122 lines
4.6 KiB
Python
Executable File
122 lines
4.6 KiB
Python
Executable File
r"""
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Logging of learning rates
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=========================
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Log learning rate for lr schedulers during training
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"""
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from pytorch_lightning.callbacks.base import Callback
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities import rank_zero_warn
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class LearningRateLogger(Callback):
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r"""
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Automatically logs learning rate for learning rate schedulers during training.
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Example::
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>>> from pytorch_lightning import Trainer
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>>> from pytorch_lightning.callbacks import LearningRateLogger
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>>> lr_logger = LearningRateLogger()
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>>> trainer = Trainer(callbacks=[lr_logger])
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Logging names are automatically determined based on optimizer class name.
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In case of multiple optimizers of same type, they will be named `Adam`,
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`Adam-1` etc. If a optimizer has multiple parameter groups they will
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be named `Adam/pg1`, `Adam/pg2` etc. To control naming, pass in a
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`name` keyword in the construction of the learning rate schdulers
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Example::
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def configure_optimizer(self):
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optimizer = torch.optim.Adam(...)
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lr_scheduler = {'scheduler': torch.optim.lr_schedulers.LambdaLR(optimizer, ...)
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'name': 'my_logging_name'}
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return [optimizer], [lr_scheduler]
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"""
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def __init__(self):
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self.lrs = None
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self.lr_sch_names = []
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def on_train_start(self, trainer, pl_module):
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""" Called before training, determines unique names for all lr
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schedulers in the case of multiple of the same type or in
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the case of multiple parameter groups
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"""
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if not trainer.logger:
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raise MisconfigurationException(
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'Cannot use LearningRateLogger callback with Trainer that has no logger.')
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if not trainer.lr_schedulers:
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rank_zero_warn(
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'You are using LearningRateLogger callback with models that'
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' have no learning rate schedulers. Please see documentation'
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' for `configure_optimizers` method.', RuntimeWarning
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)
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# Find names for schedulers
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names = self._find_names(trainer.lr_schedulers)
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# Initialize for storing values
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self.lrs = {name: [] for name in names}
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def on_batch_start(self, trainer, pl_module):
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latest_stat = self._extract_lr(trainer, 'step')
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if trainer.logger and latest_stat:
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trainer.logger.log_metrics(latest_stat, step=trainer.global_step)
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def on_epoch_start(self, trainer, pl_module):
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latest_stat = self._extract_lr(trainer, 'epoch')
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if trainer.logger and latest_stat:
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trainer.logger.log_metrics(latest_stat, step=trainer.global_step)
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def _extract_lr(self, trainer, interval):
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""" Extracts learning rates for lr schedulers and saves information
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into dict structure. """
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latest_stat = {}
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for name, scheduler in zip(self.lr_sch_names, trainer.lr_schedulers):
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if scheduler['interval'] == interval:
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param_groups = scheduler['scheduler'].optimizer.param_groups
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if len(param_groups) != 1:
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for i, pg in enumerate(param_groups):
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lr, key = pg['lr'], f'{name}/pg{i + 1}'
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self.lrs[key].append(lr)
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latest_stat[key] = lr
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else:
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self.lrs[name].append(param_groups[0]['lr'])
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latest_stat[name] = param_groups[0]['lr']
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return latest_stat
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def _find_names(self, lr_schedulers):
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# Create uniqe names in the case we have multiple of the same learning
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# rate schduler + multiple parameter groups
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names = []
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for scheduler in lr_schedulers:
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sch = scheduler['scheduler']
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if 'name' in scheduler:
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name = scheduler['name']
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else:
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opt_name = 'lr-' + sch.optimizer.__class__.__name__
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i, name = 1, opt_name
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# Multiple schduler of the same type
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while True:
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if name not in names:
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break
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i, name = i + 1, f'{opt_name}-{i}'
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# Multiple param groups for the same schduler
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param_groups = sch.optimizer.param_groups
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if len(param_groups) != 1:
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for i, pg in enumerate(param_groups):
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temp = f'{name}/pg{i + 1}'
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names.append(temp)
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else:
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names.append(name)
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self.lr_sch_names.append(name)
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return names
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