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# 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 import context, Tensor
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from mindspore.train.serialization import export, load_checkpoint
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from src.mobilenet_v1 import mobilenet_v1 as mobilenet
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parser = argparse.ArgumentParser(description="mobilenetv1 export")
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument("--dataset", type=str, default="imagenet2012", help="Dataset, either cifar10 or imagenet2012")
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parser.add_argument('--width', type=int, default=224, help='input width')
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parser.add_argument('--height', type=int, default=224, help='input height')
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parser.add_argument("--file_name", type=str, default="mobilenetv1", 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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parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
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help="device target")
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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if args.dataset == "cifar10":
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from src.config import config1 as config
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else:
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from src.config import config2 as config
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if __name__ == "__main__":
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target = args.device_target
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if target != "GPU":
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context.set_context(device_id=args.device_id)
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network = mobilenet(class_num=config.class_num)
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param_dict = load_checkpoint(args.ckpt_file, net=network)
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network.set_train(False)
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input_data = Tensor(np.zeros([config.batch_size, 3, args.height, args.width]).astype(np.float32))
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export(network, input_data, file_name=args.file_name, file_format=args.file_format)
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