wehub-resource-sync
58 lines
2.1 KiB
Python
58 lines
2.1 KiB
Python
# -*- coding: UTF-8 -*-
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# Copyright (c) 2021 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 argparse
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import os
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import paddle
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from data import load_vocab
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from model import BiGruCrf
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from paddle.static import InputSpec
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# fmt: off
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parser = argparse.ArgumentParser(__doc__)
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parser.add_argument("--data_dir", type=str, default=None, help="The folder where the dataset is located.")
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parser.add_argument("--params_path", type=str, default='./checkpoints/final.pdparams', help="The path of model parameter to be loaded.")
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parser.add_argument("--output_path", type=str, default='./infer_model/static_graph_params', help="The path of model parameter in static graph to be saved.")
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parser.add_argument("--emb_dim", type=int, default=128, help="The dimension in which a word is embedded.")
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parser.add_argument("--hidden_size", type=int, default=128, help="The number of hidden nodes in the GRU layer.")
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args = parser.parse_args()
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# fmt: on
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def main():
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word_vocab = load_vocab(os.path.join(args.data_dir, "word.dic"))
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label_vocab = load_vocab(os.path.join(args.data_dir, "tag.dic"))
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model = BiGruCrf(args.emb_dim, args.hidden_size, len(word_vocab), len(label_vocab))
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state_dict = paddle.load(args.params_path)
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model.set_dict(state_dict)
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model.eval()
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model = paddle.jit.to_static(
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model,
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input_spec=[
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InputSpec(shape=[None, None], dtype="int64", name="token_ids"),
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InputSpec(shape=[None], dtype="int64", name="length"),
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],
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)
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# Save in static graph model.
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paddle.jit.save(model, args.output_path)
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if __name__ == "__main__":
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main()
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