forked from mindspore-Ecosystem/mindspore
66 lines
2.7 KiB
Python
66 lines
2.7 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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"""export checkpoint file into models"""
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import argparse
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import numpy as np
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from mindspore import Tensor, context
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import mindspore.common.dtype as mstype
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from mindspore.train.serialization import load_checkpoint, export
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from src.vgg import vgg16
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parser = argparse.ArgumentParser(description='VGG16 export')
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument('--dataset', type=str, choices=["cifar10", "imagenet2012"], default="cifar10", help='ckpt file')
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parser.add_argument('--ckpt_file', type=str, required=True, help='vgg16 ckpt file.')
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parser.add_argument('--file_name', type=str, default='vgg16', help='vgg16 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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if args.dataset == "cifar10":
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from src.config import cifar_cfg as cfg
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else:
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from src.config import imagenet_cfg as cfg
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args.num_classes = cfg.num_classes
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args.pad_mode = cfg.pad_mode
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args.padding = cfg.padding
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args.has_bias = cfg.has_bias
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args.initialize_mode = cfg.initialize_mode
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args.batch_norm = cfg.batch_norm
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args.has_dropout = cfg.has_dropout
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args.image_size = list(map(int, cfg.image_size.split(',')))
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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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if args.dataset == "cifar10":
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net = vgg16(num_classes=args.num_classes, args=args)
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else:
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net = vgg16(args.num_classes, args, phase="test")
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net.add_flags_recursive(fp16=True)
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load_checkpoint(args.ckpt_file, net=net)
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net.set_train(False)
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input_data = Tensor(np.zeros([cfg.batch_size, 3, args.image_size[0], args.image_size[1]]), mstype.float32)
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export(net, input_data, file_name=args.file_name, file_format=args.file_format)
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