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123 lines
3.8 KiB
123 lines
3.8 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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""" test_training """
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import os
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from mindspore import Model, context
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from mindspore.train.serialization import load_checkpoint, load_param_into_net,\
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build_searched_strategy, merge_sliced_parameter
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from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
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from src.callbacks import LossCallBack, EvalCallBack
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from src.datasets import create_dataset, DataType
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from src.metrics import AUCMetric
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from src.config import WideDeepConfig
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def get_WideDeep_net(config):
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"""
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Get network of wide&deep model.
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"""
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WideDeep_net = WideDeepModel(config)
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loss_net = NetWithLossClass(WideDeep_net, config)
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train_net = TrainStepWrap(loss_net)
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eval_net = PredictWithSigmoid(WideDeep_net)
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return train_net, eval_net
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class ModelBuilder():
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"""
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Wide and deep model builder
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"""
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def __init__(self):
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pass
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def get_hook(self):
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pass
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def get_train_hook(self):
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hooks = []
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callback = LossCallBack()
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hooks.append(callback)
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if int(os.getenv('DEVICE_ID')) == 0:
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pass
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return hooks
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def get_net(self, config):
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return get_WideDeep_net(config)
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def test_eval(config):
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"""
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test evaluate
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"""
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data_path = config.data_path
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batch_size = config.batch_size
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if config.dataset_type == "tfrecord":
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dataset_type = DataType.TFRECORD
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elif config.dataset_type == "mindrecord":
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dataset_type = DataType.MINDRECORD
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else:
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dataset_type = DataType.H5
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ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
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batch_size=batch_size, data_type=dataset_type)
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print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
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net_builder = ModelBuilder()
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train_net, eval_net = net_builder.get_net(config)
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ckpt_path = config.ckpt_path
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if ";" in ckpt_path:
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ckpt_paths = ckpt_path.split(';')
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param_list_dict = {}
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strategy = build_searched_strategy(config.stra_ckpt)
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for slice_path in ckpt_paths:
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param_slice_dict = load_checkpoint(slice_path)
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for key, value in param_slice_dict.items():
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if 'optimizer' in key:
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continue
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if key not in param_list_dict:
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param_list_dict[key] = []
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param_list_dict[key].append(value)
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param_dict = {}
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for key, value in param_list_dict.items():
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if key in strategy:
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merged_parameter = merge_sliced_parameter(value, strategy)
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else:
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merged_parameter = merge_sliced_parameter(value)
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param_dict[key] = merged_parameter
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else:
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param_dict = load_checkpoint(ckpt_path)
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load_param_into_net(eval_net, param_dict)
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auc_metric = AUCMetric()
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model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
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eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
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model.eval(ds_eval, callbacks=eval_callback)
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if __name__ == "__main__":
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widedeep_config = WideDeepConfig()
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widedeep_config.argparse_init()
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context.set_context(mode=context.GRAPH_MODE, device_target=widedeep_config.device_target)
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test_eval(widedeep_config)
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