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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
# 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.
r"""
Early Stopping
^^^^^^^^^^^^^^

Monitor a metric and stop training when it stops improving.

"""
import logging
from typing import Any, Callable, Dict, Optional, Tuple

import numpy as np
import torch

import pytorch_lightning as pl
from pytorch_lightning.callbacks.base import Callback
from pytorch_lightning.utilities import rank_zero_warn
from pytorch_lightning.utilities.exceptions import MisconfigurationException

log = logging.getLogger(__name__)


[docs]class EarlyStopping(Callback): r""" Monitor a metric and stop training when it stops improving. Args: monitor: quantity to be monitored. min_delta: minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than or equal to `min_delta`, will count as no improvement. patience: number of checks with no improvement after which training will be stopped. Under the default configuration, one check happens after every training epoch. However, the frequency of validation can be modified by setting various parameters on the ``Trainer``, for example ``check_val_every_n_epoch`` and ``val_check_interval``. .. note:: It must be noted that the patience parameter counts the number of validation checks with no improvement, and not the number of training epochs. Therefore, with parameters ``check_val_every_n_epoch=10`` and ``patience=3``, the trainer will perform at least 40 training epochs before being stopped. verbose: verbosity mode. mode: one of ``'min'``, ``'max'``. In ``'min'`` mode, training will stop when the quantity monitored has stopped decreasing and in ``'max'`` mode it will stop when the quantity monitored has stopped increasing. strict: whether to crash the training if `monitor` is not found in the validation metrics. check_finite: When set ``True``, stops training when the monitor becomes NaN or infinite. stopping_threshold: Stop training immediately once the monitored quantity reaches this threshold. divergence_threshold: Stop training as soon as the monitored quantity becomes worse than this threshold. check_on_train_epoch_end: whether to run early stopping at the end of the training epoch. If this is ``False``, then the check runs at the end of the validation. Raises: MisconfigurationException: If ``mode`` is none of ``"min"`` or ``"max"``. RuntimeError: If the metric ``monitor`` is not available. Example:: >>> from pytorch_lightning import Trainer >>> from pytorch_lightning.callbacks import EarlyStopping >>> early_stopping = EarlyStopping('val_loss') >>> trainer = Trainer(callbacks=[early_stopping]) .. tip:: Saving and restoring multiple early stopping callbacks at the same time is supported under variation in the following arguments: *monitor, mode* Read more: :ref:`Persisting Callback State` """ mode_dict = {"min": torch.lt, "max": torch.gt} order_dict = {"min": "<", "max": ">"} def __init__( self, monitor: str, min_delta: float = 0.0, patience: int = 3, verbose: bool = False, mode: str = "min", strict: bool = True, check_finite: bool = True, stopping_threshold: Optional[float] = None, divergence_threshold: Optional[float] = None, check_on_train_epoch_end: Optional[bool] = None, ): super().__init__() self.monitor = monitor self.min_delta = min_delta self.patience = patience self.verbose = verbose self.mode = mode self.strict = strict self.check_finite = check_finite self.stopping_threshold = stopping_threshold self.divergence_threshold = divergence_threshold self.wait_count = 0 self.stopped_epoch = 0 self._check_on_train_epoch_end = check_on_train_epoch_end if self.mode not in self.mode_dict: raise MisconfigurationException(f"`mode` can be {', '.join(self.mode_dict.keys())}, got {self.mode}") self.min_delta *= 1 if self.monitor_op == torch.gt else -1 torch_inf = torch.tensor(np.Inf) self.best_score = torch_inf if self.monitor_op == torch.lt else -torch_inf @property def state_key(self) -> str: return self._generate_state_key(monitor=self.monitor, mode=self.mode)
[docs] def on_init_end(self, trainer: "pl.Trainer") -> None: if self._check_on_train_epoch_end is None: # if the user runs validation multiple times per training epoch or multiple training epochs without # validation, then we run after validation instead of on train epoch end self._check_on_train_epoch_end = trainer.val_check_interval == 1.0 and trainer.check_val_every_n_epoch == 1
