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- # Copyright 2021 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
-
- import mindspore.common.dtype as mstype
- from mindspore import Tensor, context, load_checkpoint, export
- from src.config import bertcfg as bert_net_cfg
- from src.models.roberta_one_sent_classification_en import RobertaOneSentClassificationEn
-
- parser = argparse.ArgumentParser(description="Bert export")
- parser.add_argument("--device_id", type=int, default=0, help="Device id")
- parser.add_argument("--batch_size", type=int, default=1, help="batch size")
- parser.add_argument(
- "--ckpt_file",
- type=str,
- required=True,
- help="Senta ckpt file.")
- parser.add_argument(
- "--file_name",
- type=str,
- default="Senta",
- help="Senta output air name.")
- parser.add_argument(
- "--file_format",
- type=str,
- choices=[
- "AIR",
- "ONNX",
- "MINDIR"],
- default="MINDIR",
- 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)
- if args.device_target == "Ascend":
- context.set_context(device_id=args.device_id)
-
-
- if __name__ == "__main__":
-
- net = RobertaOneSentClassificationEn(bert_net_cfg, False)
- load_checkpoint(args.ckpt_file, net=net)
- net.set_train(False)
-
- src_ids = Tensor(
- np.zeros([args.batch_size, bert_net_cfg.seq_length]), mstype.int32)
- sent_ids = Tensor(
- np.zeros([args.batch_size, bert_net_cfg.seq_length]), mstype.int32)
- mask_ids = Tensor(
- np.zeros([args.batch_size, bert_net_cfg.seq_length]), mstype.int32)
-
- input_data = [src_ids, sent_ids, mask_ids]
-
- export(
- net.bert,
- *input_data,
- file_name=args.file_name,
- file_format=args.file_format)
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