forked from opengaussexamples/examples
98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
# Copyright (c) 2022 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 model import ElectraForBinaryTokenClassification, ElectraForSPO
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from paddlenlp.transformers import ElectraForSequenceClassification
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NUM_CLASSES = {
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"CHIP-CDN-2C": 2,
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"CHIP-STS": 2,
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"CHIP-CTC": 44,
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"KUAKE-QQR": 3,
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"KUAKE-QTR": 4,
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"KUAKE-QIC": 11,
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"CMeEE": [33, 5],
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"CMeIE": 44,
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}
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def parse_args():
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parser = argparse.ArgumentParser()
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#parser.add_argument("--train_dataset",default="CHIP-CDN-2C", required=True, type=str, help="The name of dataset used for training.")
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parser.add_argument("--train_dataset", default="CHIP-STS", type=str,help="The name of dataset used for training.")
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'''
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parser.add_argument(
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"--params_path",
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type=str,
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required=True,
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default="./checkpoint/model_1/",
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help="The path to model parameters to be loaded.",
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)
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'''
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parser.add_argument(
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"--params_path",
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type=str,
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default="./checkpoint/model_1500/",
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help="The path to model parameters to be loaded.",
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)
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parser.add_argument(
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"--output_path", type=str, default="./export2", help="The path of model parameter in static graph to be saved."
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)
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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# Load the model parameters.
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if args.train_dataset not in NUM_CLASSES:
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raise ValueError(f"Please modify the code to fit {args.dataset}")
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if args.train_dataset == "CMeEE":
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model = ElectraForBinaryTokenClassification.from_pretrained(
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args.params_path,
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num_classes_oth=NUM_CLASSES[args.train_dataset][0],
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num_classes_sym=NUM_CLASSES[args.train_dataset][1],
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)
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elif args.train_dataset == "CMeIE":
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model = ElectraForSPO.from_pretrained(args.params_path, num_labels=NUM_CLASSES[args.train_dataset])
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else:
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model = ElectraForSequenceClassification.from_pretrained(
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args.params_path, num_labels=NUM_CLASSES[args.train_dataset]
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)
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model.eval()
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# Convert to static graph with specific input description:
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# input_ids, token_type_ids
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input_spec = [
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paddle.static.InputSpec(shape=[None, None], dtype="int64"),
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paddle.static.InputSpec(shape=[None, None], dtype="int64"),
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]
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model = paddle.jit.to_static(model, input_spec=input_spec)
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# Save in static graph model.
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save_path = os.path.join(args.output_path, "inference")
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paddle.jit.save(model, save_path)
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if __name__ == "__main__":
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main()
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