code: # 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 checkpoint file into air and onnx models#################
  python export.py
  """
  import argparse
  import numpy as np
  
  import mindspore as ms
  from mindspore import Tensor
  from mindspore import context
  from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
  
  from src.config import alexnet_cifar10_cfg, alexnet_imagenet_cfg
  from src.alexnet import AlexNet
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser(description='Classification')
      parser.add_argument('--dataset_name', type=str, default='cifar10', choices=['imagenet', 'cifar10'],
                          help='please choose dataset: imagenet or cifar10.')
      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('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\
                                  path where the trained ckpt file')
      args_opt = parser.parse_args()
      context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
  
      if args_opt.dataset_name == 'cifar10':
          cfg = alexnet_cifar10_cfg
      elif args_opt.dataset_name == 'imagenet':
          cfg = alexnet_imagenet_cfg
      else:
          raise ValueError("dataset is not support.")
  
      net = AlexNet(num_classes=cfg.num_classes)
  
      param_dict = load_checkpoint(args_opt.ckpt_path)
      load_param_into_net(net, param_dict)
  
      input_arr = Tensor(np.random.uniform(0.0, 1.0, size=[1, 3, cfg.image_height, cfg.image_width]), ms.float32)
      export(net, input_arr, file_name=cfg.air_name, file_format="AIR")
  
apis: numpy.random.uniform