forked from huawei/mindspore2022
53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
# Copyright 2020-2021 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 os
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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.model_utils.config import config
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from src.model_utils.moxing_adapter import moxing_wrapper
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from src.vit import get_network
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context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
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if config.device_target == "Ascend":
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context.set_context(device_id=config.device_id)
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def modelarts_pre_process():
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'''modelarts pre process function.'''
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config.file_name = os.path.join(config.output_path, config.file_name)
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@moxing_wrapper(pre_process=modelarts_pre_process)
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def run_export():
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"""run export."""
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net = get_network(backbone_name=config.backbone, args=config)
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assert config.pretrained is not None, "checkpoint_path is None."
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param_dict = load_checkpoint(config.pretrained)
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load_param_into_net(net, param_dict)
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config.height = config.train_image_size
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config.width = config.train_image_size
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input_arr = Tensor(np.zeros([config.batch_size, 3, config.height, config.width], np.float32))
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export(net, input_arr, file_name=config.file_name, file_format=config.file_format)
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if __name__ == '__main__':
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run_export()
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