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42 lines
1.8 KiB
42 lines
1.8 KiB
4 years ago
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# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""NAML export."""
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import numpy as np
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from mindspore import Tensor
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from mindspore.train.serialization import load_checkpoint, export
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from src.naml import NAML, NAMLWithLossCell
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from src.option import get_args
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if __name__ == '__main__':
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args = get_args("export")
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net = NAML(args)
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net.set_train(False)
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net_with_loss = NAMLWithLossCell(net)
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load_checkpoint(args.checkpoint_path, net_with_loss)
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news_encoder = net.news_encoder
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user_encoder = net.user_encoder
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bs = args.batch_size
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category = Tensor(np.zeros([bs, 1], np.int32))
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subcategory = Tensor(np.zeros([bs, 1], np.int32))
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title = Tensor(np.zeros([bs, args.n_words_title], np.int32))
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abstract = Tensor(np.zeros([bs, args.n_words_abstract], np.int32))
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news_input_data = [category, subcategory, title, abstract]
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export(news_encoder, *news_input_data, file_name=f"naml_news_encoder_bs_{bs}", file_format=args.file_format)
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browsed_news = Tensor(np.zeros([bs, args.n_browsed_news, args.n_filters], np.float32))
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export(user_encoder, browsed_news, file_name=f"naml_user_encoder_bs_{bs}", file_format=args.file_format)
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