206 lines
7.3 KiB
Python
206 lines
7.3 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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r"""
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Early Stopping
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^^^^^^^^^^^^^^
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Monitor a validation metric and stop training when it stops improving.
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"""
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import os
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import numpy as np
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import torch
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from pytorch_lightning import _logger as log
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from pytorch_lightning.callbacks.base import Callback
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from pytorch_lightning.utilities import rank_zero_warn
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from pytorch_lightning.utilities.xla_device_utils import XLADeviceUtils
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TPU_AVAILABLE = XLADeviceUtils.tpu_device_exists()
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torch_inf = torch.tensor(np.Inf)
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class EarlyStopping(Callback):
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r"""
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Monitor a validation metric and stop training when it stops improving.
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Args:
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monitor: quantity to be monitored. Default: ``'early_stop_on'``.
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min_delta: minimum change in the monitored quantity
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to qualify as an improvement, i.e. an absolute
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change of less than `min_delta`, will count as no
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improvement. Default: ``0.0``.
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patience: number of validation epochs with no improvement
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after which training will be stopped. Default: ``3``.
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verbose: verbosity mode. Default: ``False``.
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mode: one of {auto, min, max}. In `min` mode,
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training will stop when the quantity
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monitored has stopped decreasing; in `max`
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mode it will stop when the quantity
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monitored has stopped increasing; in `auto`
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mode, the direction is automatically inferred
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from the name of the monitored quantity. Default: ``'auto'``.
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strict: whether to crash the training if `monitor` is
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not found in the validation metrics. Default: ``True``.
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Example::
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>>> from pytorch_lightning import Trainer
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>>> from pytorch_lightning.callbacks import EarlyStopping
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>>> early_stopping = EarlyStopping('val_loss')
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>>> trainer = Trainer(callbacks=[early_stopping])
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"""
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mode_dict = {
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'min': torch.lt,
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'max': torch.gt,
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}
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def __init__(self, monitor: str = 'early_stop_on', min_delta: float = 0.0, patience: int = 3,
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verbose: bool = False, mode: str = 'auto', strict: bool = True):
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super().__init__()
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self.monitor = monitor
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self.patience = patience
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self.verbose = verbose
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self.strict = strict
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self.min_delta = min_delta
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self.wait_count = 0
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self.stopped_epoch = 0
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self.mode = mode
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self.warned_result_obj = False
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# Indicates, if eval results are used as basis for early stopping
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# It is set to False initially and overwritten, if eval results have been validated
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self.based_on_eval_results = False
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if mode not in self.mode_dict:
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if self.verbose > 0:
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log.info(f'EarlyStopping mode {mode} is unknown, fallback to auto mode.')
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self.mode = 'auto'
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if self.mode == 'auto':
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if self.monitor == 'acc':
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self.mode = 'max'
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else:
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self.mode = 'min'
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if self.verbose > 0:
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log.info(f'EarlyStopping mode set to {self.mode} for monitoring {self.monitor}.')
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self.min_delta *= 1 if self.monitor_op == torch.gt else -1
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self.best_score = torch_inf if self.monitor_op == torch.lt else -torch_inf
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def _validate_condition_metric(self, logs):
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monitor_val = logs.get(self.monitor)
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error_msg = (f'Early stopping conditioned on metric `{self.monitor}`'
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f' which is not available. Pass in or modify your `EarlyStopping` callback to use any of the'
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f' following: `{"`, `".join(list(logs.keys()))}`')
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if monitor_val is None:
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if self.strict:
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raise RuntimeError(error_msg)
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if self.verbose > 0:
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rank_zero_warn(error_msg, RuntimeWarning)
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return False
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return True
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@property
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def monitor_op(self):
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return self.mode_dict[self.mode]
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def on_save_checkpoint(self, trainer, pl_module):
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return {
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'wait_count': self.wait_count,
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'stopped_epoch': self.stopped_epoch,
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'best_score': self.best_score,
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'patience': self.patience
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}
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def on_load_checkpoint(self, checkpointed_state):
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self.wait_count = checkpointed_state['wait_count']
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self.stopped_epoch = checkpointed_state['stopped_epoch']
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self.best_score = checkpointed_state['best_score']
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self.patience = checkpointed_state['patience']
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def on_validation_end(self, trainer, pl_module):
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if trainer.running_sanity_check:
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return
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self._run_early_stopping_check(trainer, pl_module)
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def on_validation_epoch_end(self, trainer, pl_module):
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if trainer.running_sanity_check:
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return
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if self._validate_condition_metric(trainer.logger_connector.callback_metrics):
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# turn off early stopping in on_train_epoch_end
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self.based_on_eval_results = True
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def on_train_epoch_end(self, trainer, pl_module, outputs):
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# disable early stopping in train loop when there's a val loop
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if self.based_on_eval_results:
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return
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# early stopping can also work in the train loop when there is no val loop
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should_check_early_stop = False
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# fallback to monitor key in result dict
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if trainer.logger_connector.callback_metrics.get(self.monitor, None) is not None:
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should_check_early_stop = True
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if should_check_early_stop:
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self._run_early_stopping_check(trainer, pl_module)
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def _run_early_stopping_check(self, trainer, pl_module):
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"""
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Checks whether the early stopping condition is met
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and if so tells the trainer to stop the training.
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"""
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logs = trainer.logger_connector.callback_metrics
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if not self._validate_condition_metric(logs):
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return # short circuit if metric not present
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current = logs.get(self.monitor)
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# when in dev debugging
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trainer.dev_debugger.track_early_stopping_history(self, current)
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if not isinstance(current, torch.Tensor):
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current = torch.tensor(current, device=pl_module.device)
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if trainer.use_tpu and TPU_AVAILABLE:
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current = current.cpu()
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if self.monitor_op(current - self.min_delta, self.best_score):
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self.best_score = current
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self.wait_count = 0
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else:
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self.wait_count += 1
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should_stop = self.wait_count >= self.patience
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if bool(should_stop):
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self.stopped_epoch = trainer.current_epoch
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trainer.should_stop = True
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# stop every ddp process if any world process decides to stop
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should_stop = trainer.accelerator_backend.early_stopping_should_stop(pl_module)
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trainer.should_stop = should_stop
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