forked from mindspore-Ecosystem/mindspore
51 lines
2.4 KiB
Python
51 lines
2.4 KiB
Python
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# Copyright 2021 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 and onnx models#################
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python export.py
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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 Tensor, load_checkpoint, load_param_into_net, export, context
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from src.config import config1 as config
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from src.sknet50 import sknet50 as sknet
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parser = argparse.ArgumentParser(description='sknet export')
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parser.add_argument('--network_dataset', type=str, default="se-resnet50", choices=["se-resnet50"],
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help='network and dataset name.')
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parser.add_argument("--device_id", type=int, default=1, 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, default="/path/to/sknet-90_195.ckpt", help="Checkpoint file path.")
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parser.add_argument("--file_name", type=str, default="sknet_export", help="output file name.")
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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_format", type=str, choices=["AIR", "ONNX", "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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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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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 = sknet(config.class_num)
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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_arr = Tensor(np.zeros([args.batch_size, 3, args.height, args.width], np.float32))
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export(net, input_arr, file_name=args.file_name, file_format=args.file_format)
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