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40 lines
1.5 KiB
40 lines
1.5 KiB
# Copyright 2020 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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"""export checkpoint file into air models"""
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import numpy as np
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from mindspore import Tensor, context
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from mindspore.train.serialization import load_param_into_net, export
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from src.transformer_model import TransformerModel
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from src.eval_config import cfg, transformer_net_cfg
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from eval import load_weights
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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if __name__ == '__main__':
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tfm_model = TransformerModel(config=transformer_net_cfg, is_training=False, use_one_hot_embeddings=False)
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parameter_dict = load_weights(cfg.model_file)
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load_param_into_net(tfm_model, parameter_dict)
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source_ids = Tensor(np.ones((1, 128)).astype(np.int32))
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source_mask = Tensor(np.ones((1, 128)).astype(np.int32))
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dec_len = transformer_net_cfg.max_decode_length
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export(tfm_model, source_ids, source_mask, file_name="len" + str(dec_len) + ".air", file_format="AIR")
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