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- # Copyright 2022 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.
- # ============================================================================
-
- """ test_training """
-
- import os
-
- from mindspore import Model, context
- from mindspore.train.serialization import load_checkpoint, load_param_into_net,\
- build_searched_strategy, merge_sliced_parameter
-
- from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
- from src.callbacks import LossCallBack, EvalCallBack
- from src.datasets import create_dataset, DataType
- from src.metrics import AUCMetric
- from src.model_utils.config import config as cfg
-
-
- def get_wide_deep_net(config):
- """
- Get network of wide&deep model.
- """
- wide_deep_net = WideDeepModel(config)
-
- loss_net = NetWithLossClass(wide_deep_net, config)
- train_net = TrainStepWrap(loss_net, dynamic_embedding=config.dynamic_embedding)
- eval_net = PredictWithSigmoid(wide_deep_net)
-
- return train_net, eval_net
-
-
- class ModelBuilder():
- """
- Wide and deep model builder
- """
- 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_wide_deep_net(config)
-
-
- def test_eval(config):
- """
- test evaluate
- """
- data_path = config.data_path
- batch_size = config.batch_size
- if config.dataset_type == "tfrecord":
- dataset_type = DataType.TFRECORD
- elif config.dataset_type == "mindrecord":
- dataset_type = DataType.MINDRECORD
- else:
- dataset_type = DataType.H5
- ds_eval = create_dataset(data_path, train_mode=False,
- batch_size=batch_size, data_type=dataset_type)
- print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
-
- net_builder = ModelBuilder()
- train_net, eval_net = net_builder.get_net(config)
- ckpt_path = config.ckpt_path
- if ";" in ckpt_path:
- ckpt_paths = ckpt_path.split(';')
- param_list_dict = {}
- strategy = build_searched_strategy(config.stra_ckpt)
- for slice_path in ckpt_paths:
- param_slice_dict = load_checkpoint(slice_path)
- for key, value in param_slice_dict.items():
- if 'optimizer' in key:
- continue
- if key not in param_list_dict:
- param_list_dict[key] = []
- param_list_dict[key].append(value)
- param_dict = {}
- for key, value in param_list_dict.items():
- if key in strategy:
- merged_parameter = merge_sliced_parameter(value, strategy)
- else:
- merged_parameter = merge_sliced_parameter(value)
- param_dict[key] = merged_parameter
- else:
- param_dict = load_checkpoint(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})
-
- eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
-
- model.eval(ds_eval, callbacks=eval_callback)
-
-
- def eval_wide_and_deep():
- context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target)
- test_eval(cfg)
-
- if __name__ == "__main__":
- eval_wide_and_deep()
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