_list = post_process_seq(beam, bos_id, eos_id)
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word_list = [tgt_vocab.to_tokens(id) for id in id_list]
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sequence = " ".join(word_list) + "\n"
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f.write(sequence)
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cand_list.append(word_list)
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break
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bleu = BLEU()
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for i, data in enumerate(test_loader.dataset.data):
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ref = data["vi"].split()
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bleu.add_inst(cand_list[i], [ref])
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print("BLEU score is %s." % bleu.score())
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if __name__ == "__main__":
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args = parse_args()
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do_predict(args)
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