lightning/pytorch_lightning/callbacks/early_stopping.py

# 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 validation metric and stop training when it stops improving.

"""
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
import os

torch_inf = torch.tensor(np.Inf)

try:
    import torch_xla
    import torch_xla.core.xla_model as xm
except ImportError:
    XLA_AVAILABLE = False
else:
    XLA_AVAILABLE = True


class EarlyStopping(Callback):
    r"""

    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(early_stop_callback=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:
            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. Either add `{self.monitor}` to the return of'
                     ' `validation_epoch_end` 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]

    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
        }

    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']

    def on_validation_end(self, trainer, pl_module):
        if trainer.running_sanity_check:
            return

        self._run_early_stopping_check(trainer, pl_module)

    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

    def on_train_epoch_end(self, trainer, pl_module):
        # 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(current)

        if not isinstance(current, torch.Tensor):
            current = torch.tensor(current, device=pl_module.device)

        if trainer.use_tpu and XLA_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

    def on_train_end(self, trainer, pl_module):
        if self.stopped_epoch > 0 and self.verbose > 0:
            rank_zero_warn('Displayed epoch numbers by `EarlyStopping` start from "1" until v0.6.x,'
                           ' but will start from "0" in v0.8.0.', DeprecationWarning)
            log.info(f'Epoch {self.stopped_epoch + 1:05d}: early stopping triggered.')