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- # copyright 2023 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 for retinanet"""
- import os
- import numpy as np
- import mindspore.common.dtype as mstype
- from mindspore import context, Tensor
- from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
- from src.YOLOF.yolof import yolofWithInfer, yolof
- # from src.YOLOF.anchor_generator import AnchorGenerator
- from src.model_utils.config import config
-
- from src.model_utils.moxing_adapter import moxing_wrapper
-
-
- 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():
- context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, device_id=config.device_id)
- net = yolofWithInfer(yolof, config)
- param_dict = load_checkpoint(config.checkpoint_path)
- net.init_parameters_data()
- load_param_into_net(net, param_dict)
- net.set_train(False)
- pad_height = (np.ceil(config.img_height / 32) * 32).astype(np.int32)
- pad_width = (np.ceil(config.img_width / 32) * 32).astype(np.int32)
- shape = [config.export_batch_size, 3] + [pad_height, pad_width]
- input_data = Tensor(np.zeros(shape), mstype.float32)
- export(net, input_data, file_name=config.file_name, file_format=config.file_format)
-
-
- if __name__ == '__main__':
- model_export()
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