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- # 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.
- # ============================================================================
- """ncf export file"""
- import os
- import numpy as np
-
- from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
-
- import src.constants as rconst
- from model_utils.config import config
- from model_utils.moxing_adapter import moxing_wrapper
- from ncf import NCFModel, PredictWithSigmoid
-
-
- def modelarts_pre_process():
- '''modelarts pre process function.'''
- config.file_name = os.path.join(config.output_path, config.file_name)
-
-
- @moxing_wrapper(pre_process=modelarts_pre_process)
- def run_export():
- '''run export.'''
- context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
- if config.device_target == "Ascend":
- context.set_context(device_id=config.device_id)
-
- topk = rconst.TOP_K
- num_eval_neg = rconst.NUM_EVAL_NEGATIVES
-
- if config.dataset == "ml-1m":
- num_eval_users = 6040
- num_eval_items = 3706
- elif config.dataset == "ml-20m":
- num_eval_users = 138493
- num_eval_items = 26744
- else:
- raise ValueError("not supported dataset")
-
- ncf_net = NCFModel(num_users=num_eval_users,
- num_items=num_eval_items,
- num_factors=config.num_factors,
- model_layers=config.layers,
- mf_regularization=0,
- mlp_reg_layers=[0.0, 0.0, 0.0, 0.0],
- mf_dim=16)
-
- param_dict = load_checkpoint(config.ckpt_file)
- load_param_into_net(ncf_net, param_dict)
-
- network = PredictWithSigmoid(ncf_net, topk, num_eval_neg)
-
- users = Tensor(np.zeros([config.eval_batch_size, 1]).astype(np.int32))
- items = Tensor(np.zeros([config.eval_batch_size, 1]).astype(np.int32))
- masks = Tensor(np.zeros([config.eval_batch_size, 1]).astype(np.float32))
-
- input_data = [users, items, masks]
- export(network, *input_data, file_name=config.file_name, file_format=config.file_format)
-
-
- if __name__ == "__main__":
- run_export()
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