lightning/pytorch_lightning/trainer/states.py

69 lines
2.5 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 enum import Enum
from functools import wraps
from typing import Callable, Optional
import pytorch_lightning
class TrainerState(str, Enum):
""" State which is set in the :class:`~pytorch_lightning.trainer.trainer.Trainer`
to indicate what is currently or was executed.
>>> # you can math the type with string
>>> TrainerState.RUNNING == 'RUNNING'
True
>>> # which is case sensitive
>>> TrainerState.FINISHED == 'finished'
False
"""
INITIALIZING = 'INITIALIZING'
RUNNING = 'RUNNING'
FINISHED = 'FINISHED'
INTERRUPTED = 'INTERRUPTED'
def trainer_state(*, entering: Optional[TrainerState] = None, exiting: Optional[TrainerState] = None) -> Callable:
""" Decorator for :class:`~pytorch_lightning.trainer.trainer.Trainer` methods
which changes state to `entering` before the function execution and `exiting`
after the function is executed. If `None` is passed to `entering`, the state is not changed.
If `None` is passed to `exiting`, the state is restored to the state before function execution.
If `INTERRUPTED` state is set inside a run function, the state remains `INTERRUPTED`.
"""
def wrapper(fn) -> Callable:
@wraps(fn)
def wrapped_fn(self, *args, **kwargs):
if not isinstance(self, pytorch_lightning.Trainer):
return fn(self, *args, **kwargs)
state_before = self._state
if entering is not None:
self._state = entering
result = fn(self, *args, **kwargs)
# The INTERRUPTED state can be set inside the run function. To indicate that run was interrupted
# we retain INTERRUPTED state
if self._state == TrainerState.INTERRUPTED:
return result
self._state = exiting if exiting is not None else state_before
return result
return wrapped_fn
return wrapper