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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 os
-
- import numpy as np
- from mindspore import context, Tensor, export
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
- from src.model import CTCModel
- from src.model_utils.config import config
- from src.model_utils.device_adapter import get_device_id
- from src.model_utils.moxing_adapter import moxing_wrapper
-
- if config.enable_modelarts:
- device_id = get_device_id()
- else:
- device_id = config.device_id
- context.set_context(
- mode=context.GRAPH_MODE,
- save_graphs=False,
- device_target=config.device_target,
- device_id=device_id
- )
-
-
- def modelarts_pre_process():
- '''modelarts pre process function.'''
- config.file_name = os.path.join(config.local_train_url, config.file_name)
- config.checkpoint_path = config.local_checkpoint_path
-
-
- @moxing_wrapper(pre_process=modelarts_pre_process)
- def model_export():
- '''export mindir'''
- config.batch_size = 1
- ckpt_file = config.checkpoint_path
- param_dict = load_checkpoint(ckpt_file)
- net = CTCModel(input_size=config.feature_dim, batch_size=config.test_batch_size, hidden_size=config.hidden_size,
- num_class=config.n_class, num_layers=config.n_layer)
- load_param_into_net(net, param_dict)
- net.set_train(False)
- feature = Tensor(np.zeros([1, config.max_sequence_length, config.feature_dim]).astype(np.float32))
- masks = Tensor(np.zeros([1, config.max_sequence_length, 2 * config.hidden_size]).astype(np.float32))
- export(net, feature, masks, file_name=config.file_name, file_format=config.file_format)
-
-
- if __name__ == '__main__':
- model_export()
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