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.
  # ============================================================================
  """export ckpt to model"""
  import argparse
  import numpy as np
  
  from mindspore import context, Tensor
  from mindspore.train.serialization import export, load_checkpoint
  
  from src.bgcf import BGCF
  from src.callback import ForwardBGCF
  
  parser = argparse.ArgumentParser(description="bgcf export")
  parser.add_argument("--device_id", type=int, default=0, help="Device id")
  parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
  parser.add_argument("--file_name", type=str, default="bgcf", help="output file name.")
  parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
  parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
                      help="device target")
  parser.add_argument("--input_dim", type=int, choices=[64, 128], default=64, help="embedding dimension")
  parser.add_argument("--embedded_dimension", type=int, default=64, help="output embedding dimension")
  parser.add_argument("--row_neighs", type=int, default=40, help="num of sampling neighbors in raw graph")
  parser.add_argument("--gnew_neighs", type=int, default=20, help="num of sampling neighbors in sample graph")
  parser.add_argument("--activation", type=str, default="tanh", choices=["relu", "tanh"], help="activation function")
  args = parser.parse_args()
  
  context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
  
  if __name__ == "__main__":
      num_user, num_item = 7068, 3570
  
      network = BGCF([args.input_dim, num_user, num_item],
                     args.embedded_dimension,
                     args.activation,
                     [0.0, 0.0, 0.0],
                     num_user,
                     num_item,
                     args.input_dim)
  
      load_checkpoint(args.ckpt_file, net=network)
  
      forward_net = ForwardBGCF(network)
  
      users = Tensor(np.zeros([num_user,]).astype(np.int32))
      items = Tensor(np.zeros([num_item,]).astype(np.int32))
      neg_items = Tensor(np.zeros([num_item, 1]).astype(np.int32))
      u_test_neighs = Tensor(np.zeros([num_user, args.row_neighs]).astype(np.int32))
      u_test_gnew_neighs = Tensor(np.zeros([num_user, args.gnew_neighs]).astype(np.int32))
      i_test_neighs = Tensor(np.zeros([num_item, args.row_neighs]).astype(np.int32))
      i_test_gnew_neighs = Tensor(np.zeros([num_item, args.gnew_neighs]).astype(np.int32))
  
      input_data = [users, items, neg_items, u_test_neighs, u_test_gnew_neighs, i_test_neighs, i_test_gnew_neighs]
      export(forward_net, *input_data, file_name=args.file_name, file_format=args.file_format)
  