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71 lines
2.9 KiB
71 lines
2.9 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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"""ncf export file"""
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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, load_param_into_net, export
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import src.constants as rconst
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from src.config import cfg
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from ncf import NCFModel, PredictWithSigmoid
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parser = argparse.ArgumentParser(description='ncf 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="ml-1m", choices=["ml-1m", "ml-20m"], help="Dataset.")
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parser.add_argument("--file_name", type=str, default="ncf", 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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topk = rconst.TOP_K
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num_eval_neg = rconst.NUM_EVAL_NEGATIVES
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if args.dataset == "ml-1m":
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num_eval_users = 6040
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num_eval_items = 3706
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elif args.dataset == "ml-20m":
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num_eval_users = 138493
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num_eval_items = 26744
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else:
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raise ValueError("not supported dataset")
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ncf_net = NCFModel(num_users=num_eval_users,
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num_items=num_eval_items,
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num_factors=cfg.num_factors,
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model_layers=cfg.layers,
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mf_regularization=0,
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mlp_reg_layers=[0.0, 0.0, 0.0, 0.0],
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mf_dim=16)
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param_dict = load_checkpoint(args.ckpt_file)
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load_param_into_net(ncf_net, param_dict)
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network = PredictWithSigmoid(ncf_net, topk, num_eval_neg)
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users = Tensor(np.zeros([cfg.eval_batch_size, 1]).astype(np.int32))
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items = Tensor(np.zeros([cfg.eval_batch_size, 1]).astype(np.int32))
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masks = Tensor(np.zeros([cfg.eval_batch_size, 1]).astype(np.float32))
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input_data = [users, items, masks]
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export(network, *input_data, file_name=args.file_name, file_format=args.file_format)
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