forked from mindspore-Ecosystem/mindspore
60 lines
2.8 KiB
Python
Executable File
60 lines
2.8 KiB
Python
Executable File
# 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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"""
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##############export checkpoint file into air , mindir and onnx models#################
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python export.py --net squeezenet --dataset cifar10 --checkpoint_path squeezenet_cifar10-120_1562.ckpt
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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, load_checkpoint, load_param_into_net, export
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parser = argparse.ArgumentParser(description='checkpoint 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('--width', type=int, default=227, help='input width')
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parser.add_argument('--height', type=int, default=227, help='input height')
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parser.add_argument('--net', type=str, default='squeezenet', choices=['squeezenet', 'squeezenet_residual'],
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help='Model.')
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parser.add_argument('--dataset', type=str, default='cifar10', choices=['cifar10', 'imagenet'], help='Dataset.')
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parser.add_argument("--file_name", type=str, default="squeezenet", help="output file name.")
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parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"], default="AIR", help="file format")
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parser.add_argument("--device_target", type=str, default="Ascend",
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choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
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args = parser.parse_args()
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if args.net == "squeezenet":
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from src.squeezenet import SqueezeNet as squeezenet
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else:
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from src.squeezenet import SqueezeNet_Residual as squeezenet
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if args.dataset == "cifar10":
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num_classes = 10
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else:
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num_classes = 1000
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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if args.device_target == "Ascend":
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context.set_context(device_id=args.device_id)
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if __name__ == '__main__':
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net = squeezenet(num_classes=num_classes)
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param_dict = load_checkpoint(args.ckpt_file)
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load_param_into_net(net, param_dict)
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input_data = Tensor(np.zeros([args.batch_size, 3, args.height, args.width], np.float32))
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export(net, input_data, file_name=args.file_name, file_format=args.file_format)
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