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Source code for pytorch_lightning.callbacks.lr_monitor

# 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"""

Learning Rate Monitor
=====================

Monitor and logs learning rate for lr schedulers during training.

"""

from typing import Dict, List, Optional

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


[docs]class LearningRateMonitor(Callback): r""" Automatically monitor and logs learning rate for learning rate schedulers during training. Args: logging_interval: set to ``'epoch'`` or ``'step'`` to log ``lr`` of all optimizers at the same interval, set to ``None`` to log at individual interval according to the ``interval`` key of each scheduler. Defaults to ``None``. log_momentum: option to also log the momentum values of the optimizer, if the optimizer has the ``momentum`` or ``betas`` attribute. Defaults to ``False``. Raises: MisconfigurationException: If ``logging_interval`` is none of ``"step"``, ``"epoch"``, or ``None``. Example:: >>> from pytorch_lightning import Trainer >>> from pytorch_lightning.callbacks import LearningRateMonitor >>> lr_monitor = LearningRateMonitor(logging_interval='step') >>> trainer = Trainer(callbacks=[lr_monitor]) Logging names are automatically determined based on optimizer class name. In case of multiple optimizers of same type, they will be named ``Adam``, ``Adam-1`` etc. If a optimizer has multiple parameter groups they will be named ``Adam/pg1``, ``Adam/pg2`` etc. To control naming, pass in a ``name`` keyword in the construction of the learning rate schdulers Example:: def configure_optimizer(self): optimizer = torch.optim.Adam(...) lr_scheduler = { 'scheduler': torch.optim.lr_scheduler.LambdaLR(optimizer, ...) 'name': 'my_logging_name' } return [optimizer], [lr_scheduler] """ def __init__(self, logging_interval: Optional[str] = None, log_momentum: bool = False): if logging_interval not in (None, 'step', 'epoch'): raise MisconfigurationException('logging_interval should be `step` or `epoch` or `None`.') self.logging_interval = logging_interval self.log_momentum = log_momentum self.lrs = None self.lr_sch_names = []
[docs] def on_train_start(self, trainer, *args, **kwargs): """ Called before training, determines unique names for all lr schedulers in the case of multiple of the same type or in the case of multiple parameter groups Raises: MisconfigurationException: If ``Trainer`` has no ``logger``. """ if not trainer.logger: raise MisconfigurationException( 'Cannot use `LearningRateMonitor` callback with `Trainer` that has no logger.' ) if not trainer.lr_schedulers: rank_zero_warn( 'You are using `LearningRateMonitor` callback with models that' ' have no learning rate schedulers. Please see documentation' ' for `configure_optimizers` method.', RuntimeWarning ) if self.log_momentum: def _check_no_key(key): return any(key not in sch['scheduler'].optimizer.defaults for sch in trainer.lr_schedulers) if _check_no_key('momentum') and _check_no_key('betas'): rank_zero_warn( "You have set log_momentum=True, but some optimizers do not" " have momentum. This will log a value 0 for the momentum.", RuntimeWarning ) # Find names for schedulers names = self._find_names(trainer.lr_schedulers) # Initialize for storing values self.lrs = {name: [] for name in names} self.last_momentum_values = {name + "-momentum": None for name in names}
[docs] def on_train_batch_start(self, trainer, *args, **kwargs): if not self._should_log(trainer): return if self.logging_interval != 'epoch': interval = 'step' if self.logging_interval is None else 'any' latest_stat = self._extract_stats(trainer, interval) if latest_stat: trainer.logger.log_metrics(latest_stat, step=trainer.global_step)
[docs] def on_train_epoch_start(self, trainer, *args, **kwargs): if self.logging_interval != 'step': interval = 'epoch' if self.logging_interval is None else 'any' latest_stat = self._extract_stats(trainer, interval) if latest_stat: trainer.logger.log_metrics(latest_stat, step=trainer.global_step)
def _extract_stats(self, trainer, interval: str) -> Dict[str, float]: latest_stat = {} for name, scheduler in zip(self.lr_sch_names, trainer.lr_schedulers): if scheduler['interval'] == interval or interval == 'any': opt = scheduler['scheduler'].optimizer param_groups = opt.param_groups use_betas = 'betas' in opt.defaults for i, pg in enumerate(param_groups): suffix = f'/pg{i + 1}' if len(param_groups) > 1 else '' lr = self._extract_lr(param_group=pg, name=f'{name}{suffix}') latest_stat.update(lr) momentum = self._extract_momentum( param_group=pg, name=f'{name}-momentum{suffix}', use_betas=use_betas ) latest_stat.update(momentum) return latest_stat def _extract_lr(self, param_group, name: str) -> Dict[str, float]: lr = param_group.get('lr') self.lrs[name].append(lr) return {name: lr} def _extract_momentum(self, param_group, name: str, use_betas: bool) -> Dict[str, float]: if not self.log_momentum: return {} momentum = param_group.get('betas')[0] if use_betas else param_group.get('momentum', 0) self.last_momentum_values[name] = momentum return {name: momentum} def _find_names(self, lr_schedulers) -> List[str]: # Create uniqe names in the case we have multiple of the same learning # rate schduler + multiple parameter groups names = [] for scheduler in lr_schedulers: sch = scheduler['scheduler'] if scheduler['name'] is not None: name = scheduler['name'] else: opt_name = 'lr-' + sch.optimizer.__class__.__name__ i, name = 1, opt_name # Multiple schduler of the same type while True: if name not in names: break i, name = i + 1, f'{opt_name}-{i}' # Multiple param groups for the same schduler param_groups = sch.optimizer.param_groups if len(param_groups) != 1: for i, pg in enumerate(param_groups): temp = f'{name}/pg{i + 1}' names.append(temp) else: names.append(name) self.lr_sch_names.append(name) return names @staticmethod def _should_log(trainer) -> bool: should_log = ((trainer.global_step + 1) % trainer.log_every_n_steps == 0 or trainer.should_stop) return should_log

© Copyright Copyright (c) 2018-2021, William Falcon et al... Revision 233f252b.

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