text: # 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.
  # ============================================================================
  """
  ######################## eval alexnet example ########################
  eval alexnet according to model file:
  python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt
  """
  
  import ast
  import argparse
  from src.config import alexnet_cifar10_cfg, alexnet_imagenet_cfg
  from src.dataset import create_dataset_cifar10, create_dataset_imagenet
  from src.alexnet import AlexNet
  import mindspore.nn as nn
  from mindspore import context
  from mindspore.train.serialization import load_checkpoint, load_param_into_net
  from mindspore.train import Model
  from mindspore.nn.metrics import Accuracy
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser(description='MindSpore AlexNet Example')
      parser.add_argument('--dataset_name', type=str, default='cifar10', choices=['imagenet', 'cifar10'],
                          help='dataset name.')
      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="./", help='path where the dataset is saved')
      parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\
                              path where the trained ckpt file')
      parser.add_argument('--dataset_sink_mode', type=ast.literal_eval,
                          default=True, help='dataset_sink_mode is False or True')
      parser.add_argument('--device_id', type=int, default=0, help='device id of GPU or Ascend. (Default: 0)')
      args = parser.parse_args()
  
      context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
  
      print("============== Starting Testing ==============")
  
      if args.dataset_name == 'cifar10':
          cfg = alexnet_cifar10_cfg
          network = AlexNet(cfg.num_classes)
          loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
          opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
          ds_eval = create_dataset_cifar10(args.data_path, cfg.batch_size, status="test", target=args.device_target)
  
          param_dict = load_checkpoint(args.ckpt_path)
          print("load checkpoint from [{}].".format(args.ckpt_path))
          load_param_into_net(network, param_dict)
          network.set_train(False)
  
          model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()})
  
      elif args.dataset_name == 'imagenet':
          cfg = alexnet_imagenet_cfg
          network = AlexNet(cfg.num_classes)
          loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
          ds_eval = create_dataset_imagenet(args.data_path, cfg.batch_size, training=False)
  
          param_dict = load_checkpoint(args.ckpt_path)
          print("load checkpoint from [{}].".format(args.ckpt_path))
          load_param_into_net(network, param_dict)
          network.set_train(False)
  
          model = Model(network, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
  
      else:
          raise ValueError("Unsupport dataset.")
  
      if ds_eval.get_dataset_size() == 0:
          raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")
  
      result = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
      print("result : {}".format(result))
  