predict.py

# -*- coding: UTF-8 -*-
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
from functools import partial

import paddle
from data import convert_example, load_dataset, load_vocab, parse_result
from model import BiGruCrf

from paddlenlp.data import Pad, Stack, Tuple

# fmt: off
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--data_dir", type=str, default=None, help="The folder where the dataset is located.")
parser.add_argument("--init_checkpoint", type=str, default=None, help="Path to init model.")
parser.add_argument("--batch_size", type=int, default=300, help="The number of sequences contained in a mini-batch.")
parser.add_argument("--max_seq_len", type=int, default=64, help="Number of words of the longest seqence.")
parser.add_argument("--device", default="gpu", type=str, choices=["cpu", "gpu"], help="The device to select to train the model, is must be cpu/gpu.")
parser.add_argument("--emb_dim", type=int, default=128, help="The dimension in which a word is embedded.")
parser.add_argument("--hidden_size", type=int, default=128, help="The number of hidden nodes in the GRU layer.")
args = parser.parse_args()
# fmt: on


def infer(args):
 paddle.set_device(args.device)

 # create dataset.
 infer_ds = load_dataset(datafiles=(os.path.join(args.data_dir, "infer.tsv")))
 word_vocab = load_vocab(os.path.join(args.data_dir, "word.dic"))
 label_vocab = load_vocab(os.path.join(args.data_dir, "tag.dic"))
 # q2b.dic is used to replace DBC case to SBC case
 normlize_vocab = load_vocab(os.path.join(args.data_dir, "q2b.dic"))

 trans_func = partial(
 convert_example,
 max_seq_len=args.max_seq_len,
 word_vocab=word_vocab,
 label_vocab=label_vocab,
 normlize_vocab=normlize_vocab,
 )
 infer_ds.map(trans_func)

 batchify_fn = lambda samples, fn=Tuple(
 Pad(axis=0, pad_val=0, dtype="int64"), # word_ids
 Stack(dtype="int64"), # length
 ): fn(samples)

 # Create sampler for dataloader
 infer_sampler = paddle.io.BatchSampler(
 dataset=infer_ds, batch_size=args.batch_size, shuffle=False, drop_last=False
 )
 infer_loader = paddle.io.DataLoader(
 dataset=infer_ds, batch_sampler=infer_sampler, return_list=True, collate_fn=batchify_fn
 )

 # Define the model network
 model = BiGruCrf(args.emb_dim, args.hidden_size, len(word_vocab), len(label_vocab))

 # Load the model and start predicting
 model_dict = paddle.load(args.init_checkpoint)
 model.load_dict(model_dict)

 model.eval()
 results = []
 for batch in infer_loader:
 token_ids, length = batch
 preds = model(token_ids, length)
 result = parse_result(token_ids.numpy(), preds.numpy(), length.numpy(), word_vocab, label_vocab)
 results += result

 sent_tags = []
 for sent, tags in results:
 sent_tag = ["(%s, %s)" % (ch, tag) for ch, tag in zip(sent, tags)]
 sent_tags.append("".join(sent_tag))

 file_path = "results.txt"
 with open(file_path, "w", encoding="utf8") as fout:
 fout.write("\n".join(sent_tags))

 # Print some examples
 print("The results have been saved in the file: %s, some examples are shown below: " % file_path)
 print("\n".join(sent_tags[:10]))


if __name__ == "__main__":
 infer(args)