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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.
- # ============================================================================
- """textrcnn export ckpt file to mindir/air"""
- import os
- import argparse
- import numpy as np
- from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
-
- from src.textrcnn import textrcnn
- from src.config import textrcnn_cfg as config
-
- parser = argparse.ArgumentParser(description="textrcnn")
- parser.add_argument("--device_id", type=int, default=0, help="Device id")
- parser.add_argument("--ckpt_file", type=str, required=True, help="textrcnn ckpt file.")
- parser.add_argument("--file_name", type=str, default="textrcnn", help="textrcnn output file name.")
- parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"],
- default="MINDIR", help="file format")
- parser.add_argument("--device_target", type=str, choices=["Ascend"], default="Ascend",
- help="device target")
- args = parser.parse_args()
-
- context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
-
- if __name__ == "__main__":
- # define net
- embedding_table = np.loadtxt(os.path.join(config.preprocess_path, "weight.txt")).astype(np.float32)
-
- net = textrcnn(weight=Tensor(embedding_table), vocab_size=embedding_table.shape[0],
- cell=config.cell, batch_size=config.batch_size)
-
- # load checkpoint
- param_dict = load_checkpoint(args.ckpt_file)
- load_param_into_net(net, param_dict)
- net.set_train(False)
-
- image = Tensor(np.ones([config.batch_size, 50], np.int32))
- export(net, image, file_name=args.file_name, file_format=args.file_format)
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