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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """ export checkpoint file into models"""
-
- import argparse
- import numpy as np
-
- from mindspore import Tensor, context
- from mindspore.train.serialization import load_param_into_net, export
-
- from src.transformer_model import TransformerModel
- from src.eval_config import cfg, transformer_net_cfg
- from eval import load_weights
-
- parser = argparse.ArgumentParser(description='transformer export')
- parser.add_argument("--device_id", type=int, default=0, help="Device id")
- parser.add_argument("--file_name", type=str, default="transformer", help="output file name.")
- parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
- parser.add_argument("--device_target", type=str, default="Ascend",
- choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
- args = parser.parse_args()
-
- context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
-
- if __name__ == '__main__':
- tfm_model = TransformerModel(config=transformer_net_cfg, is_training=False, use_one_hot_embeddings=False)
-
- parameter_dict = load_weights(cfg.model_file)
- load_param_into_net(tfm_model, parameter_dict)
-
- source_ids = Tensor(np.ones((transformer_net_cfg.batch_size, transformer_net_cfg.seq_length)).astype(np.int32))
- source_mask = Tensor(np.ones((transformer_net_cfg.batch_size, transformer_net_cfg.seq_length)).astype(np.int32))
-
- export(tfm_model, source_ids, source_mask, file_name=args.file_name, file_format=args.file_format)
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