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55 lines
2.2 KiB
55 lines
2.2 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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"""Export MobilenetV2 on ImageNet"""
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import argparse
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import numpy as np
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import mindspore
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from mindspore import Tensor
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from mindspore import context
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.train.quant import quant
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from src.mobilenetV2 import mobilenetV2
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from src.config import config_ascend
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
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parser.add_argument('--device_target', type=str, default=None, help='Run device target')
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args_opt = parser.parse_args()
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if __name__ == '__main__':
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cfg = None
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if args_opt.device_target == "Ascend":
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cfg = config_ascend
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
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else:
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raise ValueError("Unsupported device target: {}.".format(args_opt.device_target))
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# define fusion network
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network = mobilenetV2(num_classes=cfg.num_classes)
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# convert fusion network to quantization aware network
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network = quant.convert_quant_network(network, bn_fold=True, per_channel=[True, False], symmetric=[True, False])
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# load checkpoint
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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load_param_into_net(network, param_dict)
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# export network
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print("============== Starting export ==============")
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inputs = Tensor(np.ones([1, 3, cfg.image_height, cfg.image_width]), mindspore.float32)
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quant.export(network, inputs, file_name="mobilenet_quant", file_format='GEIR')
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print("============== End export ==============")
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