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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.
- # ============================================================================
- """TimeMonitor Callback class."""
-
- import time
-
- from mindspore import log as logger
- from ._callback import Callback
-
-
- class TimeMonitor(Callback):
- """
- Monitor the time in training.
-
- Args:
- data_size (int): Dataset size. Default: None.
- """
-
- def __init__(self, data_size=None):
- super(TimeMonitor, self).__init__()
- self.data_size = data_size
-
- def epoch_begin(self, run_context):
- self.epoch_time = time.time()
-
- def epoch_end(self, run_context):
- epoch_seconds = (time.time() - self.epoch_time) * 1000
- step_size = self.data_size
- cb_params = run_context.original_args()
- 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
-
- if not isinstance(step_size, int) or step_size < 1:
- logger.error("data_size must be positive int.")
- return
-
- step_seconds = epoch_seconds / step_size
- print("Epoch time: {:5.3f}, per step time: {:5.3f}".format(epoch_seconds, step_seconds), flush=True)
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