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