Source code for pytorch_lightning.callbacks.early_stopping
# 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
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# See the License for the specific language governing permissions and
# limitations under the License.
r"""
Early Stopping
^^^^^^^^^^^^^^
Monitor a validation metric and stop training when it stops improving.
"""
import os
import numpy as np
import torch
from pytorch_lightning import _logger as log
from pytorch_lightning.callbacks.base import Callback
from pytorch_lightning.utilities import rank_zero_warn
from pytorch_lightning.utilities.xla_device_utils import XLADeviceUtils
TPU_AVAILABLE = XLADeviceUtils.tpu_device_exists()
torch_inf = torch.tensor(np.Inf)
[docs]class EarlyStopping(Callback):
r"""
Monitor a validation metric and stop training when it stops improving.
Args:
monitor: quantity to be monitored. Default: ``'early_stop_on'``.
min_delta: minimum change in the monitored quantity
to qualify as an improvement, i.e. an absolute
change of less than `min_delta`, will count as no
improvement. Default: ``0.0``.
patience: number of validation epochs with no improvement
after which training will be stopped. Default: ``3``.
verbose: verbosity mode. Default: ``False``.
mode: one of {auto, min, max}. In `min` mode,
training will stop when the quantity
monitored has stopped decreasing; in `max`
mode it will stop when the quantity
monitored has stopped increasing; in `auto`
mode, the direction is automatically inferred
from the name of the monitored quantity. Default: ``'auto'``.
strict: whether to crash the training if `monitor` is
not found in the validation metrics. Default: ``True``.
Example::
>>> from pytorch_lightning import Trainer
>>> from pytorch_lightning.callbacks import EarlyStopping
>>> early_stopping = EarlyStopping('val_loss')
>>> trainer = Trainer(callbacks=[early_stopping])
"""
mode_dict = {
'min': torch.lt,
'max': torch.gt,
}
def __init__(self, monitor: str = 'early_stop_on', min_delta: float = 0.0, patience: int = 3,
verbose: bool = False, mode: str = 'auto', strict: bool = True):
super().__init__()
self.monitor = monitor
self.patience = patience
self.verbose = verbose
self.strict = strict
self.min_delta = min_delta
self.wait_count = 0
self.stopped_epoch = 0
self.mode = mode
self.warned_result_obj = False
# Indicates, if eval results are used as basis for early stopping
# It is set to False initially and overwritten, if eval results have been validated
self.based_on_eval_results = False
if mode not in self.mode_dict and mode != 'auto':
if self.verbose > 0:
log.info(f'EarlyStopping mode {mode} is unknown, fallback to auto mode.')
self.mode = 'auto'
if self.mode == 'auto':
if self.monitor == 'acc':
self.mode = 'max'
else:
self.mode = 'min'
if self.verbose > 0:
log.info(f'EarlyStopping mode set to {self.mode} for monitoring {self.monitor}.')
self.min_delta *= 1 if self.monitor_op == torch.gt else -1
self.best_score = torch_inf if self.monitor_op == torch.lt else -torch_inf
def _validate_condition_metric(self, logs):
monitor_val = logs.get(self.monitor)
error_msg = (f'Early stopping conditioned on metric `{self.monitor}`'
f' which is not available. Pass in or modify your `EarlyStopping` callback to use any of the'
f' following: `{"`, `".join(list(logs.keys()))}`')
if monitor_val is None:
if self.strict:
raise RuntimeError(error_msg)
if self.verbose > 0:
rank_zero_warn(error_msg, RuntimeWarning)
return False
return True
@property
def monitor_op(self):
return self.mode_dict[self.mode]
[docs] def on_save_checkpoint(self, trainer, pl_module):
return {
'wait_count': self.wait_count,
'stopped_epoch': self.stopped_epoch,
'best_score': self.best_score,
'patience': self.patience
}
[docs] def on_load_checkpoint(self, checkpointed_state):
self.wait_count = checkpointed_state['wait_count']
self.stopped_epoch = checkpointed_state['stopped_epoch']
self.best_score = checkpointed_state['best_score']
self.patience = checkpointed_state['patience']
[docs] def on_validation_end(self, trainer, pl_module):
if trainer.running_sanity_check:
return
self._run_early_stopping_check(trainer, pl_module)
[docs] def on_validation_epoch_end(self, trainer, pl_module):
if trainer.running_sanity_check:
return
if self._validate_condition_metric(trainer.logger_connector.callback_metrics):
# turn off early stopping in on_train_epoch_end
self.based_on_eval_results = True
[docs] def on_train_epoch_end(self, trainer, pl_module, outputs):
# disable early stopping in train loop when there's a val loop
if self.based_on_eval_results:
return
# early stopping can also work in the train loop when there is no val loop
should_check_early_stop = False
# fallback to monitor key in result dict
if trainer.logger_connector.callback_metrics.get(self.monitor, None) is not None:
should_check_early_stop = True
if should_check_early_stop:
self._run_early_stopping_check(trainer, pl_module)
def _run_early_stopping_check(self, trainer, pl_module):
"""
Checks whether the early stopping condition is met
and if so tells the trainer to stop the training.
"""
logs = trainer.logger_connector.callback_metrics
if not self._validate_condition_metric(logs):
return # short circuit if metric not present
current = logs.get(self.monitor)
# when in dev debugging
trainer.dev_debugger.track_early_stopping_history(self, current)
if not isinstance(current, torch.Tensor):
current = torch.tensor(current, device=pl_module.device)
if trainer.use_tpu and TPU_AVAILABLE:
current = current.cpu()
if self.monitor_op(current - self.min_delta, self.best_score):
self.best_score = current
self.wait_count = 0
else:
self.wait_count += 1
should_stop = self.wait_count >= self.patience
if bool(should_stop):
self.stopped_epoch = trainer.current_epoch
trainer.should_stop = True
# stop every ddp process if any world process decides to stop
should_stop = trainer.accelerator_backend.early_stopping_should_stop(pl_module)
trainer.should_stop = should_stop
