wehub-resource-sync
58 lines
1.7 KiB
Python
58 lines
1.7 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 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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def main():
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args = parse_args()
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_, src_vocab_size, tgt_vocab_size, bos_id, eos_id = create_infer_loader(args)
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# Build model and load trained parameters
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model = 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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# Load the trained model
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model.set_state_dict(paddle.load(args.init_from_ckpt))
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# Wwitch to eval model
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model.eval()
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# Convert to static graph with specific input description
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model = paddle.jit.to_static(
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model,
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input_spec=[
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paddle.static.InputSpec(shape=[None, None], dtype="int64"), # src
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paddle.static.InputSpec(shape=[None], dtype="int64"), # src length
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],
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)
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# Save converted static graph model
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paddle.jit.save(model, args.export_path)
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if __name__ == "__main__":
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main()
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