forked from mindspore-Ecosystem/mindspore
58 lines
2.3 KiB
Python
58 lines
2.3 KiB
Python
# Copyright 2020 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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import argparse
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import numpy as np
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from mindspore.common import dtype as mstype
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from mindspore import context, Tensor
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from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
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from src.network import DenseNet121
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from src.config import config
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parser = argparse.ArgumentParser(description="densenet121 export")
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--batch_size", type=int, default=32, help="batch size")
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parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument("--file_name", type=str, default="densenet121", help="output file name.")
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parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args.device_id)
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if __name__ == "__main__":
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network = DenseNet121(config.num_classes)
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param_dict = load_checkpoint(args.ckpt_file)
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param_dict_new = {}
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for key, value in param_dict.items():
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if key.startswith("moments."):
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continue
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elif key.startswith("network."):
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param_dict_new[key[8:]] = value
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else:
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param_dict_new[key] = value
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load_param_into_net(network, param_dict_new)
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network.add_flags_recursive(fp16=True)
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network.set_train(False)
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shape = [int(args.batch_size), 3] + [int(config.image_size.split(",")[0]), int(config.image_size.split(",")[1])]
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input_data = Tensor(np.zeros(shape), mstype.float32)
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export(network, input_data, file_name=args.file_name, file_format=args.file_format)
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