!1601 [Auto parallel] Add some wide&deep files

Merge pull request !1601 from Xiaoda/some-wide-deep-files
This commit is contained in:
mindspore-ci-bot 2020-05-29 07:50:45 +08:00 committed by Gitee
commit ffd2bea87e
2 changed files with 189 additions and 0 deletions

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# Copyright 2020 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.
"""
callbacks
"""
import time
from mindspore.train.callback import Callback
from mindspore import context
def add_write(file_path, out_str):
"""
add lines to the file
"""
with open(file_path, 'a+', encoding="utf-8") as file_out:
file_out.write(out_str + "\n")
class LossCallBack(Callback):
"""
Monitor the loss in training.
If the loss is NAN or INF, terminate the training.
Note:
If per_print_times is 0, do NOT print loss.
Args:
per_print_times (int): Print loss every times. Default: 1.
"""
def __init__(self, config, per_print_times=1):
super(LossCallBack, self).__init__()
if not isinstance(per_print_times, int) or per_print_times < 0:
raise ValueError("per_print_times must be in and >= 0.")
self._per_print_times = per_print_times
self.config = config
def step_end(self, run_context):
cb_params = run_context.original_args()
wide_loss, deep_loss = cb_params.net_outputs[0].asnumpy(), cb_params.net_outputs[1].asnumpy()
cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
cur_num = cb_params.cur_step_num
print("===loss===", cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss)
# raise ValueError
if self._per_print_times != 0 and cur_num % self._per_print_times == 0:
loss_file = open(self.config.loss_file_name, "a+")
loss_file.write("epoch: %s, step: %s, wide_loss: %s, deep_loss: %s" %
(cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss))
loss_file.write("\n")
loss_file.close()
print("epoch: %s, step: %s, wide_loss: %s, deep_loss: %s" %
(cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss))
class EvalCallBack(Callback):
"""
Monitor the loss in evaluating.
If the loss is NAN or INF, terminate evaluating.
Note:
If per_print_times is 0, do NOT print loss.
Args:
print_per_step (int): Print loss every times. Default: 1.
"""
def __init__(self, model, eval_dataset, auc_metric, config, print_per_step=1):
super(EvalCallBack, self).__init__()
if not isinstance(print_per_step, int) or print_per_step < 0:
raise ValueError("print_per_step must be int and >= 0.")
self.print_per_step = print_per_step
self.model = model
self.eval_dataset = eval_dataset
self.aucMetric = auc_metric
self.aucMetric.clear()
self.eval_file_name = config.eval_file_name
def epoch_name(self, run_context):
"""
epoch name
"""
self.aucMetric.clear()
context.set_auto_parallel_context(strategy_ckpt_save_file="",
strategy_ckpt_load_file="./strategy_train.ckpt")
start_time = time.time()
out = self.model.eval(self.eval_dataset)
end_time = time.time()
eval_time = int(end_time - start_time)
time_str = time.strftime("%Y-%m-%d %H:%M%S", time.localtime())
out_str = "{}==== EvalCallBack model.eval(): {}; eval_time: {}s".format(time_str, out.values(), eval_time)
print(out_str)
add_write(self.eval_file_name, out_str)

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# Copyright 2020 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.callback import ModelCheckpoint, CheckpointConfig
from wide_deep.models.WideDeep import PredictWithSigmoid, TrainStepWarp, NetWithLossClass, WideDeepModel
from wide_deep.utils.callbacks import LossCallBack
from wide_deep.data.datasets import create_dataset
from tools.config import Config_WideDeep
context.set_context(model=context.GRAPH_MODE, device_target="Ascend", save_graphs=True)
def get_WideDeep_net(configure):
WideDeep_net = WideDeepModel(configure)
loss_net = NetWithLossClass(WideDeep_net, configure)
train_net = TrainStepWarp(loss_net)
eval_net = PredictWithSigmoid(WideDeep_net)
return train_net, eval_net
class ModelBuilder():
"""
Build the model.
"""
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, configure):
return get_WideDeep_net(configure)
def test_train(configure):
"""
test_train
"""
data_path = configure.data_path
batch_size = configure.batch_size
epochs = configure.epochs
ds_train = create_dataset(data_path, train_mode=True, epochs=epochs, batch_size=batch_size)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
net_builder = ModelBuilder()
train_net, _ = net_builder.get_net(configure)
train_net.set_train()
model = Model(train_net)
callback = LossCallBack(configure)
ckptconfig = CheckpointConfig(save_checkpoint_steps=1,
keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train', directory=configure.ckpt_path, config=ckptconfig)
model.train(epochs, ds_train, callbacks=[callback, ckpoint_cb])
if __name__ == "__main__":
config = Config_WideDeep()
config.argparse_init()
test_train(config)