wehub-resource-sync
62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
# 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 model import TextCNNModel
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from paddlenlp.data import Vocab
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# yapf: disable
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parser = argparse.ArgumentParser(__doc__)
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parser.add_argument("--vocab_path", type=str, default="./robot_chat_word_dict.txt", help="The path to vocabulary.")
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parser.add_argument('--device', choices=['cpu', 'gpu', 'xpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
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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='./static_graph_params', help="The path of model parameter in static graph to be saved.")
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args = parser.parse_args()
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# yapf: enable
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def main():
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# Load vocab.
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if not os.path.exists(args.vocab_path):
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raise RuntimeError("The vocab_path can not be found in the path %s" % args.vocab_path)
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vocab = Vocab.load_vocabulary(args.vocab_path)
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label_map = {0: "negative", 1: "neutral", 2: "positive"}
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# Construct the network.
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vocab_size = len(vocab)
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num_classes = len(label_map)
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pad_token_id = vocab.to_indices("[PAD]")
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model = TextCNNModel(vocab_size, num_classes, padding_idx=pad_token_id, ngram_filter_sizes=(1, 2, 3))
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# Load model parameters.
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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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inputs = [paddle.static.InputSpec(shape=[None, None], dtype="int64")]
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model = paddle.jit.to_static(model, input_spec=inputs)
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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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