mindspore.train.callback._time_monitor 源代码

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"""TimeMonitor Callback class."""
from __future__ import absolute_import

import time

from mindspore._checkparam import 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: .. note:: Before running the following example, you need to customize the network LeNet5 and dataset preparation function create_dataset. Refer to `Building a Network <https://www.mindspore.cn/tutorials/en/r2.0.0-alpha/beginner/model.html>`_ and `Dataset <https://www.mindspore.cn/tutorials/en/r2.0.0-alpha/beginner/dataset.html>`_ . >>> from mindspore import nn >>> from mindspore.train import Model, TimeMonitor >>> >>> 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) >>> data_path = './MNIST_Data' >>> dataset = create_dataset(data_path) >>> 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)