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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 air, onnx, mindir models
- Suggest run as python export.py --file_name [file_name] --ckpt_files [ckpt path] --file_format [file format]
- """
- import os
- import numpy as np
- from mindspore.common import dtype as mstype
- from mindspore import context, Tensor
- from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
- from src.model_utils.config import config
- from src.model_utils.moxing_adapter import moxing_wrapper
-
-
- context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
-
-
- def modelarts_pre_process():
- config.file_name = os.path.join(config.output_path, config.file_name)
-
-
- @moxing_wrapper(pre_process=modelarts_pre_process)
- def model_export():
- if config.device_target == "Ascend":
- context.set_context(device_id=config.device_id)
-
- if config.net == "sphereface20a":
- from src.network.spherenet import sphere20a
-
- network = sphere20a(config.num_classes, True)
-
- param_dict = load_checkpoint(config.ckpt_files)
-
- param_dict_new = {}
- for key, value in param_dict.items():
- if key.startswith("moments."):
- continue
- elif key.startswith("network."):
- param_dict_new[key[8:]] = value
- else:
- param_dict_new[key] = value
-
- load_param_into_net(network, param_dict_new)
-
- network.set_train(False)
-
- shape = [1, 3] + [int(config.image_size.split(",")[0]), int(config.image_size.split(",")[1])]
- input_data = Tensor(np.zeros(shape), mstype.float32)
-
- export(network, input_data, file_name=config.file_name, file_format=config.file_format)
-
-
- if __name__ == '__main__':
- model_export()
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