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54 lines
2.5 KiB
54 lines
2.5 KiB
4 years ago
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# Copyright 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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"""Export Resnet50 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, context, load_checkpoint, load_param_into_net, export
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from mindspore.compression.quant import QuantizationAwareTraining
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from models.resnet_quant_manual import resnet50_quant
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from src.config import config_quant
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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('--file_format', type=str, choices=["AIR", "MINDIR"], default="MINDIR", help="file format")
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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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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, save_graphs=False)
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# define fusion network
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network = resnet50_quant(class_num=config_quant.class_num)
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# convert fusion network to quantization aware network
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quantizer = QuantizationAwareTraining(bn_fold=True,
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per_channel=[True, False],
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symmetric=[True, False])
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network = quantizer.quantize(network)
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# load checkpoint
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if args_opt.checkpoint_path:
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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not_load_param = load_param_into_net(network, param_dict)
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if not_load_param:
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raise ValueError("Load param into network fail!")
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# export network
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print("============== Starting export ==============")
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inputs = Tensor(np.ones([1, 3, 224, 224]), mindspore.float32)
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export(network, inputs, file_name="resnet50_quant", file_format=args_opt.file_format,
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quant_mode='MANUAL', mean=0., std_dev=48.106)
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print("============== End export ==============")
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