# 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.
# ============================================================================
"""TimeMonitor Callback class."""
from __future__ import absolute_import
import time
from mindspore import _checkparam as Validator
from mindspore.train.callback._callback import Callback
[文档]class TimeMonitor(Callback):
"""
Monitor the time in train or eval process.
Args:
data_size (int): How many steps are the intervals between print information each time.
if the program get `batch_num` during training, `data_size` will be set to `batch_num`,
otherwise `data_size` will be used. Default: ``None`` .
Raises:
ValueError: If data_size is not positive int.
Examples:
>>> from mindspore import nn
>>> from mindspore.train import Model, TimeMonitor
>>>
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/r2.1/docs/mindspore/code/lenet.py
>>> net = LeNet5()
>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
>>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9)
>>> model = Model(net, loss_fn=loss, optimizer=optim)
>>> # Create the dataset taking MNIST as an example. Refer to
>>> # https://gitee.com/mindspore/docs/blob/r2.1/docs/mindspore/code/mnist.py
>>> dataset = create_dataset()
>>> time_monitor = TimeMonitor()
>>> model.train(10, dataset, callbacks=time_monitor)
"""
def __init__(self, data_size=None):
super(TimeMonitor, self).__init__()
self.data_size = data_size
self.epoch_time = time.time()
[文档] def epoch_begin(self, run_context):
"""
Record time at the beginning of epoch.
Args:
run_context (RunContext): Context of the process running. For more details,
please refer to :class:`mindspore.train.RunContext`.
"""
self.epoch_time = time.time()
[文档] def epoch_end(self, run_context):
"""
Print process cost time at the end of epoch.
Args:
run_context (RunContext): Context of the process running. For more details,
please refer to :class:`mindspore.train.RunContext`.
"""
epoch_seconds = (time.time() - self.epoch_time) * 1000
step_size = self.data_size
cb_params = run_context.original_args()
mode = cb_params.get("mode", "")
if hasattr(cb_params, "batch_num"):
batch_num = cb_params.batch_num
if isinstance(batch_num, int) and batch_num > 0:
step_size = cb_params.batch_num
Validator.check_positive_int(step_size)
step_seconds = epoch_seconds / step_size
print("{} epoch time: {:5.3f} ms, per step time: {:5.3f} ms".format
(mode.title(), epoch_seconds, step_seconds), flush=True)