lightning/pytorch_lightning/trainer/states.py

69 lines
2.3 KiB
Python

# Copyright The PyTorch Lightning team.
#
# 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.
from pytorch_lightning.utilities import LightningEnum
class TrainerState(LightningEnum):
""" State for the :class:`~pytorch_lightning.trainer.trainer.Trainer`
to indicate what is currently or was executed. It follows the user-called
functions such as `trainer.fit()` and `trainer.test().
>>> # you can compare the type with a string
>>> TrainerState.FITTING == 'fit'
True
>>> # which is case insensitive
>>> TrainerState.FINISHED == 'FINISHED'
True
"""
INITIALIZING = 'initializing' # trainer creation
FITTING = 'fit' # trainer.fit()
VALIDATING = 'validate' # trainer.validate()
TESTING = 'test' # trainer.test()
PREDICTING = 'predict' # trainer.predict()
TUNING = 'tune' # trainer.tune()
FINISHED = 'finished'
INTERRUPTED = 'interrupted'
@property
def stopped(self) -> bool:
return self in (self.FINISHED, self.INTERRUPTED)
@property
def running(self) -> bool:
return self in (self.FITTING, self.VALIDATING, self.TESTING, self.PREDICTING, self.TUNING)
class RunningStage(LightningEnum):
"""Current running stage.
This stage complements :class:`TrainerState` for example to indicate that
`RunningStage.VALIDATING` will be set both during `TrainerState.FITTING`
and `TrainerState.VALIDATING`. It follows the internal code logic.
>>> # you can match the Enum with string
>>> RunningStage.TRAINING == 'train'
True
"""
TRAINING = 'train'
SANITY_CHECKING = 'sanity_check'
VALIDATING = 'validate'
TESTING = 'test'
PREDICTING = 'predict'
TUNING = 'tune'
@property
def evaluating(self) -> bool:
return self in (self.VALIDATING, self.TESTING)