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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.
- # ============================================================================
- """ training_and_evaluating """
-
- import os
- import sys
- from mindspore import Model, context
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
- from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
- from src.callbacks import LossCallBack
- from src.datasets import create_dataset, compute_emb_dim
- from src.metrics import AUCMetric
- from src.config import WideDeepConfig
- sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-
- def get_WideDeep_net(config):
- """
- Get network of wide&deep model.
- """
- WideDeep_net = WideDeepModel(config)
-
- loss_net = NetWithLossClass(WideDeep_net, config)
- train_net = TrainStepWrap(loss_net, config)
- eval_net = PredictWithSigmoid(WideDeep_net)
-
- return train_net, eval_net
-
-
- class ModelBuilder():
- """
- ModelBuilder.
- """
- def __init__(self):
- pass
-
- def get_hook(self):
- pass
-
- def get_train_hook(self):
- hooks = []
- callback = LossCallBack()
- hooks.append(callback)
-
- if int(os.getenv('DEVICE_ID')) == 0:
- pass
- return hooks
-
- def get_net(self, config):
- return get_WideDeep_net(config)
-
- def train_and_eval(config):
- """
- train_and_eval.
- """
- data_path = config.data_path
- epochs = config.epochs
- print("epochs is {}".format(epochs))
-
- ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
- batch_size=config.batch_size, is_tf_dataset=config.is_tf_dataset)
-
- print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
-
- net_builder = ModelBuilder()
-
- train_net, eval_net = net_builder.get_net(config)
- param_dict = load_checkpoint(config.ckpt_path)
- load_param_into_net(eval_net, param_dict)
-
- auc_metric = AUCMetric()
- model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
-
- model.eval(ds_eval)
-
-
- if __name__ == "__main__":
- wide_and_deep_config = WideDeepConfig()
- wide_and_deep_config.argparse_init()
- compute_emb_dim(wide_and_deep_config)
- context.set_context(mode=context.GRAPH_MODE, device_target="Davinci")
- train_and_eval(wide_and_deep_config)
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