forked from huawei/mindspore2022
43 lines
1.8 KiB
Python
43 lines
1.8 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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resnext export mindir.
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"""
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import numpy as np
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from mindspore.common import dtype as mstype
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from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
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from src.model_utils.config import config
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from src.image_classification import get_network
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from src.utils.auto_mixed_precision import auto_mixed_precision
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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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if __name__ == '__main__':
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network = get_network(network=config.network, num_classes=config.num_classes, platform=config.device_target)
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param_dict = load_checkpoint(config.checkpoint_file_path)
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load_param_into_net(network, param_dict)
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if config.device_target == "Ascend":
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network.to_float(mstype.float16)
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else:
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auto_mixed_precision(network)
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network.set_train(False)
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input_shp = [config.batch_size, 3, config.height, config.width]
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input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
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export(network, input_array, file_name=config.file_name, file_format=config.file_format)
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