def _validate_condition_metric(self, logs: Dict[str, float]) -> bool: monitor_val = logs.get(self.monitor) error_msg = ( f"Early stopping conditioned on metric `{self.monitor}` which is not available." " Pass in or modify your `EarlyStopping` callback to use any of the following:" f' `{"`, `".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) -> Callable: return self.mode_dict[self.mode]
[docs] def on_save_checkpoint( self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", checkpoint: Dict[str, Any] ) -> Dict[str, Any]: 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, trainer: "pl.Trainer", pl_module: "pl.LightningModule", callback_state: Dict[str, Any] ) -> None: self.wait_count = callback_state["wait_count"] self.stopped_epoch = callback_state["stopped_epoch"] self.best_score = callback_state["best_score"] self.patience = callback_state["patience"]
def _should_skip_check(self, trainer: "pl.Trainer") -> bool: from pytorch_lightning.trainer.states import TrainerFn return trainer.state.fn != TrainerFn.FITTING or trainer.sanity_checking
[docs] def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: if not self._check_on_train_epoch_end or self._should_skip_check(trainer): return self._run_early_stopping_check(trainer)
[docs] def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: if self._check_on_train_epoch_end or self._should_skip_check(trainer): return self._run_early_stopping_check(trainer)
def _run_early_stopping_check(self, trainer: "pl.Trainer") -> None: """Checks whether the early stopping condition is met and if so tells the trainer to stop the training.""" logs = trainer.callback_metrics if trainer.fast_dev_run or not self._validate_condition_metric( # disable early_stopping with fast_dev_run logs ): # short circuit if metric not present return current = logs[self.monitor].squeeze() should_stop, reason = self._evaluate_stopping_criteria(current) # stop every ddp process if any world process decides to stop should_stop = trainer.training_type_plugin.reduce_boolean_decision(should_stop) trainer.should_stop = trainer.should_stop or should_stop if should_stop: self.stopped_epoch = trainer.current_epoch if reason and self.verbose: self._log_info(trainer, reason) def _evaluate_stopping_criteria(self, current: torch.Tensor) -> Tuple[bool, Optional[str]]: should_stop = False reason = None if self.check_finite and not torch.isfinite(current): should_stop = True reason = ( f"Monitored metric {self.monitor} = {current} is not finite." f" Previous best value was {self.best_score:.3f}. Signaling Trainer to stop." ) elif self.stopping_threshold is not None and self.monitor_op(current, self.stopping_threshold): should_stop = True reason = ( "Stopping threshold reached:" f" {self.monitor} = {current} {self.order_dict[self.mode]} {self.stopping_threshold}." " Signaling Trainer to stop." ) elif self.divergence_threshold is not None and self.monitor_op(-current, -self.divergence_threshold): should_stop = True reason = ( "Divergence threshold reached:" f" {self.monitor} = {current} {self.order_dict[self.mode]} {self.divergence_threshold}." " Signaling Trainer to stop." ) elif self.monitor_op(current - self.min_delta, self.best_score.to(current.device)): should_stop = False reason = self._improvement_message(current) self.best_score = current self.wait_count = 0 else: self.wait_count += 1 if self.wait_count >= self.patience: should_stop = True reason = ( f"Monitored metric {self.monitor} did not improve in the last {self.wait_count} records." f" Best score: {self.best_score:.3f}. Signaling Trainer to stop." ) return should_stop, reason def _improvement_message(self, current: torch.Tensor) -> str: """Formats a log message that informs the user about an improvement in the monitored score.""" if torch.isfinite(self.best_score): msg = ( f"Metric {self.monitor} improved by {abs(self.best_score - current):.3f} >=" f" min_delta = {abs(self.min_delta)}. New best score: {current:.3f}" ) else: msg = f"Metric {self.monitor} improved. New best score: {current:.3f}" return msg @staticmethod def _log_info(trainer: Optional["pl.Trainer"], message: str) -> None: if trainer is not None and trainer.world_size > 1: log.info(f"[rank: {trainer.global_rank}] {message}") else: log.info(message)

© Copyright Copyright (c) 2018-2021, William Falcon et al... Revision 46f718d2.

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