wsq3
5 years ago
parent
d113e7a694
commit
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# 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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# httpwww.apache.orglicensesLICENSE-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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"""
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Defined callback for DeepFM.
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"""
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import time
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from mindspore.train.callback import Callback
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def add_write(file_path, out_str):
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with open(file_path, 'a+', encoding='utf-8') as file_out:
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file_out.write(out_str + '\n')
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class EvalCallBack(Callback):
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"""
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Monitor the loss in training.
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If the loss is NAN or INF terminating training.
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Note
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If per_print_times is 0 do not print loss.
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"""
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def __init__(self, model, eval_dataset, auc_metric, eval_file_path):
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super(EvalCallBack, self).__init__()
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self.model = model
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self.eval_dataset = eval_dataset
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self.aucMetric = auc_metric
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self.aucMetric.clear()
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self.eval_file_path = eval_file_path
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def epoch_end(self, run_context):
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start_time = time.time()
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out = self.model.eval(self.eval_dataset)
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eval_time = int(time.time() - start_time)
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time_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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out_str = "{} EvalCallBack metric{}; eval_time{}s".format(
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time_str, out.values(), eval_time)
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print(out_str)
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add_write(self.eval_file_path, out_str)
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class LossCallBack(Callback):
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"""
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Monitor the loss in training.
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If the loss is NAN or INF terminating training.
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Note
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If per_print_times is 0 do not print loss.
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Args
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loss_file_path (str) The file absolute path, to save as loss_file;
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per_print_times (int) Print loss every times. Default 1.
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"""
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def __init__(self, loss_file_path, per_print_times=1):
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super(LossCallBack, self).__init__()
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if not isinstance(per_print_times, int) or per_print_times < 0:
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raise ValueError("print_step must be int and >= 0.")
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self.loss_file_path = loss_file_path
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self._per_print_times = per_print_times
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self.loss = 0
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def step_end(self, run_context):
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cb_params = run_context.original_args()
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loss = cb_params.net_outputs.asnumpy()
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cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
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cur_num = cb_params.cur_step_num
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if self._per_print_times != 0 and cur_num % self._per_print_times == 0:
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with open(self.loss_file_path, "a+") as loss_file:
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time_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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loss_file.write("{} epoch: {} step: {}, loss is {}\n".format(
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time_str, cb_params.cur_epoch_num, cur_step_in_epoch, loss))
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print("epoch: {} step: {}, loss is {}\n".format(
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cb_params.cur_epoch_num, cur_step_in_epoch, loss))
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self.loss = loss
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class TimeMonitor(Callback):
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"""
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Time monitor for calculating cost of each epoch.
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Args
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data_size (int) step size of an epoch.
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"""
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def __init__(self, data_size):
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super(TimeMonitor, self).__init__()
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self.data_size = data_size
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self.per_step_time = 0
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def epoch_begin(self, run_context):
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self.epoch_time = time.time()
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def epoch_end(self, run_context):
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epoch_mseconds = (time.time() - self.epoch_time) * 1000
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per_step_mseconds = epoch_mseconds / self.data_size
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print("epoch time: {0}, per step time: {1}".format(epoch_mseconds, per_step_mseconds), flush=True)
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self.per_step_time = per_step_mseconds
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def step_begin(self, run_context):
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self.step_time = time.time()
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def step_end(self, run_context):
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step_mseconds = (time.time() - self.step_time) * 1000
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print(f"step time {step_mseconds}", flush=True)
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# 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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"""
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network config setting, will be used in train.py and eval.py
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"""
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class DataConfig:
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"""
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Define parameters of dataset.
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"""
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data_vocab_size = 184965
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train_num_of_parts = 21
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test_num_of_parts = 3
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batch_size = 1000
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data_field_size = 39
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# dataset format, 1: mindrecord, 2: tfrecord, 3: h5
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data_format = 3
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class ModelConfig:
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"""
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Define parameters of model.
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"""
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batch_size = DataConfig.batch_size
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data_field_size = DataConfig.data_field_size
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data_vocab_size = DataConfig.data_vocab_size
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data_emb_dim = 80
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deep_layer_args = [[400, 400, 512], "relu"]
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init_args = [-0.01, 0.01]
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weight_bias_init = ['normal', 'normal']
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keep_prob = 0.9
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class TrainConfig:
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"""
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Define parameters of training.
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"""
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batch_size = DataConfig.batch_size
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l2_coef = 1e-6
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learning_rate = 1e-5
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epsilon = 1e-8
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loss_scale = 1024.0
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train_epochs = 3
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save_checkpoint = True
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ckpt_file_name_prefix = "deepfm"
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save_checkpoint_steps = 1
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keep_checkpoint_max = 15
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eval_callback = True
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loss_callback = True
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File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
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# 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/criteo_h5/"
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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 = 0.51
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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 = 10.4
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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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