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 checkpoint file into air, mindir and onnx models"""
  import argparse
  import numpy as np
  
  from mindspore import Tensor, context, load_checkpoint, export
  
  from src.gat import GAT
  from src.config import GatConfig
  
  parser = argparse.ArgumentParser(description="GAT 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("--dataset", type=str, default="cora", choices=["cora", "citeseer"], help="Dataset.")
  parser.add_argument("--file_name", type=str, default="gat", 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, default="Ascend",
                      choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
  args = parser.parse_args()
  
  context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
  if args.device_target == "Ascend":
      context.set_context(device_id=args.device_id)
  
  if __name__ == "__main__":
  
  
      if args.dataset == "citeseer":
          feature_size = [1, 3312, 3703]
          biases_size = [1, 3312, 3312]
          num_classes = 6
      else:
          feature_size = [1, 2708, 1433]
          biases_size = [1, 2708, 2708]
          num_classes = 7
  
      hid_units = GatConfig.hid_units
      n_heads = GatConfig.n_heads
  
      feature = np.random.uniform(0.0, 1.0, size=feature_size).astype(np.float32)
      biases = np.random.uniform(0.0, 1.0, size=biases_size).astype(np.float64)
  
      feature_size = feature.shape[2]
      num_nodes = feature.shape[1]
  
      gat_net = GAT(feature_size,
                    num_classes,
                    num_nodes,
                    hid_units,
                    n_heads,
                    attn_drop=0.0,
                    ftr_drop=0.0)
  
      gat_net.set_train(False)
      load_checkpoint(args.ckpt_file, net=gat_net)
      gat_net.add_flags_recursive(fp16=True)
  
      export(gat_net, Tensor(feature), Tensor(biases), file_name=args.file_name, file_format=args.file_format)
  