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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""export checkpoint file into air models"""
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import argparse
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import numpy as np
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from mindspore import Tensor, context, load_checkpoint, export
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from src.gcn import GCN
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from src.config import ConfigGCN
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parser = argparse.ArgumentParser(description="GCN export")
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument("--dataset", type=str, default="cora", choices=["cora", "citeseer"], help="Dataset.")
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parser.add_argument("--file_name", type=str, default="gcn", help="output file name.")
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parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
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parser.add_argument("--device_target", type=str, default="Ascend",
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choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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if args.device_target == "Ascend":
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context.set_context(device_id=args.device_id)
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if __name__ == "__main__":
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config = ConfigGCN()
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if args.dataset == "cora":
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input_dim = 1433
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class_num = 7
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adj = Tensor(np.zeros((2708, 2708), np.float64))
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feature = Tensor(np.zeros((2708, 1433), np.float32))
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else:
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input_dim = 3703
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class_num = 6
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adj = Tensor(np.zeros((3312, 3312), np.float64))
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feature = Tensor(np.zeros((3312, 3703), np.float32))
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gcn_net = GCN(config, input_dim, class_num)
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gcn_net.set_train(False)
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load_checkpoint(args.ckpt_file, net=gcn_net)
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gcn_net.add_flags_recursive(fp16=True)
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export(gcn_net, adj, feature, file_name=args.file_name, file_format=args.file_format)
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