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68 lines
3.3 KiB
68 lines
3.3 KiB
# 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 ckpt to model"""
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import argparse
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import numpy as np
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from mindspore import context, Tensor
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from mindspore.train.serialization import export, load_checkpoint
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from src.bgcf import BGCF
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from src.callback import ForwardBGCF
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parser = argparse.ArgumentParser(description="bgcf 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("--file_name", type=str, default="bgcf", 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, choices=["Ascend", "GPU", "CPU"], default="Ascend",
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help="device target")
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parser.add_argument("--input_dim", type=int, choices=[64, 128], default=64, help="embedding dimension")
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parser.add_argument("--embedded_dimension", type=int, default=64, help="output embedding dimension")
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parser.add_argument("--row_neighs", type=int, default=40, help="num of sampling neighbors in raw graph")
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parser.add_argument("--gnew_neighs", type=int, default=20, help="num of sampling neighbors in sample graph")
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parser.add_argument("--activation", type=str, default="tanh", choices=["relu", "tanh"], help="activation function")
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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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num_user, num_item = 7068, 3570
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network = BGCF([args.input_dim, num_user, num_item],
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args.embedded_dimension,
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args.activation,
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[0.0, 0.0, 0.0],
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num_user,
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num_item,
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args.input_dim)
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load_checkpoint(args.ckpt_file, net=network)
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forward_net = ForwardBGCF(network)
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users = Tensor(np.zeros([num_user,]).astype(np.int32))
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items = Tensor(np.zeros([num_item,]).astype(np.int32))
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neg_items = Tensor(np.zeros([num_item, 1]).astype(np.int32))
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u_test_neighs = Tensor(np.zeros([num_user, args.row_neighs]).astype(np.int32))
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u_test_gnew_neighs = Tensor(np.zeros([num_user, args.gnew_neighs]).astype(np.int32))
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i_test_neighs = Tensor(np.zeros([num_item, args.row_neighs]).astype(np.int32))
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i_test_gnew_neighs = Tensor(np.zeros([num_item, args.gnew_neighs]).astype(np.int32))
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input_data = [users, items, neg_items, u_test_neighs, u_test_gnew_neighs, i_test_neighs, i_test_gnew_neighs]
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export(forward_net, *input_data, file_name=args.file_name, file_format=args.file_format)
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