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63 lines
2.4 KiB
63 lines
2.4 KiB
# 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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"""Convert ckpt to air."""
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import os
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import argparse
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import numpy as np
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from mindspore import context
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from mindspore import Tensor
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from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
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from src.centerface import CenterfaceMobilev2
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
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def save_air():
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"""Save air file"""
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print('============= centerface start save air ==================')
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parser = argparse.ArgumentParser(description='Convert ckpt to air')
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parser.add_argument('--pretrained', type=str, default='', help='pretrained model to load')
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parser.add_argument('--batch_size', type=int, default=8, help='batch size')
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args = parser.parse_args()
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network = CenterfaceMobilev2()
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if os.path.isfile(args.pretrained):
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param_dict = load_checkpoint(args.pretrained)
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param_dict_new = {}
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for key, values in param_dict.items():
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if key.startswith('moments.') or key.startswith('moment1.') or key.startswith('moment2.'):
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continue
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elif key.startswith('centerface_network.'):
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param_dict_new[key[19:]] = values
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else:
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param_dict_new[key] = values
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load_param_into_net(network, param_dict_new)
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print('load model {} success'.format(args.pretrained))
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input_data = np.random.uniform(low=0, high=1.0, size=(args.batch_size, 3, 832, 832)).astype(np.float32)
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tensor_input_data = Tensor(input_data)
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export(network, tensor_input_data,
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file_name=args.pretrained.replace('.ckpt', '_' + str(args.batch_size) + 'b.air'), file_format='AIR')
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print("export model success.")
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if __name__ == "__main__":
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save_air()
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