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74 lines
3.2 KiB
74 lines
3.2 KiB
# Copyright 2020-2021 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 mindir model#################
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python export.py
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"""
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import argparse
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import os
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import numpy as np
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from mindspore import Tensor, context
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from mindspore import export, load_checkpoint, load_param_into_net
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from src.config import lstm_cfg, lstm_cfg_ascend
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from src.lstm import SentimentNet
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='MindSpore LSTM Exporter')
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parser.add_argument('--preprocess_path', type=str, default='./preprocess',
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help='path where the pre-process data is stored.')
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parser.add_argument('--ckpt_file', type=str, required=True, help='lstm ckpt file.')
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--file_name", type=str, default="lstm", help="output file name.")
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parser.add_argument('--file_format', type=str, choices=["AIR", "MINDIR"], default='AIR', help='file format')
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parser.add_argument('--device_target', type=str, default="Ascend", choices=['GPU', 'CPU', 'Ascend'],
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help='the target device to run, support "GPU", "CPU". Default: "Ascend".')
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args = parser.parse_args()
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context.set_context(
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mode=context.GRAPH_MODE,
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save_graphs=False,
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device_target=args.device_target,
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device_id=args.device_id)
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if args.device_target == 'Ascend':
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cfg = lstm_cfg_ascend
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else:
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cfg = lstm_cfg
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embedding_table = np.loadtxt(os.path.join(args.preprocess_path, "weight.txt")).astype(np.float32)
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if args.device_target == 'Ascend':
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pad_num = int(np.ceil(cfg.embed_size / 16) * 16 - cfg.embed_size)
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if pad_num > 0:
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embedding_table = np.pad(embedding_table, [(0, 0), (0, pad_num)], 'constant')
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cfg.embed_size = int(np.ceil(cfg.embed_size / 16) * 16)
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network = SentimentNet(vocab_size=embedding_table.shape[0],
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embed_size=cfg.embed_size,
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num_hiddens=cfg.num_hiddens,
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num_layers=cfg.num_layers,
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bidirectional=cfg.bidirectional,
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num_classes=cfg.num_classes,
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weight=Tensor(embedding_table),
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batch_size=cfg.batch_size)
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param_dict = load_checkpoint(args.ckpt_file)
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load_param_into_net(network, param_dict)
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input_arr = Tensor(np.random.uniform(0.0, 1e5, size=[cfg.batch_size, 500]).astype(np.int32))
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export(network, input_arr, file_name=args.file_name, file_format=args.file_format)
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