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82 lines
3.4 KiB
82 lines
3.4 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.backbone.resnet import get_backbone
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devid = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=devid)
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def main(args):
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network = get_backbone(args)
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ckpt_path = args.pretrained
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if os.path.isfile(ckpt_path):
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param_dict = load_checkpoint(ckpt_path)
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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.'):
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continue
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elif key.startswith('network.'):
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param_dict_new[key[8:]] = 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-----------------------')
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else:
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print('-----------------------load model failed -----------------------')
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network.add_flags_recursive(fp16=True)
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network.set_train(False)
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input_data = np.random.uniform(low=0, high=1.0, size=(args.batch_size, 3, 112, 112)).astype(np.float32)
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tensor_input_data = Tensor(input_data)
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file_path = ckpt_path.replace('.ckpt', '_' + str(args.batch_size) + 'b.air')
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export(network, tensor_input_data, file_name=file_path, file_format='AIR')
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print('-----------------------export model success, save file:{}-----------------------'.format(file_path))
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def parse_args():
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'''parse_args'''
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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=16, help='batch size')
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parser.add_argument('--pre_bn', type=int, default=0, help='1: bn-conv-bn-conv-bn, 0: conv-bn-conv-bn')
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parser.add_argument('--inference', type=int, default=1, help='use inference backbone')
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parser.add_argument('--use_se', type=int, default=0, help='use se block or not')
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parser.add_argument('--emb_size', type=int, default=256, help='embedding size of the network')
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parser.add_argument('--act_type', type=str, default='relu', help='activation layer type')
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parser.add_argument('--backbone', type=str, default='r100', help='backbone network')
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parser.add_argument('--head', type=str, default='0', help='head type, default is 0')
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parser.add_argument('--use_drop', type=int, default=0, help='whether use dropout in network')
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args = parser.parse_args()
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return args
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if __name__ == "__main__":
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arg = parse_args()
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main(arg)
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