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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """
- export quantization aware training network to infer `AIR` backend.
- """
-
- import argparse
- import numpy as np
-
- import mindspore
- from mindspore import Tensor
- from mindspore import context
- from mindspore.compression.quant import QuantizationAwareTraining
- from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
-
- from src.config import mnist_cfg as cfg
- from src.lenet_fusion import LeNet5 as LeNet5Fusion
-
- parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
- parser.add_argument('--device_target', type=str, default="Ascend",
- choices=['Ascend', 'GPU'],
- help='device where the code will be implemented (default: Ascend)')
- parser.add_argument('--data_path', type=str, default="./MNIST_Data",
- help='path where the dataset is saved')
- parser.add_argument('--ckpt_path', type=str, default="",
- help='if mode is test, must provide path where the trained ckpt file')
- args = parser.parse_args()
-
- if __name__ == "__main__":
- context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
-
- # define fusion network
- network = LeNet5Fusion(cfg.num_classes)
- # convert fusion network to quantization aware network
- quantizer = QuantizationAwareTraining(quant_delay=0,
- bn_fold=False,
- freeze_bn=10000,
- per_channel=[True, False],
- symmetric=[True, False])
- network = quantizer.quantize(network)
- # load quantization aware network checkpoint
- param_dict = load_checkpoint(args.ckpt_path)
- load_param_into_net(network, param_dict)
-
- # export network
- inputs = Tensor(np.ones([1, 1, cfg.image_height, cfg.image_width]), mindspore.float32)
- export(network, inputs, file_name="lenet_quant", file_format='MINDIR', quant_mode='AUTO')
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