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