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- # Copyright 2020 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.
- # ============================================================================
- import argparse
- 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.network import DenseNet121
- from src.config import config
-
- parser = argparse.ArgumentParser(description="densenet121 export")
- parser.add_argument("--device_id", type=int, default=0, help="Device id")
- parser.add_argument("--batch_size", type=int, default=32, help="batch size")
- parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
- parser.add_argument("--file_name", type=str, default="densenet121", help="output file name.")
- parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
- args = parser.parse_args()
-
- context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args.device_id)
-
- if __name__ == "__main__":
- network = DenseNet121(config.num_classes)
-
- param_dict = load_checkpoint(args.ckpt_file)
-
- 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.add_flags_recursive(fp16=True)
- network.set_train(False)
-
- shape = [int(args.batch_size), 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=args.file_name, file_format=args.file_format)
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