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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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"""
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##############export checkpoint file into air, onnx, mindir models#################
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python export.py
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"""
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import argparse
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import numpy as np
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from mindspore import Tensor, load_checkpoint, load_param_into_net, export, context
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from src.config import cfg_mr, cfg_subj, cfg_sst2
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from src.textcnn import TextCNN
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from src.dataset import MovieReview, SST2, Subjectivity
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parser = argparse.ArgumentParser(description='TextCNN export')
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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="checkpoint file path.")
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parser.add_argument("--file_name", type=str, default="textcnn", help="output file name.")
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parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
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parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
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help="device target")
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parser.add_argument('--dataset', type=str, default='MR', choices=['MR', 'SUBJ', 'SST2'],
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help='dataset name.')
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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if args.device_target == "Ascend":
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context.set_context(device_id=args.device_id)
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if __name__ == '__main__':
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if args.dataset == 'MR':
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cfg = cfg_mr
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instance = MovieReview(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
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elif args.dataset == 'SUBJ':
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cfg = cfg_subj
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instance = Subjectivity(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
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elif args.dataset == 'SST2':
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cfg = cfg_sst2
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instance = SST2(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
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else:
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raise ValueError("dataset is not support.")
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net = TextCNN(vocab_len=instance.get_dict_len(), word_len=cfg.word_len,
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num_classes=cfg.num_classes, vec_length=cfg.vec_length)
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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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input_arr = Tensor(np.ones([cfg.batch_size, cfg.word_len], np.int32))
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export(net, input_arr, file_name=args.file_name, file_format=args.file_format)
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