forked from mindspore-Ecosystem/mindspore
102 lines
3.5 KiB
Python
102 lines
3.5 KiB
Python
# 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_multinpu."""
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import os
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import sys
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import numpy as np
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from mindspore import Model, context
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
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from mindspore.train import ParallelMode
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from mindspore.communication.management import get_rank, get_group_size, init
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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
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from src.metrics import AUCMetric
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from src.config import WideDeepConfig
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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context.set_context(mode=GRAPH_MODE, device_target="Davinci", save_graph=True)
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context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True)
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init()
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def get_WideDeep_net(config):
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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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ModelBuilder
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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_train_eval():
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"""
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test_train_eval
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"""
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np.random.seed(1000)
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config = WideDeepConfig
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data_path = Config.data_path
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batch_size = config.batch_size
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epochs = config.epochs
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print("epochs is {}".format(epochs))
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ds_train = create_dataset(data_path, train_mode=True, epochs=epochs,
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batch_size=batch_size, rank_id=get_rank(), rank_size=get_group_size())
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ds_eval = create_dataset(data_path, train_mode=False, epochs=epochs + 1,
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batch_size=batch_size, rank_id=get_rank(), rank_size=get_group_size())
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print("ds_train.size: {}".format(ds_train.get_dataset_size()))
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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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train_net.set_train()
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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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callback = LossCallBack(config=config)
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ckptconfig = CheckpointConfig(save_checkpoint_steps=1, keep_checkpoint_max=5)
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ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
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directory=config.ckpt_path, config=ckptconfig)
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model.train(epochs, ds_train,
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callbacks=[TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback, ckpoint_cb])
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