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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 for retinanet"""
- import argparse
- 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.retinanet import retinanet50, resnet50, retinanetInferWithDecoder
- from src.config import config
- from src.box_utils import default_boxes
-
-
- if __name__ == '__main__':
- parser = argparse.ArgumentParser(description='retinanet evaluation')
- parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
- parser.add_argument("--run_platform", type=str, default="Ascend", choices=("Ascend"),
- help="run platform, only support Ascend.")
- parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"], default="MINDIR", help="file format")
- parser.add_argument("--batch_size", type=int, default=1, help="batch size")
- parser.add_argument("--file_name", type=str, default="retinanet", help="output file name.")
- args_opt = parser.parse_args()
- context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.run_platform, device_id=args_opt.device_id)
-
- backbone = resnet50(config.num_classes)
- net = retinanet50(backbone, config)
- net = retinanetInferWithDecoder(net, Tensor(default_boxes), config)
- param_dict = load_checkpoint(config.checkpoint_path)
- net.init_parameters_data()
- load_param_into_net(net, param_dict)
- net.set_train(False)
- shape = [args_opt.batch_size, 3] + config.img_shape
- input_data = Tensor(np.zeros(shape), mstype.float32)
- export(net, input_data, file_name=args_opt.file_name, file_format=args_opt.file_format)
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