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81 lines
3.2 KiB
81 lines
3.2 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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"""train_criteo."""
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import os
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import pytest
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from mindspore import context
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from mindspore.train.model import Model
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from mindspore.common import set_seed
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from src.deepfm import ModelBuilder, AUCMetric
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from src.config import DataConfig, ModelConfig, TrainConfig
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from src.dataset import create_dataset, DataType
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from src.callback import EvalCallBack, LossCallBack, TimeMonitor
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set_seed(1)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_deepfm():
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data_config = DataConfig()
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train_config = TrainConfig()
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id)
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rank_size = None
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rank_id = None
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dataset_path = "/home/workspace/mindspore_dataset/criteo_data/mindrecord/"
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print("dataset_path:", dataset_path)
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ds_train = create_dataset(dataset_path,
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train_mode=True,
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epochs=1,
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batch_size=train_config.batch_size,
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data_type=DataType(data_config.data_format),
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rank_size=rank_size,
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rank_id=rank_id)
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model_builder = ModelBuilder(ModelConfig, TrainConfig)
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train_net, eval_net = model_builder.get_train_eval_net()
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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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loss_file_name = './loss.log'
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time_callback = TimeMonitor(data_size=ds_train.get_dataset_size())
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loss_callback = LossCallBack(loss_file_path=loss_file_name)
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callback_list = [time_callback, loss_callback]
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eval_file_name = './auc.log'
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ds_eval = create_dataset(dataset_path, train_mode=False,
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epochs=1,
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batch_size=train_config.batch_size,
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data_type=DataType(data_config.data_format))
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eval_callback = EvalCallBack(model, ds_eval, auc_metric,
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eval_file_path=eval_file_name)
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callback_list.append(eval_callback)
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print("train_config.train_epochs:", train_config.train_epochs)
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model.train(train_config.train_epochs, ds_train, callbacks=callback_list)
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export_loss_value = 0.52
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print("loss_callback.loss:", loss_callback.loss)
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assert loss_callback.loss < export_loss_value
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export_per_step_time = 30.0
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print("time_callback:", time_callback.per_step_time)
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assert time_callback.per_step_time < export_per_step_time
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print("*******test case pass!********")
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