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.
  # ============================================================================
  """
  Function:
      test network
  Usage:
      python test_predict_save_model.py --path ./
  """
  
  import argparse
  import os
  import numpy as np
  
  import mindspore.context as context
  import mindspore.nn as nn
  import mindspore.ops.operations as P
  from mindspore.common.tensor import Tensor
  from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
  
  
  class LeNet(nn.Cell):
      def __init__(self):
          super(LeNet, self).__init__()
          self.relu = P.ReLU()
          self.batch_size = 32
  
          self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
          self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
          self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
          self.reshape = P.Reshape()
          self.fc1 = nn.Dense(400, 120)
          self.fc2 = nn.Dense(120, 84)
          self.fc3 = nn.Dense(84, 10)
  
      def construct(self, input_x):
          output = self.conv1(input_x)
          output = self.relu(output)
          output = self.pool(output)
          output = self.conv2(output)
          output = self.relu(output)
          output = self.pool(output)
          output = self.reshape(output, (self.batch_size, -1))
          output = self.fc1(output)
          output = self.relu(output)
          output = self.fc2(output)
          output = self.relu(output)
          output = self.fc3(output)
          return output
  
  
  parser = argparse.ArgumentParser(description='MindSpore Model Save')
  parser.add_argument('--path', default='./lenet_model.ms', type=str, help='model save path')
  
  if __name__ == '__main__':
      context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
  
      print("test lenet predict start")
      seed = 0
      np.random.seed(seed)
      batch = 1
      channel = 1
      input_h = 32
      input_w = 32
      origin_data = np.random.uniform(low=0, high=255, size=(batch, channel, input_h, input_w)).astype(np.float32)
      origin_data.tofile("lenet_input_data.bin")
  
      input_data = Tensor(origin_data)
      print(input_data.asnumpy())
      net = LeNet()
      ckpt_file_path = "./tests/ut/python/predict/checkpoint_lenet.ckpt"
      predict_args = parser.parse_args()
      model_path_name = predict_args.path
  
      is_ckpt_exist = os.path.exists(ckpt_file_path)
      if is_ckpt_exist:
          param_dict = load_checkpoint(ckpoint_file_name=ckpt_file_path)
          load_param_into_net(net, param_dict)
          export(net, input_data, file_name=model_path_name, file_format='LITE')
          print("test lenet predict success.")
      else:
          print("checkpoint file is not exist.")
  